machine learning phd research proposal

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How to Write a Machine Learning Research Proposal

Introduction, what is a machine learning research proposal, the structure of a machine learning research proposal, tips for writing a machine learning research proposal, how to get started with writing a machine learning research proposal, the importance of a machine learning research proposal, why you should take the time to write a machine learning research proposal, how to make your machine learning research proposal stand out, the bottom line: why writing a machine learning research proposal is worth it, further resources on writing machine learning research proposals.

If you want to get into machine learning, you first need to get past the research proposal stage. We’ll show you how.

Checkout this video:

A machine learning research proposal is a document that summarizes your research project, methods, and expected outcomes. It is typically used to secure funding for your project from a sponsor or institution, and can also be used to assessment your project by peers. Your proposal should be clear, concise, and well-organized. It should also provide enough detail to allow reviewers to assess your project’s feasibility and potential impact.

In this guide, we will cover the basics of what you need to include in a machine learning research proposal. We will also provide some tips on how to create a strong proposal that is more likely to be funded.

A machine learning research proposal is a document that describes a proposed research project that uses machine learning algorithms and techniques. The proposal should include a brief overview of the problem to be tackled, the proposed solution, and the expected results. It should also briefly describe the dataset to be used, the evaluation metric, and any other relevant details.

There is no one-size-fits-all answer to this question, as the structure of a machine learning research proposal will vary depending on the specific research question you are proposing to answer, the methods you plan to use, and the overall focus of your proposal. However, there are some general principles that all good proposals should follow.

In general, a machine learning research proposal should include:

-A summary of the problem you are trying to solve and the motivation for solving it -A brief overview of previous work in this area, including any relevant background information -A description of your proposed solution and a discussion of how it compares to existing approaches -An evaluation plan outlining how you will evaluate the effectiveness of your proposed solution -A discussion of any potential risks or limitations associated with your proposed research

Useful tips for writing a machine learning research proposal:

-Your proposal should address a specific problem or question in machine learning.

-Before writing your proposal, familiarize yourself with the existing literature in the field. Your proposal should build on the existing body of knowledge and contribute to the understanding of the chosen problem or question.

-Your proposal should be clear and concise. It should be easy for non-experts to understand what you are proposing and why it is important.

-Your proposal should be well organized. Include a brief introduction, literature review, methodology, expected results, and significance of your work.

-Make sure to proofread your proposal carefully before submitting it.

A machine learning research proposal is a document that outlines the problem you want to solve with machine learning, the methods you will use to solve it, the data you will use, and the anticipated results. This guide provides an overview of what should be included in a machine learning research proposal so that you can get started on writing your own.

1. Introduction 2. Problem statement 3. Methodology 4. Data 5. Evaluation 6. References

A machine learning research proposal is a document that outlines the rationale for a proposed machine learning research project. The proposal should convince potential supervisors or funding bodies that the project is worthwhile and that the researcher is competent to undertake it.

The proposal should include:

– A clear statement of the problem to be addressed or the question to be answered – A review of relevant literature – An outline of the proposed research methodology – A discussion of the expected outcome of the research – A realistic timeline for completing the project

A machine learning research proposal is not just a formal exercise; it is an opportunity to sell your idea to potential supervisors or funding bodies. Take advantage of this opportunity by doing your best to make your proposal as clear, concise, and convincing as possible.

Your machine learning research proposal is your chance to sell your project to potential supervisors and funders. It should be clear, concise and make a strong case for why your project is worth undertaking.

A well-written proposal will convince others that you have a worthwhile project and that you have the necessary skills and experience to complete it successfully. It will also help you to clarify your own ideas and focus your research.

Writing a machine learning research proposal can seem like a daunting task, but it doesn’t have to be. If you take it one step at a time, you’ll be well on your way to writing a strong proposal that will get the support you need.

In order to make your machine learning research proposal stand out, you will need to do several things. First, make sure that your proposal is well written and free of grammatical errors. Second, make sure that your proposal is clear and concise. Third, make sure that your proposal is organized and includes all of the necessary information. Finally, be sure to proofread your proposal carefully before submitting it.

The benefits of writing a machine learning research proposal go beyond helping you get funding for your project. A good proposal will also force you to think carefully about your problem and how you plan to solve it. This process can help you identify potential flaws in your approach and make sure that your project is as strong as possible before you start.

It can also be helpful to have a machine learning research proposal on hand when you’re talking to potential collaborators or presenting your work to a wider audience. A well-written proposal can give people a clear sense of what your project is about and why it’s important, which can make it easier to get buy-in and find people who are excited to work with you.

In short, writing a machine learning research proposal is a valuable exercise that can help you hone your ideas and make sure that your project is as strong as possible before you start.

Here are some further resources on writing machine learning research proposals:

– How to Write a Machine Learning Research Paper: https://MachineLearningMastery.com/how-to-write-a-machine-learning-research-paper/

– 10 Tips for Writing a Machine Learning Research Paper: https://blog.MachineLearning.net/10-tips-for-writing-a-machine-learning-research-paper/

Please also see our other blog post on writing research proposals: https://www.MachineLearningMastery.com/how-to-write-a-research-proposal/

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PhD Programme in Advanced Machine Learning

The Cambridge Machine Learning Group (MLG) runs a PhD programme in Advanced Machine Learning. The supervisors are Jose Miguel Hernandez-Lobato , Carl Rasmussen , Richard E. Turner , Adrian Weller , Hong Ge and David Krueger . Zoubin Ghahramani is currently on academic leave and not accepting new students at this time.

We encourage applications from outstanding candidates with academic backgrounds in Mathematics, Physics, Computer Science, Engineering and related fields, and a keen interest in doing basic research in machine learning and its scientific applications. There are no additional restrictions on the topic of the PhD, but for further information on our current research areas, please consult our webpages at http://mlg.eng.cam.ac.uk .

The typical duration of the PhD will be four years.

Applicants must formally apply through the Applicant Portal at the University of Cambridge by the deadline, indicating “PhD in Engineering” as the course (supervisor Hernandez-Lobato, Rasmussen, Turner, Weller, Ge and/or Krueger). Applicants who want to apply for University funding need to reply ‘Yes’ to the question ‘Apply for Cambridge Scholarships’. See http://www.admin.cam.ac.uk/students/gradadmissions/prospec/apply/deadlines.html for details. Note that applications will not be complete until all the required material has been uploaded (including reference letters), and we will not be able to see any applications until that happens.

Gates funding applicants (US or other overseas) need to fill out the dedicated Gates Cambridge Scholarships section later on the form which is sent on to the administrators of Gates funding.

Deadline for PhD Application: noon 5 December, 2023

Applications from outstanding individuals may be considered after this time, but applying later may adversely impact your chances for both admission and funding.

FURTHER INFORMATION ABOUT COMPLETING THE ADMISSIONS FORMS:

The Machine Learning Group is based in the Department of Engineering, not Computer Science.

We will assess your application on three criteria:

1 Academic performance (ensure evidence for strong academic achievement, e.g. position in year, awards, etc.) 2 references (clearly your references will need to be strong; they should also mention evidence of excellence as quotes will be drawn from them) 3 research (detail your research experience, especially that which relates to machine learning)

You will also need to put together a research proposal. We do not offer individual support for this. It is part of the application assessment, i.e. ascertaining whether you can write about a research area in a sensible way and pose interesting questions. It is not a commitment to what you will work on during your PhD. Most often PhD topics crystallise over the first year. The research proposal should be about 2 pages long and can be attached to your application (you can indicate that your proposal is attached in the 1500 character count Research Summary box). This aspect of the application does not carry a huge amount of weight so do not spend a large amount of time on it. Please also attach a recent CV to your application too.

INFORMATION ABOUT THE CAMBRIDGE-TUEBINGEN PROGRAMME:

We also offer a small number of PhDs on the Cambridge-Tuebingen programme. This stream is for specific candidates whose research interests are well-matched to both the machine learning group in Cambridge and the MPI for Intelligent Systems in Tuebingen. For more information about the Cambridge-Tuebingen programme and how to apply see here . IMPORTANT: remember to download your application form before you submit so that you can send a copy to the administrators in Tuebingen directly . Note that the application deadline for the Cambridge-Tuebingen programme is noon, 5th December, 2023, CET.

What background do I need?

An ideal background is a top undergraduate or Masters degree in Mathematics, Physics, Computer Science, or Electrical Engineering. You should be both very strong mathematically and have an intuitive and practical grasp of computation. Successful applicants often have research experience in statistical machine learning. Shortlisted applicants are interviewed.

Do you have funding?

There are a number of funding sources at Cambridge University for PhD students, including for international students. All our students receive partial or full funding for the full three years of the PhD. We do not give preference to “self-funded” students. To be eligible for funding it is important to apply early (see https://www.graduate.study.cam.ac.uk/finance/funding – current deadlines are 10 October for US students, and 1 December for others). Also make sure you tick the box on the application saying you wish to be considered for funding!

If you are applying to the Cambridge-Tuebingen programme, note that this source of funding will not be listed as one of the official funding sources, but if you apply to this programme, please tick the other possible sources of funding if you want to maximise your chances of getting funding from Cambridge.

What is my likelihood of being admitted?

Because we receive so many applications, unfortunately we can’t admit many excellent candidates, even some who have funding. Successful applicants tend to be among the very top students at their institution, have very strong mathematics backgrounds, and references, and have some research experience in statistical machine learning.

Do I have to contact one of the faculty members first or can I apply formally directly?

It is not necessary, but if you have doubts about whether your background is suitable for the programme, or if you have questions about the group, you are welcome to contact one of the faculty members directly. Due to their high email volume you may not receive an immediate response but they will endeavour to get back to you as quickly as possible. It is important to make your official application to Graduate Admissions at Cambridge before the funding deadlines, even if you don’t hear back from us; otherwise we may not be able to consider you.

Do you take Masters students, or part-time PhD students?

We generally don’t admit students for a part-time PhD. We also don’t usually admit students just for a pure-research Masters in machine learning , except for specific programs such as the Churchill and Marshall scholarships. However, please do note that we run a one-year taught Master’s Programme: The MPhil in Machine Learning, and Machine Intelligence . You are welcome to apply directly to this.

What Department / course should I indicate on my application form?

This machine learning group is in the Department of Engineering. The degree you would be applying for is a PhD in Engineering (not Computer Science or Statistics).

How long does a PhD take?

A typical PhD from our group takes 3-4 years. The first year requires students to pass some courses and submit a first-year research report. Students must submit their PhD before the 4th year.

What research topics do you have projects on?

We don’t generally pre-specify projects for students. We prefer to find a research area that suits the student. For a sample of our research, you can check group members’ personal pages or our research publications page.

What are the career prospects for PhD students from your group?

Students and postdocs from the group have moved on to excellent positions both in academia and industry. Have a look at our list of recent alumni on the Machine Learning group webpage . Research expertise in machine learning is in very high demand these days.

Machine Learning - CMU

PhD Dissertations

[all are .pdf files].

Learning Models that Match Jacob Tyo, 2024

Improving Human Integration across the Machine Learning Pipeline Charvi Rastogi, 2024

Reliable and Practical Machine Learning for Dynamic Healthcare Settings Helen Zhou, 2023

Automatic customization of large-scale spiking network models to neuronal population activity (unavailable) Shenghao Wu, 2023

Estimation of BVk functions from scattered data (unavailable) Addison J. Hu, 2023

Rethinking object categorization in computer vision (unavailable) Jayanth Koushik, 2023

Advances in Statistical Gene Networks Jinjin Tian, 2023 Post-hoc calibration without distributional assumptions Chirag Gupta, 2023

The Role of Noise, Proxies, and Dynamics in Algorithmic Fairness Nil-Jana Akpinar, 2023

Collaborative learning by leveraging siloed data Sebastian Caldas, 2023

Modeling Epidemiological Time Series Aaron Rumack, 2023

Human-Centered Machine Learning: A Statistical and Algorithmic Perspective Leqi Liu, 2023

Uncertainty Quantification under Distribution Shifts Aleksandr Podkopaev, 2023

Probabilistic Reinforcement Learning: Using Data to Define Desired Outcomes, and Inferring How to Get There Benjamin Eysenbach, 2023

Comparing Forecasters and Abstaining Classifiers Yo Joong Choe, 2023

Using Task Driven Methods to Uncover Representations of Human Vision and Semantics Aria Yuan Wang, 2023

Data-driven Decisions - An Anomaly Detection Perspective Shubhranshu Shekhar, 2023

Applied Mathematics of the Future Kin G. Olivares, 2023

METHODS AND APPLICATIONS OF EXPLAINABLE MACHINE LEARNING Joon Sik Kim, 2023

NEURAL REASONING FOR QUESTION ANSWERING Haitian Sun, 2023

Principled Machine Learning for Societally Consequential Decision Making Amanda Coston, 2023

Long term brain dynamics extend cognitive neuroscience to timescales relevant for health and physiology Maxwell B. Wang, 2023

Long term brain dynamics extend cognitive neuroscience to timescales relevant for health and physiology Darby M. Losey, 2023

Calibrated Conditional Density Models and Predictive Inference via Local Diagnostics David Zhao, 2023

Towards an Application-based Pipeline for Explainability Gregory Plumb, 2022

Objective Criteria for Explainable Machine Learning Chih-Kuan Yeh, 2022

Making Scientific Peer Review Scientific Ivan Stelmakh, 2022

Facets of regularization in high-dimensional learning: Cross-validation, risk monotonization, and model complexity Pratik Patil, 2022

Active Robot Perception using Programmable Light Curtains Siddharth Ancha, 2022

Strategies for Black-Box and Multi-Objective Optimization Biswajit Paria, 2022

Unifying State and Policy-Level Explanations for Reinforcement Learning Nicholay Topin, 2022

Sensor Fusion Frameworks for Nowcasting Maria Jahja, 2022

Equilibrium Approaches to Modern Deep Learning Shaojie Bai, 2022

Towards General Natural Language Understanding with Probabilistic Worldbuilding Abulhair Saparov, 2022

Applications of Point Process Modeling to Spiking Neurons (Unavailable) Yu Chen, 2021

Neural variability: structure, sources, control, and data augmentation Akash Umakantha, 2021

Structure and time course of neural population activity during learning Jay Hennig, 2021

Cross-view Learning with Limited Supervision Yao-Hung Hubert Tsai, 2021

Meta Reinforcement Learning through Memory Emilio Parisotto, 2021

Learning Embodied Agents with Scalably-Supervised Reinforcement Learning Lisa Lee, 2021

Learning to Predict and Make Decisions under Distribution Shift Yifan Wu, 2021

Statistical Game Theory Arun Sai Suggala, 2021

Towards Knowledge-capable AI: Agents that See, Speak, Act and Know Kenneth Marino, 2021

Learning and Reasoning with Fast Semidefinite Programming and Mixing Methods Po-Wei Wang, 2021

Bridging Language in Machines with Language in the Brain Mariya Toneva, 2021

Curriculum Learning Otilia Stretcu, 2021

Principles of Learning in Multitask Settings: A Probabilistic Perspective Maruan Al-Shedivat, 2021

Towards Robust and Resilient Machine Learning Adarsh Prasad, 2021

Towards Training AI Agents with All Types of Experiences: A Unified ML Formalism Zhiting Hu, 2021

Building Intelligent Autonomous Navigation Agents Devendra Chaplot, 2021

Learning to See by Moving: Self-supervising 3D Scene Representations for Perception, Control, and Visual Reasoning Hsiao-Yu Fish Tung, 2021

Statistical Astrophysics: From Extrasolar Planets to the Large-scale Structure of the Universe Collin Politsch, 2020

Causal Inference with Complex Data Structures and Non-Standard Effects Kwhangho Kim, 2020

Networks, Point Processes, and Networks of Point Processes Neil Spencer, 2020

Dissecting neural variability using population recordings, network models, and neurofeedback (Unavailable) Ryan Williamson, 2020

Predicting Health and Safety: Essays in Machine Learning for Decision Support in the Public Sector Dylan Fitzpatrick, 2020

Towards a Unified Framework for Learning and Reasoning Han Zhao, 2020

Learning DAGs with Continuous Optimization Xun Zheng, 2020

Machine Learning and Multiagent Preferences Ritesh Noothigattu, 2020

Learning and Decision Making from Diverse Forms of Information Yichong Xu, 2020

Towards Data-Efficient Machine Learning Qizhe Xie, 2020

Change modeling for understanding our world and the counterfactual one(s) William Herlands, 2020

Machine Learning in High-Stakes Settings: Risks and Opportunities Maria De-Arteaga, 2020

Data Decomposition for Constrained Visual Learning Calvin Murdock, 2020

Structured Sparse Regression Methods for Learning from High-Dimensional Genomic Data Micol Marchetti-Bowick, 2020

Towards Efficient Automated Machine Learning Liam Li, 2020

LEARNING COLLECTIONS OF FUNCTIONS Emmanouil Antonios Platanios, 2020

Provable, structured, and efficient methods for robustness of deep networks to adversarial examples Eric Wong , 2020

Reconstructing and Mining Signals: Algorithms and Applications Hyun Ah Song, 2020

Probabilistic Single Cell Lineage Tracing Chieh Lin, 2020

Graphical network modeling of phase coupling in brain activity (unavailable) Josue Orellana, 2019

Strategic Exploration in Reinforcement Learning - New Algorithms and Learning Guarantees Christoph Dann, 2019 Learning Generative Models using Transformations Chun-Liang Li, 2019

Estimating Probability Distributions and their Properties Shashank Singh, 2019

Post-Inference Methods for Scalable Probabilistic Modeling and Sequential Decision Making Willie Neiswanger, 2019

Accelerating Text-as-Data Research in Computational Social Science Dallas Card, 2019

Multi-view Relationships for Analytics and Inference Eric Lei, 2019

Information flow in networks based on nonstationary multivariate neural recordings Natalie Klein, 2019

Competitive Analysis for Machine Learning & Data Science Michael Spece, 2019

The When, Where and Why of Human Memory Retrieval Qiong Zhang, 2019

Towards Effective and Efficient Learning at Scale Adams Wei Yu, 2019

Towards Literate Artificial Intelligence Mrinmaya Sachan, 2019

Learning Gene Networks Underlying Clinical Phenotypes Under SNP Perturbations From Genome-Wide Data Calvin McCarter, 2019

Unified Models for Dynamical Systems Carlton Downey, 2019

Anytime Prediction and Learning for the Balance between Computation and Accuracy Hanzhang Hu, 2019

Statistical and Computational Properties of Some "User-Friendly" Methods for High-Dimensional Estimation Alnur Ali, 2019

Nonparametric Methods with Total Variation Type Regularization Veeranjaneyulu Sadhanala, 2019

New Advances in Sparse Learning, Deep Networks, and Adversarial Learning: Theory and Applications Hongyang Zhang, 2019

Gradient Descent for Non-convex Problems in Modern Machine Learning Simon Shaolei Du, 2019

Selective Data Acquisition in Learning and Decision Making Problems Yining Wang, 2019

Anomaly Detection in Graphs and Time Series: Algorithms and Applications Bryan Hooi, 2019

Neural dynamics and interactions in the human ventral visual pathway Yuanning Li, 2018

Tuning Hyperparameters without Grad Students: Scaling up Bandit Optimisation Kirthevasan Kandasamy, 2018

Teaching Machines to Classify from Natural Language Interactions Shashank Srivastava, 2018

Statistical Inference for Geometric Data Jisu Kim, 2018

Representation Learning @ Scale Manzil Zaheer, 2018

Diversity-promoting and Large-scale Machine Learning for Healthcare Pengtao Xie, 2018

Distribution and Histogram (DIsH) Learning Junier Oliva, 2018

Stress Detection for Keystroke Dynamics Shing-Hon Lau, 2018

Sublinear-Time Learning and Inference for High-Dimensional Models Enxu Yan, 2018

Neural population activity in the visual cortex: Statistical methods and application Benjamin Cowley, 2018

Efficient Methods for Prediction and Control in Partially Observable Environments Ahmed Hefny, 2018

Learning with Staleness Wei Dai, 2018

Statistical Approach for Functionally Validating Transcription Factor Bindings Using Population SNP and Gene Expression Data Jing Xiang, 2017

New Paradigms and Optimality Guarantees in Statistical Learning and Estimation Yu-Xiang Wang, 2017

Dynamic Question Ordering: Obtaining Useful Information While Reducing User Burden Kirstin Early, 2017

New Optimization Methods for Modern Machine Learning Sashank J. Reddi, 2017

Active Search with Complex Actions and Rewards Yifei Ma, 2017

Why Machine Learning Works George D. Montañez , 2017

Source-Space Analyses in MEG/EEG and Applications to Explore Spatio-temporal Neural Dynamics in Human Vision Ying Yang , 2017

Computational Tools for Identification and Analysis of Neuronal Population Activity Pengcheng Zhou, 2016

Expressive Collaborative Music Performance via Machine Learning Gus (Guangyu) Xia, 2016

Supervision Beyond Manual Annotations for Learning Visual Representations Carl Doersch, 2016

Exploring Weakly Labeled Data Across the Noise-Bias Spectrum Robert W. H. Fisher, 2016

Optimizing Optimization: Scalable Convex Programming with Proximal Operators Matt Wytock, 2016

Combining Neural Population Recordings: Theory and Application William Bishop, 2015

Discovering Compact and Informative Structures through Data Partitioning Madalina Fiterau-Brostean, 2015

Machine Learning in Space and Time Seth R. Flaxman, 2015

The Time and Location of Natural Reading Processes in the Brain Leila Wehbe, 2015

Shape-Constrained Estimation in High Dimensions Min Xu, 2015

Spectral Probabilistic Modeling and Applications to Natural Language Processing Ankur Parikh, 2015 Computational and Statistical Advances in Testing and Learning Aaditya Kumar Ramdas, 2015

Corpora and Cognition: The Semantic Composition of Adjectives and Nouns in the Human Brain Alona Fyshe, 2015

Learning Statistical Features of Scene Images Wooyoung Lee, 2014

Towards Scalable Analysis of Images and Videos Bin Zhao, 2014

Statistical Text Analysis for Social Science Brendan T. O'Connor, 2014

Modeling Large Social Networks in Context Qirong Ho, 2014

Semi-Cooperative Learning in Smart Grid Agents Prashant P. Reddy, 2013

On Learning from Collective Data Liang Xiong, 2013

Exploiting Non-sequence Data in Dynamic Model Learning Tzu-Kuo Huang, 2013

Mathematical Theories of Interaction with Oracles Liu Yang, 2013

Short-Sighted Probabilistic Planning Felipe W. Trevizan, 2013

Statistical Models and Algorithms for Studying Hand and Finger Kinematics and their Neural Mechanisms Lucia Castellanos, 2013

Approximation Algorithms and New Models for Clustering and Learning Pranjal Awasthi, 2013

Uncovering Structure in High-Dimensions: Networks and Multi-task Learning Problems Mladen Kolar, 2013

Learning with Sparsity: Structures, Optimization and Applications Xi Chen, 2013

GraphLab: A Distributed Abstraction for Large Scale Machine Learning Yucheng Low, 2013

Graph Structured Normal Means Inference James Sharpnack, 2013 (Joint Statistics & ML PhD)

Probabilistic Models for Collecting, Analyzing, and Modeling Expression Data Hai-Son Phuoc Le, 2013

Learning Large-Scale Conditional Random Fields Joseph K. Bradley, 2013

New Statistical Applications for Differential Privacy Rob Hall, 2013 (Joint Statistics & ML PhD)

Parallel and Distributed Systems for Probabilistic Reasoning Joseph Gonzalez, 2012

Spectral Approaches to Learning Predictive Representations Byron Boots, 2012

Attribute Learning using Joint Human and Machine Computation Edith L. M. Law, 2012

Statistical Methods for Studying Genetic Variation in Populations Suyash Shringarpure, 2012

Data Mining Meets HCI: Making Sense of Large Graphs Duen Horng (Polo) Chau, 2012

Learning with Limited Supervision by Input and Output Coding Yi Zhang, 2012

Target Sequence Clustering Benjamin Shih, 2011

Nonparametric Learning in High Dimensions Han Liu, 2010 (Joint Statistics & ML PhD)

Structural Analysis of Large Networks: Observations and Applications Mary McGlohon, 2010

Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy Brian D. Ziebart, 2010

Tractable Algorithms for Proximity Search on Large Graphs Purnamrita Sarkar, 2010

Rare Category Analysis Jingrui He, 2010

Coupled Semi-Supervised Learning Andrew Carlson, 2010

Fast Algorithms for Querying and Mining Large Graphs Hanghang Tong, 2009

Efficient Matrix Models for Relational Learning Ajit Paul Singh, 2009

Exploiting Domain and Task Regularities for Robust Named Entity Recognition Andrew O. Arnold, 2009

Theoretical Foundations of Active Learning Steve Hanneke, 2009

Generalized Learning Factors Analysis: Improving Cognitive Models with Machine Learning Hao Cen, 2009

Detecting Patterns of Anomalies Kaustav Das, 2009

Dynamics of Large Networks Jurij Leskovec, 2008

Computational Methods for Analyzing and Modeling Gene Regulation Dynamics Jason Ernst, 2008

Stacked Graphical Learning Zhenzhen Kou, 2007

Actively Learning Specific Function Properties with Applications to Statistical Inference Brent Bryan, 2007

Approximate Inference, Structure Learning and Feature Estimation in Markov Random Fields Pradeep Ravikumar, 2007

Scalable Graphical Models for Social Networks Anna Goldenberg, 2007

Measure Concentration of Strongly Mixing Processes with Applications Leonid Kontorovich, 2007

Tools for Graph Mining Deepayan Chakrabarti, 2005

Automatic Discovery of Latent Variable Models Ricardo Silva, 2005

Doctor of Philosophy with a major in Machine Learning

The Doctor of Philosophy with a major in Machine Learning program has the following principal objectives, each of which supports an aspect of the Institute’s mission:

• Create students that are able to advance the state of knowledge and practice in machine learning through innovative research contributions.
• Create students who are able to integrate and apply principles from computing, statistics, optimization, engineering, mathematics and science to innovate, and create machine learning models and apply them to solve important real-world data intensive problems.
• Create students who are able to participate in multidisciplinary teams that include individuals whose primary background is in statistics, optimization, engineering, mathematics and science.
• Provide a high quality education that prepares individuals for careers in industry, government (e.g., national laboratories), and academia, both in terms of knowledge, computational (e.g., software development) skills, and mathematical modeling skills.
• Foster multidisciplinary collaboration among researchers and educators in areas such as computer science, statistics, optimization, engineering, social science, and computational biology.
• Foster economic development in the state of Georgia.
• Advance Georgia Tech’s position of academic leadership by attracting high quality students who would not otherwise apply to Tech for graduate study.

All PhD programs must incorporate a standard set of Requirements for the Doctoral Degree .

The central goal of the PhD program is to train students to perform original, independent research.  The most important part of the curriculum is the successful defense of a PhD Dissertation, which demonstrates this research ability.  The academic requirements are designed in service of this goal.

The curriculum for the PhD in Machine Learning is truly multidisciplinary, containing courses taught in nine schools across three colleges at Georgia Tech: the Schools of Computational Science and Engineering, Computer Science, and Interactive Computing in the College of Computing; the Schools of Aerospace Engineering, Chemical and Biomolecular Engineering, Industrial and Systems Engineering, Electrical and Computer Engineering, and Biomedical Engineering in the College of Engineering; and the School of Mathematics in the College of Science.

Summary of General Requirements for a PhD in Machine Learning

• Core curriculum (4 courses, 12 hours). Machine Learning PhD students will be required to complete courses in four different areas: Mathematical Foundations, Probabilistic and Statistical Methods in Machine Learning, ML Theory and Methods, and Optimization.   
• Area electives (5 courses, 15 hours).
• Responsible Conduct of Research (RCR) (1 course, 1 hour, pass/fail).  Georgia Tech requires that all PhD students complete an RCR requirement that consists of an online component and in-person training. The online component is completed during the student’s first semester enrolled at Georgia Tech.  The in-person training is satisfied by taking PHIL 6000 or their associated academic program’s in-house RCR course.
• Qualifying examination (1 course, 3 hours). This consists of a one-semester independent literature review followed by an oral examination.
• Doctoral minor (2 courses, 6 hours).
• Research Proposal.  The purpose of the proposal is to give the faculty an opportunity to give feedback on the student’s research direction, and to make sure they are developing into able communicators.
• PhD Dissertation.

Almost all of the courses in both the core and elective categories are already taught regularly at Georgia Tech.  However, two core courses (designated in the next section) are being developed specifically for this program.  The proposed outlines for these courses can be found in the Appendix. Students who complete these required courses as part of a master’s program will not need to repeat the courses if they are admitted to the ML PhD program.

Core Courses

Machine Learning PhD students will be required to complete courses in four different areas. With the exception of the Foundations course, each of these area requirements can be satisfied using existing courses from the College of Computing or Schools of ECE, ISyE, and Mathematics.

Machine Learning core:

Mathematical Foundations of Machine Learning. This required course is the gateway into the program, and covers the key subjects from applied mathematics needed for a rigorous graduate program in ML. Particular emphasis will be put on advanced concepts in linear algebra and probabilistic modeling. This course is cross-listed between CS, CSE, ECE, and ISyE.

ECE 7750 / ISYE 7750 / CS 7750 / CSE 7750 Mathematical Foundations of Machine Learning

Probabilistic and Statistical Methods in Machine Learning

• ISYE 6412 , Theoretical Statistics
• ECE 7751 / ISYE 7751 / CS 7751 / CSE 7751 Probabilistic Graphical Models
• MATH 7251 High Dimension Probability
• MATH 7252 High Dimension Statistics

Machine Learning: Theory and Methods.   This course serves as an introduction to the foundational problems, algorithms, and modeling techniques in machine learning.  Each of the courses listed below treats roughly the same material using a mix of applied mathematics and computer science, and each has a different balance between the two. 

• CS 7545 Machine Learning Theory and Methods
• CS 7616 , Pattern Recognition
• CSE 6740 / ISYE 6740 , Computational Data Analysis
• ECE 6254 , Statistical Machine Learning
• ECE 6273 , Methods of Pattern Recognition with Applications to Voice

Optimization.   Optimization plays a crucial role in both developing new machine learning algorithms and analyzing their performance.  The three courses below all provide a rigorous introduction to this topic; each emphasizes different material and provides a unique balance of mathematics and algorithms.

• ECE 8823 , Convex Optimization: Theory, Algorithms, and Applications
• ISYE 6661 , Linear Optimization
• ISYE 6663 , Nonlinear Optimization
• ISYE 7683 , Advanced Nonlinear Programming

After core requirements are satisfied, all courses listed in the core not already taken can be used as (appropriately classified) electives.

In addition to meeting the core area requirements, each student is required to complete five elective courses. These courses are required for getting a complete breadth in ML. These courses must be chosen from at least two of the five subject areas listed below. In addition, students can use up to six special problems research hours to satisfy this requirement. 

i. Statistics and Applied Probability : To build breadth and depth in the areas of statistics and probability as applied to ML.

• AE 6505 , Kalman Filtering
• BMED 6700 , Biostatistics
• ECE 6558 , Stochastic Systems
• ECE 6601 , Random Processes
• ECE 6605 , Information Theory
• ISYE 6404 , Nonparametric Data Analysis
• ISYE 6413 , Design and Analysis of Experiments
• ISYE 6414 , Regression Analysis
• ISYE 6416 , Computational Statistics
• ISYE 6420 , Bayesian Statistics
• ISYE 6761 , Stochastic Processes I
• ISYE 6762 , Stochastic Processes II
• ISYE 7400 , Adv Design-Experiments
• ISYE 7401 , Adv Statistical Modeling
• ISYE 7405 , Multivariate Data Analysis
• ISYE 8803 , Statistical and Probabilistic Methods for Data Science
• ISYE 8813 , Special Topics in Data Science
• MATH 6263 , Testing Statistical Hypotheses
• MATH 6266 , Statistical Linear Modeling
• MATH 6267 , Multivariate Statistical Analysis
• MATH 7244 , Stochastic Processes and Stochastic Calculus I
• MATH 7245 , Stochastic Processes and Stochastic Calculus II

ii. Advanced Theory: To build a deeper understanding of foundations of ML.

• CS 7280 , Network Science
• CS 7510 , Graph Algorithms
• CS 7520 , Approximation Algorithms
• CS 7530 , Randomized Algorithms
• CS 7535 , Markov Chain Monte Carlo Algorithms
• CS 7540 , Spectral Algorithms
• CS 8803 , Continuous Algorithms
• ECE 6283 , Harmonic Analysis and Signal Processing
• ECE 6555 , Linear Estimation
• ISYE 7682 , Convexity
• MATH 6112 , Advanced Linear Algebra
• MATH 6221 , Advanced Classical Probability Theory
• MATH 6241 , Probability I
• MATH 6580 , Introduction to Hilbert Space
• MATH 7338 , Functional Analysis
• MATH 7586 , Tensor Analysis
• MATH 88XX, Special Topics: High Dimensional Probability and Statistics

iii. Applications: To develop a breadth and depth in variety of applications domains impacted by/with ML.

• AE 6373 , Advanced Design Methods
• AE 8803 , Machine Learning for Control Systems
• AE 8803 , Nonlinear Stochastic Optimal Control
• BMED 6780 , Medical Image Processing
• BMED 8813 BHI, Biomedical and Health Informatics
• BMED 8813 MHI, mHealth Informatics
• BMED 8813 MLB, Machine Learning in Biomedicine
• BMED 8823 ALG, OMICS Data and Bioinformatics Algorithms
• CS 6440 , Introduction to Health Informatics
• CS 6465 , Computational Journalism
• CS 6471 , Computational Social Science
• CS 6474 , Social Computing
• CS 6475 , Computational Photography
• CS 6476 , Computer Vision
• CS 6601 , Artificial Intelligence
• CS 7450 , Information Visualization
• CS 7476 , Advanced Computer Vision
• CS 7630 , Autonomous Robots
• CS 7632 , Game AI
• CS 7636 , Computational Perception
• CS 7643 , Deep Learning
• CS 7646 , Machine Learning for Trading
• CS 7650 , Natural Language Processing
• CSE 6141 , Massive Graph Analysis
• CSE 6240 , Web Search and Text Mining
• CSE 6242 , Data and Visual Analytics
• CSE 6301 , Algorithms in Bioinformatics and Computational Biology
• ECE 4580 , Computational Computer Vision
• ECE 6255 , Digital Processing of Speech Signals
• ECE 6258 , Digital Image Processing
• ECE 6260 , Data Compression and Modeling
• ECE 6273 , Methods of Pattern Recognition with Application to Voice
• ECE 6550 , Linear Systems and Controls
• ECE 8813 , Network Security
• ISYE 6421 , Biostatistics
• ISYE 6810 , Systems Monitoring and Prognosis
• ISYE 7201 , Production Systems
• ISYE 7204 , Info Prod & Ser Sys
• ISYE 7203 , Logistics Systems
• ISYE 8813 , Supply Chain Inventory Theory
• HS 6000 , Healthcare Delivery
• MATH 6759 , Stochastic Processes in Finance
• MATH 6783 , Financial Data Analysis

iv. Computing and Optimization: To provide more breadth and foundation in areas of math, optimization and computation for ML.

• CS 6515 , Introduction to Graduate Algorithms
• CS 6550 , Design and Analysis of Algorithms
• CSE 6140 , Computational Science and Engineering Algorithms
• CSE 6643 , Numerical Linear Algebra
• CSE 6644 , Iterative Methods for Systems of Equations
• CSE 6710 , Numerical Methods I
• CSE 6711 , Numerical Methods II
• ISYE 6644 , Simulation
• ISYE 6645 , Monte Carlo Methods
• ISYE 6662 , Discrete Optimization
• ISYE 6664 , Stochastic Optimization
• ISYE 6679 , Computational methods for optimization
• ISYE 7686 , Advanced Combinatorial Optimization
• ISYE 7687 , Advanced Integer Programming

v. Platforms : To provide breadth and depth in computing platforms that support ML and Computation.

• CS 6421 , Temporal, Spatial, and Active Databases
• CS 6430 , Parallel and Distributed Databases
• CS 6290 , High-Performance Computer Architecture
• CSE 6220 , High Performance Computing
• CSE 6230 , High Performance Parallel Computing

Qualifying Examination

The purpose of the Qualifying Examination is to judge the candidate’s potential as an independent researcher.

The Ph.D. qualifying exam consists of a focused literature review that will take place over the course of one semester.  At the beginning of the second semester of their second year, a qualifying committee consisting of three members of the ML faculty will assign, in consultation with the student and the student’s advisor, a course of study consisting of influential papers, books, or other intellectual artifacts relevant to the student’s research interests.  The student’s focus area and current research efforts (and related portfolio) will be considered in defining the course of study.

At the end of the semester, the student will submit a written summary of each artifact which highlights their understanding of the importance (and weaknesses) of the work in question and the relationship of this work to their current research.  Subsequently, the student will have a closed oral exam with the three members of the committee.  The exam will be interactive, with the student and the committee discussing and criticizing each work and posing questions related the students current research to determine the breadth of student’s knowledge in that specific area.  

The success of the examination will be determined by the committee’s qualitative assessment of the student’s understanding of the theory, methods, and ultimate impact of the assigned syllabus.

The student will be given a passing grade for meeting the requirements of the committee in both the written and the oral part. Unsatisfactory performance on either part will require the student to redo the entire qualifying exam in the following semester year. Each student will be allowed only two attempts at the exam.

Students are expected to perform the review by the end of their second year in the program.

Doctoral Dissertation

The primary requirement of the PhD student is to do original and substantial research.  This research is reported for review in the PhD dissertation, and presented at the final defense.  As the first step towards completing a dissertation, the student must prepare and defend a Research Proposal.  The proposal is a document of no more than 20 pages in length that carefully describes the topic of the dissertation, including references to prior work, and any preliminary results to date.  The written proposal is submitted to a committee of three faculty members from the ML PhD program, and is presented in a public seminar shortly thereafter.  The committee members provide feedback on the proposed research directions, comments on the strength of writing and oral presentation skills, and might suggest further courses to solidify the student’s background.  Approval of the Research Proposal by the committee is required at least six months prior to the scheduling of the PhD defense. It is expected that the student complete this proposal requirement no later than their fourth year in the program. The PhD thesis committee consists of five faculty members: the student’s advisor, three additional members from the ML PhD program, and one faculty member external to the ML program.  The committee is charged with approving the written dissertation and administering the final defense.  The defense consists of a public seminar followed by oral examination from the thesis committee.

Doctoral minor (2 courses, 6 hours): 

The minor follows the standard Georgia Tech requirement: 6 hours, preferably outside the student’s home unit, with a GPA in those graduate-level courses of at least 3.0.  The courses for the minor should form a cohesive program of study outside the area of Machine Learning; no ML core or elective courses may be used to fulfill this requirement and must be approved by your thesis advisor and ML Academic Advisor.  Typical programs will consist of three courses two courses from the same school (any school at the Institute) or two courses from the same area of study. 

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17 Compelling Machine Learning Ph.D. Dissertations

Machine Learning Modeling Research posted by Daniel Gutierrez, ODSC August 12, 2021 Daniel Gutierrez, ODSC

Working in the field of data science, I’m always seeking ways to keep current in the field and there are a number of important resources available for this purpose: new book titles, blog articles, conference sessions, Meetups, webinars/podcasts, not to mention the gems floating around in social media. But to dig even deeper, I routinely look at what’s coming out of the world’s research labs. And one great way to keep a pulse for what the research community is working on is to monitor the flow of new machine learning Ph.D. dissertations. Admittedly, many such theses are laser-focused and narrow, but from previous experience reading these documents, you can learn an awful lot about new ways to solve difficult problems over a vast range of problem domains. 

In this article, I present a number of hand-picked machine learning dissertations that I found compelling in terms of my own areas of interest and aligned with problems that I’m working on. I hope you’ll find a number of them that match your own interests. Each dissertation may be challenging to consume but the process will result in hours of satisfying summer reading. Enjoy!

Please check out my previous data science dissertation round-up article . 

1. Fitting Convex Sets to Data: Algorithms and Applications

This machine learning dissertation concerns the geometric problem of finding a convex set that best fits a given data set. The overarching question serves as an abstraction for data-analytical tasks arising in a range of scientific and engineering applications with a focus on two specific instances: (i) a key challenge that arises in solving inverse problems is ill-posedness due to a lack of measurements. A prominent family of methods for addressing such issues is based on augmenting optimization-based approaches with a convex penalty function so as to induce a desired structure in the solution. These functions are typically chosen using prior knowledge about the data. The thesis also studies the problem of learning convex penalty functions directly from data for settings in which we lack the domain expertise to choose a penalty function. The solution relies on suitably transforming the problem of learning a penalty function into a fitting task; and (ii) the problem of fitting tractably-described convex sets given the optimal value of linear functionals evaluated in different directions.

2. Structured Tensors and the Geometry of Data

This machine learning dissertation analyzes data to build a quantitative understanding of the world. Linear algebra is the foundation of algorithms, dating back one hundred years, for extracting structure from data. Modern technologies provide an abundance of multi-dimensional data, in which multiple variables or factors can be compared simultaneously. To organize and analyze such data sets we can use a tensor , the higher-order analogue of a matrix. However, many theoretical and practical challenges arise in extending linear algebra to the setting of tensors. The first part of the thesis studies and develops the algebraic theory of tensors. The second part of the thesis presents three algorithms for tensor data. The algorithms use algebraic and geometric structure to give guarantees of optimality.

3. Statistical approaches for spatial prediction and anomaly detection

This machine learning dissertation is primarily a description of three projects. It starts with a method for spatial prediction and parameter estimation for irregularly spaced, and non-Gaussian data. It is shown that by judiciously replacing the likelihood with an empirical likelihood in the Bayesian hierarchical model, approximate posterior distributions for the mean and covariance parameters can be obtained. Due to the complex nature of the hierarchical model, standard Markov chain Monte Carlo methods cannot be applied to sample from the posterior distributions. To overcome this issue, a generalized sequential Monte Carlo algorithm is used. Finally, this method is applied to iron concentrations in California. The second project focuses on anomaly detection for functional data; specifically for functional data where the observed functions may lie over different domains. By approximating each function as a low-rank sum of spline basis functions the coefficients will be compared for each basis across each function. The idea being, if two functions are similar then their respective coefficients should not be significantly different. This project concludes with an application of the proposed method to detect anomalous behavior of users of a supercomputer at NREL. The final project is an extension of the second project to two-dimensional data. This project aims to detect location and temporal anomalies from ground motion data from a fiber-optic cable using distributed acoustic sensing (DAS). 

4. Sampling for Streaming Data

Advances in data acquisition technology pose challenges in analyzing large volumes of streaming data. Sampling is a natural yet powerful tool for analyzing such data sets due to their competent estimation accuracy and low computational cost. Unfortunately, sampling methods and their statistical properties for streaming data, especially streaming time series data, are not well studied in the literature. Meanwhile, estimating the dependence structure of multidimensional streaming time-series data in real-time is challenging. With large volumes of streaming data, the problem becomes more difficult when the multidimensional data are collected asynchronously across distributed nodes, which motivates us to sample representative data points from streams. This machine learning dissertation proposes a series of leverage score-based sampling methods for streaming time series data. The simulation studies and real data analysis are conducted to validate the proposed methods. The theoretical analysis of the asymptotic behaviors of the least-squares estimator is developed based on the subsamples.

5.  Statistical Machine Learning Methods for Complex, Heterogeneous Data

This machine learning dissertation develops statistical machine learning methodology for three distinct tasks. Each method blends classical statistical approaches with machine learning methods to provide principled solutions to problems with complex, heterogeneous data sets. The first framework proposes two methods for high-dimensional shape-constrained regression and classification. These methods reshape pre-trained prediction rules to satisfy shape constraints like monotonicity and convexity. The second method provides a nonparametric approach to the econometric analysis of discrete choice. This method provides a scalable algorithm for estimating utility functions with random forests, and combines this with random effects to properly model preference heterogeneity. The final method draws inspiration from early work in statistical machine translation to construct embeddings for variable-length objects like mathematical equations

6. Topics in Multivariate Statistics with Dependent Data

This machine learning dissertation comprises four chapters. The first is an introduction to the topics of the dissertation and the remaining chapters contain the main results. Chapter 2 gives new results for consistency of maximum likelihood estimators with a focus on multivariate mixed models. The presented theory builds on the idea of using subsets of the full data to establish consistency of estimators based on the full data. The theory is applied to two multivariate mixed models for which it was unknown whether maximum likelihood estimators are consistent. In Chapter 3 an algorithm is proposed for maximum likelihood estimation of a covariance matrix when the corresponding correlation matrix can be written as the Kronecker product of two lower-dimensional correlation matrices. The proposed method is fully likelihood-based. Some desirable properties of separable correlation in comparison to separable covariance are also discussed. Chapter 4 is concerned with Bayesian vector auto-regressions (VARs). A collapsed Gibbs sampler is proposed for Bayesian VARs with predictors and the convergence properties of the algorithm are studied. 

7.  Model Selection and Estimation for High-dimensional Data Analysis

In the era of big data, uncovering useful information and hidden patterns in the data is prevalent in different fields. However, it is challenging to effectively select input variables in data and estimate their effects. The goal of this machine learning dissertation is to develop reproducible statistical approaches that provide mechanistic explanations of the phenomenon observed in big data analysis. The research contains two parts: variable selection and model estimation. The first part investigates how to measure and interpret the usefulness of an input variable using an approach called “variable importance learning” and builds tools (methodology and software) that can be widely applied. Two variable importance measures are proposed, a parametric measure SOIL and a non-parametric measure CVIL, using the idea of a model combining and cross-validation respectively. The SOIL method is theoretically shown to have the inclusion/exclusion property: When the model weights are properly around the true model, the SOIL importance can well separate the variables in the true model from the rest. The CVIL method possesses desirable theoretical properties and enhances the interpretability of many mysterious but effective machine learning methods. The second part focuses on how to estimate the effect of a useful input variable in the case where the interaction of two input variables exists. Investigated is the minimax rate of convergence for regression estimation in high-dimensional sparse linear models with two-way interactions, and construct an adaptive estimator that achieves the minimax rate of convergence regardless of the true heredity condition and the sparsity indices.

8.  High-Dimensional Structured Regression Using Convex Optimization

While the term “Big Data” can have multiple meanings, this dissertation considers the type of data in which the number of features can be much greater than the number of observations (also known as high-dimensional data). High-dimensional data is abundant in contemporary scientific research due to the rapid advances in new data-measurement technologies and computing power. Recent advances in statistics have witnessed great development in the field of high-dimensional data analysis. This machine learning dissertation proposes three methods that study three different components of a general framework of the high-dimensional structured regression problem. A general theme of the proposed methods is that they cast a certain structured regression as a convex optimization problem. In so doing, the theoretical properties of each method can be well studied, and efficient computation is facilitated. Each method is accompanied by a thorough theoretical analysis of its performance, and also by an R package containing its practical implementation. It is shown that the proposed methods perform favorably (both theoretically and practically) compared with pre-existing methods.

9. Asymptotics and Interpretability of Decision Trees and Decision Tree Ensembles

Decision trees and decision tree ensembles are widely used nonparametric statistical models. A decision tree is a binary tree that recursively segments the covariate space along the coordinate directions to create hyper rectangles as basic prediction units for fitting constant values within each of them. A decision tree ensemble combines multiple decision trees, either in parallel or in sequence, in order to increase model flexibility and accuracy, as well as to reduce prediction variance. Despite the fact that tree models have been extensively used in practice, results on their asymptotic behaviors are scarce. This machine learning dissertation presents analyses on tree asymptotics in the perspectives of tree terminal nodes, tree ensembles, and models incorporating tree ensembles respectively. The study introduces a few new tree-related learning frameworks which provides provable statistical guarantees and interpretations. A study on the Gini index used in the greedy tree building algorithm reveals its limiting distribution, leading to the development of a test of better splitting that helps to measure the uncertain optimality of a decision tree split. This test is combined with the concept of decision tree distillation, which implements a decision tree to mimic the behavior of a block box model, to generate stable interpretations by guaranteeing a unique distillation tree structure as long as there are sufficiently many random sample points. Also applied is mild modification and regularization to the standard tree boosting to create a new boosting framework named Boulevard. Also included is an integration of two new mechanisms: honest trees , which isolate the tree terminal values from the tree structure, and adaptive shrinkage , which scales the boosting history to create an equally weighted ensemble. This theoretical development provides the prerequisite for the practice of statistical inference with boosted trees. Lastly, the thesis investigates the feasibility of incorporating existing semi-parametric models with tree boosting. 

10. Bayesian Models for Imputing Missing Data and Editing Erroneous Responses in Surveys

This dissertation develops Bayesian methods for handling unit nonresponse, item nonresponse, and erroneous responses in large-scale surveys and censuses containing categorical data. The focus is on applications of nested household data where individuals are nested within households and certain combinations of the variables are not allowed, such as the U.S. Decennial Census, as well as surveys subject to both unit and item nonresponse, such as the Current Population Survey.

11. Localized Variable Selection with Random Forest  

Due to recent advances in computer technology, the cost of collecting and storing data has dropped drastically. This makes it feasible to collect large amounts of information for each data point. This increasing trend in feature dimensionality justifies the need for research on variable selection. Random forest (RF) has demonstrated the ability to select important variables and model complex data. However, simulations confirm that it fails in detecting less influential features in presence of variables with large impacts in some cases. This dissertation proposes two algorithms for localized variable selection: clustering-based feature selection (CBFS) and locally adjusted feature importance (LAFI). Both methods aim to find regions where the effects of weaker features can be isolated and measured. CBFS combines RF variable selection with a two-stage clustering method to detect variables where their effect can be detected only in certain regions. LAFI, on the other hand, uses a binary tree approach to split data into bins based on response variable rankings, and implements RF to find important variables in each bin. Larger LAFI is assigned to variables that get selected in more bins. Simulations and real data sets are used to evaluate these variable selection methods. 

12. Functional Principal Component Analysis and Sparse Functional Regression

The focus of this dissertation is on functional data which are sparsely and irregularly observed. Such data require special consideration, as classical functional data methods and theory were developed for densely observed data. As is the case in much of functional data analysis, the functional principal components (FPCs) play a key role in current sparse functional data methods via the Karhunen-Loéve expansion. Thus, after a review of relevant background material, this dissertation is divided roughly into two parts, the first focusing specifically on theoretical properties of FPCs, and the second on regression for sparsely observed functional data.

13. Essays In Causal Inference: Addressing Bias In Observational And Randomized Studies Through Analysis And Design

In observational studies, identifying assumptions may fail, often quietly and without notice, leading to biased causal estimates. Although less of a concern in randomized trials where treatment is assigned at random, bias may still enter the equation through other means. This dissertation has three parts, each developing new methods to address a particular pattern or source of bias in the setting being studied. The first part extends the conventional sensitivity analysis methods for observational studies to better address patterns of heterogeneous confounding in matched-pair designs. The second part develops a modified difference-in-difference design for comparative interrupted time-series studies. The method permits partial identification of causal effects when the parallel trends assumption is violated by an interaction between group and history. The method is applied to a study of the repeal of Missouri’s permit-to-purchase handgun law and its effect on firearm homicide rates. The final part presents a study design to identify vaccine efficacy in randomized control trials when there is no gold standard case definition. The approach augments a two-arm randomized trial with natural variation of a genetic trait to produce a factorial experiment. 

14. Bayesian Shrinkage: Computation, Methods, and Theory

Sparsity is a standard structural assumption that is made while modeling high-dimensional statistical parameters. This assumption essentially entails a lower-dimensional embedding of the high-dimensional parameter thus enabling sound statistical inference. Apart from this obvious statistical motivation, in many modern applications of statistics such as Genomics, Neuroscience, etc. parameters of interest are indeed of this nature. For over almost two decades, spike and slab type priors have been the Bayesian gold standard for modeling of sparsity. However, due to their computational bottlenecks, shrinkage priors have emerged as a powerful alternative. This family of priors can almost exclusively be represented as a scale mixture of Gaussian distribution and posterior Markov chain Monte Carlo (MCMC) updates of related parameters are then relatively easy to design. Although shrinkage priors were tipped as having computational scalability in high-dimensions, when the number of parameters is in thousands or more, they do come with their own computational challenges. Standard MCMC algorithms implementing shrinkage priors generally scale cubic in the dimension of the parameter making real-life application of these priors severely limited. 

The first chapter of this dissertation addresses this computational issue and proposes an alternative exact posterior sampling algorithm complexity of which that linearly in the ambient dimension. The algorithm developed in the first chapter is specifically designed for regression problems. The second chapter develops a Bayesian method based on shrinkage priors for high-dimensional multiple response regression. Chapter three chooses a specific member of the shrinkage family known as the horseshoe prior and studies its convergence rates in several high-dimensional models. 

15.  Topics in Measurement Error Analysis and High-Dimensional Binary Classification

This dissertation proposes novel methods to tackle two problems: the misspecified model with measurement error and high-dimensional binary classification, both have a crucial impact on applications in public health. The first problem exists in the epidemiology practice. Epidemiologists often categorize a continuous risk predictor since categorization is thought to be more robust and interpretable, even when the true risk model is not a categorical one. Thus, their goal is to fit the categorical model and interpret the categorical parameters. The second project considers the problem of high-dimensional classification between the two groups with unequal covariance matrices. Rather than estimating the full quadratic discriminant rule, it is proposed to perform simultaneous variable selection and linear dimension reduction on original data, with the subsequent application of quadratic discriminant analysis on the reduced space. Further, in order to support the proposed methodology, two R packages were developed, CCP and DAP, along with two vignettes as long-format illustrations for their usage.

16. Model-Based Penalized Regression

This dissertation contains three chapters that consider penalized regression from a model-based perspective, interpreting penalties as assumed prior distributions for unknown regression coefficients. The first chapter shows that treating a lasso penalty as a prior can facilitate the choice of tuning parameters when standard methods for choosing the tuning parameters are not available, and when it is necessary to choose multiple tuning parameters simultaneously. The second chapter considers a possible drawback of treating penalties as models, specifically possible misspecification. The third chapter introduces structured shrinkage priors for dependent regression coefficients which generalize popular independent shrinkage priors. These can be useful in various applied settings where many regression coefficients are not only expected to be nearly or exactly equal to zero, but also structured.

17. Topics on Least Squares Estimation

This dissertation revisits and makes progress on some old but challenging problems concerning least squares estimation, the work-horse of supervised machine learning. Two major problems are addressed: (i) least squares estimation with heavy-tailed errors, and (ii) least squares estimation in non-Donsker classes. For (i), this problem is studied both from a worst-case perspective, and a more refined envelope perspective. For (ii), two case studies are performed in the context of (a) estimation involving sets and (b) estimation of multivariate isotonic functions. Understanding these particular aspects of least squares estimation problems requires several new tools in the empirical process theory, including a sharp multiplier inequality controlling the size of the multiplier empirical process, and matching upper and lower bounds for empirical processes indexed by non-Donsker classes.

How to Learn More about Machine Learning

At our upcoming event this November 16th-18th in San Francisco,  ODSC West 2021  will feature a plethora of talks, workshops, and training sessions on machine learning and machine learning research. You can  register now for 50% off all ticket types  before the discount drops to 40% in a few weeks. Some  highlighted sessions on machine learning  include:

• Towards More Energy-Efficient Neural Networks? Use Your Brain!: Olaf de Leeuw | Data Scientist | Dataworkz
• Practical MLOps: Automation Journey: Evgenii Vinogradov, PhD | Head of DHW Development | YooMoney
• Applications of Modern Survival Modeling with Python: Brian Kent, PhD | Data Scientist | Founder The Crosstab Kite
• Using Change Detection Algorithms for Detecting Anomalous Behavior in Large Systems: Veena Mendiratta, PhD | Adjunct Faculty, Network Reliability and Analytics Researcher | Northwestern University

Sessions on MLOps:

• Tuning Hyperparameters with Reproducible Experiments: Milecia McGregor | Senior Software Engineer | Iterative
• MLOps… From Model to Production: Filipa Peleja, PhD | Lead Data Scientist | Levi Strauss & Co
• Operationalization of Models Developed and Deployed in Heterogeneous Platforms: Sourav Mazumder | Data Scientist, Thought Leader, AI & ML Operationalization Leader | IBM
• Develop and Deploy a Machine Learning Pipeline in 45 Minutes with Ploomber: Eduardo Blancas | Data Scientist | Fidelity Investments

Sessions on Deep Learning:

• GANs: Theory and Practice, Image Synthesis With GANs Using TensorFlow: Ajay Baranwal | Center Director | Center for Deep Learning in Electronic Manufacturing, Inc
• Machine Learning With Graphs: Going Beyond Tabular Data: Dr. Clair J. Sullivan | Data Science Advocate | Neo4j
• Deep Dive into Reinforcement Learning with PPO using TF-Agents & TensorFlow 2.0: Oliver Zeigermann | Software Developer | embarc Software Consulting GmbH
• Get Started with Time-Series Forecasting using the Google Cloud AI Platform: Karl Weinmeister | Developer Relations Engineering Manager | Google

Daniel Gutierrez, ODSC

Daniel D. Gutierrez is a practicing data scientist who’s been working with data long before the field came in vogue. As a technology journalist, he enjoys keeping a pulse on this fast-paced industry. Daniel is also an educator having taught data science, machine learning and R classes at the university level. He has authored four computer industry books on database and data science technology, including his most recent title, “Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R.” Daniel holds a BS in Mathematics and Computer Science from UCLA.

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Curriculum: Ph.D. Dissertation

The primary requirement of the Ph.D. student is to do original and substantial research. This research is reported for review in the Ph.D. dissertation.

There are three milestones for a dissertation: 1) passing the qualifying exam; 2) writing and successfully defending a thesis proposal; 3) writing and successfully defending a thesis. Students are responsible for regularly viewing and understanding the processes and specific requirements for these milestones as detailed in the ML Ph.D. Handbook , as well as any additional requirements of their home unit.

Thesis advisors for the ML Ph.D. can be any of the participating ML Ph.D. Program Faculty, regardless of whether they are in the same unit as the student. Students will be eligible for Graduate Research Assistantships and Graduate Teaching Assistantships in accordance with the policies of their home units.

Qualifying Exam

The purpose of the Qualifying Examination is to judge the candidate’s potential as an independent researcher.

The Ph.D. qualifying exam consists of a focused literature review that will take place over the course of one semester. At the beginning of the second semester of their second year, a qualifying committee consisting of three members of the ML Ph.D. Program Faculty will assign, in consultation with the student, a course of study consisting of influential papers, books, or other intellectual artifacts relevant to the student’s research interests. The student’s focus area and current research efforts (and related portfolio) will be considered in defining the course of study.

At the end of the semester, the student will submit a written report the demonstrates their understanding of both the technical content and the context of each artifact. Subsequently, the student will have a closed oral exam with the three members of the committee. The exam will be interactive, with the student and the committee discussing and criticizing each work and posing questions related to the student's current research to determine the breadth of the student’s knowledge in that specific area. 

The success of the examination will be determined by the committee’s qualitative assessment of the student’s understanding of the theory, methods, and ultimate impact of the assigned syllabus.

The student will be given a passing grade for meeting the requirements of the committee in both the written and the oral part. Unsatisfactory performance on either part will require the student to redo the entire qualifying exam in the following semester. Each student will be allowed only two attempts at the exam.

Students are expected to perform the review by the end of their second year in the program. Students taking the qualifying exam should register for CS 7999. To receive a permit or if you have any questions about the qualifying exam, please contact the ML Academic Advisor, Stephanie Niebuhr at  [email protected] .

Research Proposal

As the first step towards completing a dissertation, the student must prepare and defend a Research Proposal. The purpose of the proposal is to give the faculty an opportunity to give feedback on the student’s research direction, and to make sure they are developing into able communicators.

The Ph.D. proposal consists of a short document - 30 pages or less - describing the student's thesis, and a presentation to a proposal committee consisting of three  ML Ph.D. Program Faculty  chosen by the student. All coursework must be completed before the proposal can be scheduled.*

Ph.D. Thesis

The Ph.D. thesis committee consists of five faculty members, including the three ML Ph.D. Program Faculty who served on the proposal committee, and two additional members. At least one member must be external to the ML Ph.D. program. The committee is charged with approving the written dissertation and administering the final defense.  The defense consists of a public seminar followed by oral examination from the thesis committee.

Institute guidelines and requirements for formatting the document can be found in the Georgia Institute of Technology Graduate Studies Thesis and Dissertation Manual.  

*The Research Proposal and Ph.D. Thesis both require several forms before and after scheduling and moving forward with these milestones. Please review the ML Handbook and contact the ML Academic Advisor, Stephanie Niebuhr at [email protected] at least one month prior to express the intent of submitting your thesis proposal or defense, review coursework (due at the time of the proposal) and committee requirements, and clarify all details of the process.

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PhD Research Proposal Machine Learning

A Ph.D. research proposal is an official paper that defines research of the students proposed for experiment. The process of designing the fascinating proposal in (ML) Machine learning includes detecting the effective issue, expressing its importance, displaying the initial feedback of the functioning literature and recommending techniques or approaches to handle the problem.

We act as a trusted partner for scholars to carry out top tier research proposal on machine learning. Our dedicated staffs has more than 18+ years of experience to offer top notch research proposal. Effective and customized research proposal on all areas of machine learning are provided by us. As per scholars’ university rules we frame the proposal and all steps are covered by us.

Here, we provide the model for a Ph.D. research proposal in machine learning:

Robust and Interpretable Deep Learning for Medical Image Diagnostics

• Introduction :
• We elaborately establish the importance of medical image diagnostics and the character of machine learning in developing the diagnostics for a perfect accuracy.
• The faced obstacles are highlighted. For example, the black-box nature of deep learning models and their sensitivity to adversarial attacks.
• Background and Literature Review :
• By utilizing machine learning, we analyse the modernized medical image diagnostics.
• The latest technique is considered for explaining the deep learning models.
• The familiar vulnerabilities of these models are investigated by us for adversarial disturbance and their significance in medical settings.
• Problem Statement :
• The vital key of the research problem is defined: How can we improve deep learning models for medical image diagnostics that are both interpretable and robust against adversarial assaults?
• Sketch the expected effect for solving this problem which involves reliability of ML-driven diagnostics.
• Research Objectives and Questions :
• In what way do we transform existing architectures to yield more interpretable feature representations?
• What kinds of methods are applied to identify and reduce adversarial disturbances in medical images?
• Proposed Methodology :
• Interpretable Models : We analyse tools such as attention mechanisms, activation maximization, or feature visualization designed for medical imagery.
• Robustness Enhancement : Defence strategies are inspected like adversarial training, input pre-processing and gradient obfuscation in the environment of medical images.
• Validation : The standard medical images datasets are employed for initiating fake adversarial disturbances for estimating the validity and clarification of our proposed models.
• Expected Outcomes :
• A pair of new deep learning architectures is developed by us for medical image observation that contributes perception into their decision-making process.
• Developing the validity of diagnostic models against adversarial threats through the especially designed defence mechanisms.
• Preliminary Results (if any) :
• If we previously manage some initial observations or analyses, display the reasoning here. This process boosts up our proposed methods.
• From initial experiments to final estimations, the failure is provided for the expected progression of our research. For the purpose of the Ph.D. proposal, this extends the duration up to several years.
• Conclusion :
• Let’s give an outline about the importance of proposed research , it’s goals and the future effect on the domain of  medical diagnostics and machine learning
• References :
• Make a list of all the academic articles, books and other sources that we referenced on our proposal.

This is the general model and that is suitable for various machine learning topics. Not to forget it, the capacity of a research proposal is not only evaluated by its significance and portability of the research questions, but also it requires the clarity and logic of the presentation. Constantly, we always follow the advice of potential supervisors or advisors to concentrate and improve our model with advancements. Our research proposal presentation paves a way for successful doctoral research in machine learning as we abide by the standards.

Get supreme research proposal support from phddirection.com….

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PhD Research Methodology Ideas in Machine Learning

PhD Research Methodology Ideas in Machine Learning will be shred from reputable and international journals. As per users’ interest, we suggest the best topic with referral papers. Our team will use correct methodology to build up the machine learning research work. We are always alert on trending tools and methodologies and our research team stays updated for the benefit of scholars. Massive resources and updated techniques stay as a vital importance for the success of our work.

• Notifying problems of a machine by using Machine Learning
• ProFiOt: Abnormal Behavior Profiling (ABP) of IoT devices based on a machine learning approach
• Malware detection on Android smartphones using API class and machine learning
• Challenges, comparative analysis and a proposed methodology to predict sentiment from movie reviews using machine learning
• An Approach to Optimize Future Inbound Logistics Processes Using Machine Learning Algorithms
• Quantum Machine Learning with HQC Architectures using non-Classically Simulable Feature Maps
• Machine Learning-Based Intrusion Detection System for the Internet of Vehicles
• Detection of Inter-Turn Short Circuit in Permanent Magnet Synchronous Motor using Machine Learning
• A Classifiers Experimentation with Quantum Machine Learning
• A Machine Learning Based Implementation of Product and Service Recommendation Models
• Analogous Examination of Various Machine Learning Algorithm Applied to Big Data
• Content-Based Image Retrieval using Local Patterns and Supervised Machine Learning Techniques
• WeChat Text and Picture Messages Service Flow Traffic Classification Using Machine Learning Technique
• Assessment of Machine Learning Methods for Classification in Single Cell ATAC-seq
• Machine Learning Algorithms for ccRCC Data Analysis
• Development of Machine Learning-based Predictive Models for Wireless Indoor Localization Application with Feature Ranking via Recursive Feature Elimination Algorithm
• Feature Evaluation of Emerging E-Learning Systems Using Machine Learning: An Extensive Survey
• Optimized FPGA Architecture for Machine Learning Applications using Posit Multipliers
• Sparse Ensemble Machine Learning to Improve Robustness of Long-Term Decoding in iBMIs
• The Detection Method for XSS Attacks on NFV by Using Machine Learning Models

Why Work With Us ?

Senior research member, research experience, journal member, book publisher, research ethics, business ethics, valid references, explanations, paper publication, 9 big reasons to select us.

Our Editor-in-Chief has Website Ownership who control and deliver all aspects of PhD Direction to scholars and students and also keep the look to fully manage all our clients.

Our world-class certified experts have 18+years of experience in Research & Development programs (Industrial Research) who absolutely immersed as many scholars as possible in developing strong PhD research projects.

We associated with 200+reputed SCI and SCOPUS indexed journals (SJR ranking) for getting research work to be published in standard journals (Your first-choice journal).

PhDdirection.com is world’s largest book publishing platform that predominantly work subject-wise categories for scholars/students to assist their books writing and takes out into the University Library.

Our researchers provide required research ethics such as Confidentiality & Privacy, Novelty (valuable research), Plagiarism-Free, and Timely Delivery. Our customers have freedom to examine their current specific research activities.

Our organization take into consideration of customer satisfaction, online, offline support and professional works deliver since these are the actual inspiring business factors.

Solid works delivering by young qualified global research team. "References" is the key to evaluating works easier because we carefully assess scholars findings.

Detailed Videos, Readme files, Screenshots are provided for all research projects. We provide Teamviewer support and other online channels for project explanation.

Worthy journal publication is our main thing like IEEE, ACM, Springer, IET, Elsevier, etc. We substantially reduces scholars burden in publication side. We carry scholars from initial submission to final acceptance.

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• Deep Learning Research Proposal

The word deep learning is the study and analysis of deep features that are hidden in the data using some intelligent deep learning models . Recently, it turns out to be the most important research paradigm for advanced automated systems for decision-making . Deep learning is derived from machine learning technologies that learn based on hierarchical concepts . So, it is best for performing complex and long mathematical computations in deep learning .

This page describes to you the innovations of deep learning research proposals with major challenges, techniques, limitations, tools, etc.!!!

One most important thing about deep learning is the multi-layered approach . It enables the machine to construct and work the algorithms in different layers for deep analysis . Further, it also works on the principle of artificial neural networks which functions in the same human brain. Since it got inspiration from the human brain to make machines automatically understand the situation and make smart decisions accordingly.  Here, we have given you some of the important real-time applications of deep learning.

Deep Learning Project Ideas

• Natural Language Processing
• Pattern detection in Human Face
• Image Recognition and Object Detection
• Driverless UAV Control Systems
• Prediction of Weather Condition Variation
• Machine Translation for Autonomous Cars
• Medical Disorder Diagnosis and Treatment
• Traffic and Speed Control in Motorized Systems
• Voice Assistance for Dense Areas Navigation
• Altitude Control System for UAV and Satellites

Now, we can see the workflow of deep learning models . Here, we have given you the steps involved in the deep learning model. This assists you to know the general procedure of deep learning model execution . Similarly, we precisely guide you in every step of your proposed deep learning model . Further, the steps may vary based on the requirement of the handpicked deep learning project idea. Anyway, the deep learning model is intended to grab deep features of data by processing through neural networks . Then, the machine will learn and understand the sudden scenarios for controlling systems.

Process Flow of Deep Learning

• Step 1 – Load the dataset as input
• Step 2 – Extraction of features
• Step 3 – Process add-on layers for more abstract features
• Step 4 – Perform feature mapping
• Step 5 –Display the output

Although deep learning is more efficient to automatically learn features than conventional methods, it has some technical constraints. Here, we have specified only a few constraints to make you aware of current research. Beyond these primary constraints, we also handpicked more number of other constraints. To know other exciting research limitations in deep learning , approach us. We will make you understand more from top research areas.

Deep Learning Limitations

• Test Data Variation – When the test data is different from training data, then the employed deep learning technique may get failure. Further, it also does not efficiently work in a controlled environment.
• Huge Dataset – Deep learning models efficiently work on large-scale datasets than limited data

Our research team is highly proficient to handle different deep learning technologies . To present you with up-to-date information, we constantly upgrade our research knowledge in all advanced developments. So, we are good not only at handpicking research challenges but also more skilled to develop novel solutions. For your information, here we have given you some most common data handling issues with appropriate solutions. 

What are the data handling techniques?

• Variables signifies the linear combo of factors with errors
• Depends on the presence of different unobserved variables (i.e., assumption)
• Identify the correlations between existing observed variables
• If the data in a column has fixed values, then it has “0” variance.
• Further, these kinds of variables are not considered in target variables
• If there is the issue of outliers, variables, and missing values, then effective feature selection will help you to get rid out of it. 
• So, we can employ the random forest method
• Remove the unwanted features from the model
• Repeat the same process until attaining maximum  error rate
• At last, define the minimum features
• Remove one at a time and check the error rate
• If there are dependent values among data columns, then may have redundant information due to similarities.
• So, we can filter the largely correlated columns based on coefficients of correlation
• Add one at a time for high performance
• Enhance the entire model efficiency
• Addresses the possibility where data points are associated with high-dimensional space
• Select low-dimensional embedding to generate related distribution
•   Identify the missing value columns and remove them by threshold
• Present variable set is converted to a new variable set
• Also, referred to as a linear combo of new variables
• Determine the location of each point by pair-wise spaces among all points which are represented in a matrix
• Further, use standard multi-dimensional scaling (MDS) for determining low-dimensional points locations

In addition, we have also given you the broadly utilized deep learning models in current research . Here, we have classified the models into two major classifications such as discriminant models and generative models . Further, we have also specified the deep learning process with suitable techniques. If there is a complex situation, then we design new algorithms based on the project’s needs . On the whole, we find apt solutions for any sort of problem through our smart approach to problems.

Deep Learning Models

• CNN and NLP (Hybrid)
• Domain-specific
• Image conversion
• Meta-Learning

Furthermore, our developers are like to share the globally suggested deep learning software and tools . In truth, we have thorough practice on all these developing technologies. So, we are ready to fine-tuned guidance on deep learning libraries, modules, packages, toolboxes , etc. to ease your development process. By the by, we will also suggest you best-fitting software/tool for your project . We ensure you that our suggested software/tool will make your implementation process of deep learning projects techniques more simple and reliable .

Deep Learning Software and Tools

• Caffe & Caffe2
• Deep Learning 4j
• Microsoft Cognitive Toolkit

So far, we have discussed important research updates of deep learning . Now, we can see the importance of handpicking a good research topic for an impressive deep learning research proposal. In the research topic, we have to outline your research by mentioning the research problem and efficient solutions . Also, it is necessary to check the future scope of research for that particular topic.

The topic without future research direction is not meant to do research!!!

For more clarity, here we have given you a few significant tips to select a good deep learning research topic.

How to write a research paper on deep learning?

• Check whether your selected research problem is inspiring to overcome but not take more complex to solve
• Check whether your selected problem not only inspires you but also create interest among readers and followers
• Check whether your proposed research create a contribution to social developments
• Check whether your selected research problem is unique

From the above list, you can get an idea about what exactly a good research topic is. Now, we can see how a good research topic is identified.

• To recognize the best research topic, first undergo in-depth research on recent deep learning studied by referring latest reputed journal papers.
• Then, perform a review process over the collected papers to detect what are the current research limitations, which aspect not addressed yet, which is a problem is not solved effectively,   which solution is needed to improve, what the techniques are followed in recent research, etc.
• This literature review process needs more time and effort to grasp knowledge on research demands among scholars.
• If you are new to this field, then it is suggested to take the advice of field experts who recommend good and resourceful research papers.
• Majorly, the drawbacks of the existing research are proposed as a problem to provide suitable research solutions.
• Usually, it is good to work on resource-filled research areas than areas that have limited reference.
• When you find the desired research idea, then immediately check the originality of the idea. Make sure that no one is already proved your research idea.
• Since, it is better to find it in the initial stage itself to choose some other one.
• For that, the search keyword is more important because someone may already conduct the same research in a different name. So, concentrate on choosing keywords for the literature study.

How to describe your research topic?

One common error faced by beginners in research topic selection is a misunderstanding. Some researchers think topic selection means is just the title of your project. But it is not like that, you have to give detailed information about your research work on a short and crisp topic . In other words, the research topic is needed to act as an outline for your research work.

For instance: “deep learning for disease detection” is not the topic with clear information. In this, you can mention the details like type of deep learning technique, type of image and its process, type of human parts, symptoms , etc.

The modified research topic for “deep learning for disease detection” is “COVID-19 detection using automated deep learning algorithm”

 For your awareness, here we have given you some key points that need to focus on while framing research topics. To clearly define your research topic, we recommend writing some text explaining:

• Research title
• Previous research constraints
• Importance of the problem that overcomes in proposed research
• Reason of challenges in the research problem
• Outline of problem-solving possibility

To the end, now we can see different research perspectives of deep learning among the research community. In the following, we have presented you with the most demanded research topics in deep learning such as image denoising, moving object detection, and event recognition . In addition to this list, we also have a repository of recent deep learning research proposal topics, machine learning thesis topics . So, communicate with us to know the advanced research ideas of deep learning.

Research Topics in Deep Learning

• Continuous Network Monitoring and Pipeline Representation in Temporal Segment Networks
• Dynamic Image Networks and Semantic Image Networks
• Advance Non-uniform denoising verification based on FFDNet and DnCNN
• Efficient image denoising based on ResNets and CNNs
• Accurate object recognition in deep architecture using ResNeXts, Inception Nets and  Squeeze and Excitation Networks
• Improved object detection using Faster R-CNN, YOLO, Fast R-CNN, and Mask-RCNN

Overall, we are ready to support you in all significant and new research areas of deep learning . We guarantee you that we provide you novel deep learning research proposal in your interested area with writing support. Further, we also give you code development , paper writing, paper publication, and thesis writing services . So, create a bond with us to create a strong foundation for your research career in the deep learning field.

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Latest phd thesis topics in machine learning.

• With progressive technological development, the exploration of machine learning has increased a huge number of applications. Consequentially. Machine learning instigates an essential part of implementing smart and automated applications by intelligent data analysis.
• The applicability of machine learning is abundant in many real-world application fields, such as predictive analytics and intelligent decision-making, cyber-security systems, smart cities, healthcare, e-commerce, agriculture, finance, retail, social media, traffic prediction and transportation, computer vision applications, user behavior analytics and context-aware smartphone applications, bioinformatics, cheminformatics, computer networks, DNA sequence classification, economics and banking, robotics, advanced engineering, and many more.
• Recently described branches of machine learning are computational learning theory, adversarial machine learning, quantum machine learning, robot learning, and meta-learning. Efficient data processing and handling the diverse learning algorithms are the constraints that are needed to be the focus in machine learning. PHD thesis on machine learning contributes to the challenges, promising research opportunities, and effective solutions in various application areas. Below is the list of PHD thesis topics on machine learning to effectively explore, scrutinize and discover the new findings of machine learning systems.

List of Sample PHD Thesis in Machine Learning

• Incremental Learning for Large-Scale Data Stream Analytics in a Complex Environment
• Neural Sequential Transfer Learning for Relation Extraction
• Predicting Depression and Suicide Ideation in the Canadian Population Using Social Media Data
• Neural Aspect-based Text Generation
• Leveraging Social Media and Machine Learning for enhanced Communication and Understanding between Organizations and Stakeholders
• Deep Learning Methods for Short,Informal, and Multilingual Text Analytics
• Deep Learning Based Cursive Text Detection and Recognition in Natural Scene Images
• Deep Learning-Based Text Detection and Recognition
• Explaining Deep Neural Networks
• Machine Learning Techniques in Spam Filtering
• Anomaly-Based Network Intrusion Detection Using Machine Learning
• Machine Learning for Financial Products Recommendation
• Sentiment Analysis of Textual Content in Social Networks
• Deep Learning For Time Series Classification
• Deep Learning for Traffic Time Series Data Analysis
• Novel applications of Machine Learning to Network Traffic Analysis and Prediction
• Deep Learning for Animal Recognition
• Neural Transfer Learning for Natural Language Processing
• Scalable and Ensemble Learning for Big Data
• Ensembles for Time Series Forecasting
• Sample-Efficient Deep Reinforcement Learning for Continuous Control
• Towards Generalization and Efficiency in Reinforcement Learning
• Transfer Learning with Deep Neural Networks for Computer Vision
• Deep Learning for Recommender Systems
• CHAMELEON: A Deep Learning Meta-Architecture For News Recommender Systems
• Learning in Dynamic Data-Streams with a Scarcity of Labels
• Learning Meaning Representations For Text Generation With Deep Generative Models
• Social Media Sentiment Analysis with a Deep Neural Network: An Enhanced Approach Using User Behavioral Information
• Global-Local Word Embedding for Text Classification
• Measuring Generalization and Overfitting in Machine Learning
• Handling Class Imbalance Using Swarm Intelligence Techniques, Hybrid Data and Algorithmic Level Solutions
• Using Data Science and Predictive Analytics to Understand 4-Year University Student Churn
• Deep Learning Based Imbalanced Data Classification and Information Retrieval for Multimedia Big Data
• Improving Geospatial Data Search Ranking Using Deep Learning and User Behaviour Data
• An Investigation Into Machine Learning Solutions Involving Time Series Across Different Problem Domains
• Deep Learning Applications for Biomedical Data and Natural Language Processing
• Deep Neural Network Models for Image Classification and Regression
• Deep learning for medical report texts
• Deep multi-agent reinforcement learning
• Artificial intelligence methods to support people management in organisations
• An Intelligent Recommender System Based on Short-term Disease Risk Prediction for Patients with Chronic Diseases in a Telehealth Environment
• Bringing Interpretability and Visualization with Artificial Neural Networks
• Investigating machine learning methods in Recommender systems
• Adaptive Machine Learning Algorithms For Data Streams Subject To Concept Drifts
• Active Learning for Data Streams
• Heart Diseases Diagnosis Using Artificial Neural Networks
• Advanced Natural Language Processing and Temporal Mining for Clinical Discovery
• Uncertainty in Deep Learning
• Parallel Transfer Learning: Accelerating Reinforcement Learning in Multi-Agent Systems
• Sentiment analysis on students Real-time Feedback
• Aspect-Based Opinion Mining From Customer Reviews
• Word Embeddings for Natural Language Processing
• On Effectively Creating Ensembles of Classifiers
• Design of Intelligent Ensembled Classifiers Combination Methods
• ELSE: Ensemble Learning System with Evolution for Content Based Image Retrieval
• Using Hybrid Algorithm to Improve Intrusion Detection in Multi Layer Feed Forward Neural Networks
• Achieving Consistent Near-Optimal Pattern Recognition Accuracy Using Particle Swarm Optimization to Pre-Train Artificial Neural Networks
• Using Assessments of Contextual Learning to Identify Characteristics of Adaptive Transfer in Medical Students
• Recursive Deep Learning for Natural Language Processing and Computer Vision
• Machine learning strategies for multi-step-ahead time series forecasting
• General Attention Mechanism for Artificial Intelligence Systems
• Defense Acquisition University: A Study of Employee Perceptions on Web Based Learning Transfer
• Incremental Learning with Large Datasets
• Machine Learning and Data Mining Methods for Recommender Systems and Chemical Informatics
• Transfer of Learning in Leadership Development: Lived Experiences of HPI Practitioners
• Online Ensemble Learning in the Presence of Concept Drift
• Learning From Data Streams With Concept Drift
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• PhD Research Proposal on Machine Learning

We design a PhD research proposal in machine learning (ML) which involves finding a particular issue in a field of study, preparing research queries, proposing techniques and summarizing the possible specialty of the research. Our work includes the following a well-defined problem statement, flawless languages, on time delivery, unlimited revisions and technical discussions. The perfect structure of PhD research proposal is offered at phdservices.org .The following is an overview that helps us to construct a PhD research proposal in ML.

Title of the Proposal

       Enhancing the Robustness of Deep Neural Networks (DNNs) against Adversarial Attacks

Introduction

• We shortly introduce the area of ML and its importance.
• Describing the particular field within ML that we study and solve it.
• To represent the limitations and similarity of harmful threats on DNNs.

Problem Statement

• By examining the issue we challenge the research.
• We offer context and inspiration for why this problem is difficult to overcome.

Research Questions and Assumption

• What are the most efficient ideas to predict and reduce adversarial attacks on DNN?
• Can we build a generalizable model that improves the robustness of these frameworks across various fields?
• How do harmful defenses affect the understandability and basic performance of DNNs?

Literature Survey

• We outline the traditional research on adversarial ML.
• By detecting the gaps we improve the recent techniques and skills.
• To develop framework efficiency we justify the requirement of our research.
• We list down the primary and secondary goals of our research.
• Explaining what our research focuses to attain in terms of theoretical and experimental contributions.

Methodology

• For directing the research we describe the proposed methods.
• We explain data collection, framework development, practical design and validation metrics.
• Discussing our option of ML approaches, models and techniques.

Expected Results

• We detect the possible outcomes and their suggestions.
• To define how our research commits to the area of ML.
• Explaining the possible applications of our powerful neural networks.

Work Plan and Timeline

• Sketch the stages of our research.
• We offer duration for every section involving literature review, data gathering, experimentation and writing.

Bibliography

• By adding a preliminary list of references we scratch the research.

       We recognize that the significance and depth of our proposal is based on the needs of the PhD program that we’re applying and the expectations of prospective mentors. It should also return us the realistic scope of task that is finished within the usual duration for a PhD (actually 3-5 years).

Before submitting our proposal we ensure to:

• Check the Alignment: Make sure that our research interests coincide with our skills and the resources are available at the university.
• Proofread: To find any fault and not clear phases, we should get feedback on our proposal from our experts and professors.
• Concise: Our proposal should be clear and accurate as potential and recognize that it is an outline and not a full paper.
• Feasible: We should be realistic about what is achieved in the scope and duration of a PhD course.
• Original: Ensure that our proposal demonstrates an original plan with methods to an existing problem.

       We design a robust PhD research proposal that is a complicated process in securing a stage in a doctoral program and setting a position for a successful research attempt.

Get your dissertation proposals also done with top notched quality free from error.   We cater the best needs for your machine learning proposals that meet the standards to your university. Our prices are affordable. Money back policy and on time delivery is our key ethics.

Order from us today…Contact for more help…

Machine Learning Research Proposals Ideas

If you are looking for a best research proposal idea on ML for your PhD …. look no further other than phdservices.org

Our commitment towards working will help you to attain high grade with affordable pricing for all ML research proposals. Our proposals include the summary of the proposed research work and its conclusions. So, it gets easily accepted as here we contribute more to machine learning field. Here our writers have experienced knowledge on machine learning topics which paves the path for successful research.

• Real Time Facial Emotion Recognition Methods using Different Machine Learning Techniques
• Rotor position estimator based on machine learning
• Quantum Adversarial Machine Learning: Status, Challenges and Perspectives
• Machine Learning model for Numeral Recognition
• Electrical Impedance Tomography, Enclosure Method and Machine Learning
• Comparison of Manifold Learning Algorithms for Rapid Circuit Defect Extraction in SPICE-Augmented Machine Learning
• The Development of a System for Objects Recognition in Reverse Vending Machine Using Machine Learning Technologies
• Defeating the Credit Card Scams Through Machine Learning Algorithms
• Error-Resilient Reconfigurable Boosting Extreme Learning Machine for ECG Telemonitoring Systems
• PODA: Positive Active Power Outlier Detection Based on Auto Machine Learning
• A study on several Machine-learning methods for classification of Malignant and benign clustered microcalcifications
• A review of supervised machine learning algorithms
• Sentiment Analysis of Audio Files Using Machine Learning and Textual Classification of Audio Data
• MALADY: A Machine Learning-Based Autonomous Decision-Making System for Sensor Networks
• Industry 4.0 and International Collaborative Online Learning in a Higher Education Course on Machine Learning
• Multi-feature Machine Learning with Quantum Superposition
• Order-preserving Encryption as a Tool for Privacy-Preserving Machine Learning
• Hyperparameters Search Methods for Machine Learning Linear Workflows
• High Throughput Ultrasonic Multi-implant Readout Using a Machine-Learning Assisted CDMA Receiver
• Web Content Based Features for Malicious Web Page Detection Using Machine Learning

MILESTONE 1: Research Proposal

Finalize journal (indexing).

Before sit down to research proposal writing, we need to decide exact journals. For e.g. SCI, SCI-E, ISI, SCOPUS.

Research Subject Selection

As a doctoral student, subject selection is a big problem. Phdservices.org has the team of world class experts who experience in assisting all subjects. When you decide to work in networking, we assign our experts in your specific area for assistance.

Research Topic Selection

We helping you with right and perfect topic selection, which sound interesting to the other fellows of your committee. For e.g. if your interest in networking, the research topic is VANET / MANET / any other

Literature Survey Writing

To ensure the novelty of research, we find research gaps in 50+ latest benchmark papers (IEEE, Springer, Elsevier, MDPI, Hindawi, etc.)

Case Study Writing

After literature survey, we get the main issue/problem that your research topic will aim to resolve and elegant writing support to identify relevance of the issue.

Based on the research gaps finding and importance of your research, we conclude the appropriate and specific problem statement.

Writing Research Proposal

Writing a good research proposal has need of lot of time. We only span a few to cover all major aspects (reference papers collection, deficiency finding, drawing system architecture, highlights novelty)

MILESTONE 2: System Development

Fix implementation plan.

We prepare a clear project implementation plan that narrates your proposal in step-by step and it contains Software and OS specification. We recommend you very suitable tools/software that fit for your concept.

Tools/Plan Approval

We get the approval for implementation tool, software, programing language and finally implementation plan to start development process.

Pseudocode Description

Our source code is original since we write the code after pseudocodes, algorithm writing and mathematical equation derivations.

Develop Proposal Idea

We implement our novel idea in step-by-step process that given in implementation plan. We can help scholars in implementation.

Comparison/Experiments

We perform the comparison between proposed and existing schemes in both quantitative and qualitative manner since it is most crucial part of any journal paper.

Graphs, Results, Analysis Table

We evaluate and analyze the project results by plotting graphs, numerical results computation, and broader discussion of quantitative results in table.

Project Deliverables

For every project order, we deliver the following: reference papers, source codes screenshots, project video, installation and running procedures.

MILESTONE 3: Paper Writing

Choosing right format.

We intend to write a paper in customized layout. If you are interesting in any specific journal, we ready to support you. Otherwise we prepare in IEEE transaction level.

Collecting Reliable Resources

Before paper writing, we collect reliable resources such as 50+ journal papers, magazines, news, encyclopedia (books), benchmark datasets, and online resources.

Writing Rough Draft

We create an outline of a paper at first and then writing under each heading and sub-headings. It consists of novel idea and resources

Proofreading & Formatting

We must proofread and formatting a paper to fix typesetting errors, and avoiding misspelled words, misplaced punctuation marks, and so on

Native English Writing

We check the communication of a paper by rewriting with native English writers who accomplish their English literature in University of Oxford.

Scrutinizing Paper Quality

We examine the paper quality by top-experts who can easily fix the issues in journal paper writing and also confirm the level of journal paper (SCI, Scopus or Normal).

Plagiarism Checking

We at phdservices.org is 100% guarantee for original journal paper writing. We never use previously published works.

MILESTONE 4: Paper Publication

Finding apt journal.

We play crucial role in this step since this is very important for scholar’s future. Our experts will help you in choosing high Impact Factor (SJR) journals for publishing.

Lay Paper to Submit

We organize your paper for journal submission, which covers the preparation of Authors Biography, Cover Letter, Highlights of Novelty, and Suggested Reviewers.

Paper Submission

We upload paper with submit all prerequisites that are required in journal. We completely remove frustration in paper publishing.

Paper Status Tracking

We track your paper status and answering the questions raise before review process and also we giving you frequent updates for your paper received from journal.

Revising Paper Precisely

When we receive decision for revising paper, we get ready to prepare the point-point response to address all reviewers query and resubmit it to catch final acceptance.

Get Accept & e-Proofing

We receive final mail for acceptance confirmation letter and editors send e-proofing and licensing to ensure the originality.

Publishing Paper

Paper published in online and we inform you with paper title, authors information, journal name volume, issue number, page number, and DOI link

MILESTONE 5: Thesis Writing

Identifying university format.

We pay special attention for your thesis writing and our 100+ thesis writers are proficient and clear in writing thesis for all university formats.

Gathering Adequate Resources

We collect primary and adequate resources for writing well-structured thesis using published research articles, 150+ reputed reference papers, writing plan, and so on.

Writing Thesis (Preliminary)

We write thesis in chapter-by-chapter without any empirical mistakes and we completely provide plagiarism-free thesis.

Skimming & Reading

Skimming involve reading the thesis and looking abstract, conclusions, sections, & sub-sections, paragraphs, sentences & words and writing thesis chorological order of papers.

Fixing Crosscutting Issues

This step is tricky when write thesis by amateurs. Proofreading and formatting is made by our world class thesis writers who avoid verbose, and brainstorming for significant writing.

Organize Thesis Chapters

We organize thesis chapters by completing the following: elaborate chapter, structuring chapters, flow of writing, citations correction, etc.

Writing Thesis (Final Version)

We attention to details of importance of thesis contribution, well-illustrated literature review, sharp and broad results and discussion and relevant applications study.

How PhDservices.org deal with significant issues ?

1. novel ideas.

Novelty is essential for a PhD degree. Our experts are bringing quality of being novel ideas in the particular research area. It can be only determined by after thorough literature search (state-of-the-art works published in IEEE, Springer, Elsevier, ACM, ScienceDirect, Inderscience, and so on). SCI and SCOPUS journals reviewers and editors will always demand “Novelty” for each publishing work. Our experts have in-depth knowledge in all major and sub-research fields to introduce New Methods and Ideas. MAKING NOVEL IDEAS IS THE ONLY WAY OF WINNING PHD.

2. Plagiarism-Free

To improve the quality and originality of works, we are strictly avoiding plagiarism since plagiarism is not allowed and acceptable for any type journals (SCI, SCI-E, or Scopus) in editorial and reviewer point of view. We have software named as “Anti-Plagiarism Software” that examines the similarity score for documents with good accuracy. We consist of various plagiarism tools like Viper, Turnitin, Students and scholars can get your work in Zero Tolerance to Plagiarism. DONT WORRY ABOUT PHD, WE WILL TAKE CARE OF EVERYTHING.

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• Technical Info: We never share your technical details to any other scholar since we know the importance of time and resources that are giving us by scholars.
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CONFIDENTIALITY AND PRIVACY OF INFORMATION HELD IS OF VITAL IMPORTANCE AT PHDSERVICES.ORG. WE HONEST FOR ALL CUSTOMERS.

4. Publication

Most of the PhD consultancy services will end their services in Paper Writing, but our PhDservices.org is different from others by giving guarantee for both paper writing and publication in reputed journals. With our 18+ year of experience in delivering PhD services, we meet all requirements of journals (reviewers, editors, and editor-in-chief) for rapid publications. From the beginning of paper writing, we lay our smart works. PUBLICATION IS A ROOT FOR PHD DEGREE. WE LIKE A FRUIT FOR GIVING SWEET FEELING FOR ALL SCHOLARS.

5. No Duplication

After completion of your work, it does not available in our library i.e. we erased after completion of your PhD work so we avoid of giving duplicate contents for scholars. This step makes our experts to bringing new ideas, applications, methodologies and algorithms. Our work is more standard, quality and universal. Everything we make it as a new for all scholars. INNOVATION IS THE ABILITY TO SEE THE ORIGINALITY. EXPLORATION IS OUR ENGINE THAT DRIVES INNOVATION SO LET’S ALL GO EXPLORING.

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Artificial Intelligence and Data Science Group

Featured story.

Suggested PhD Projects

Here are some suggested topics for PhD projects from our group members. These projects are merely suggestions and illustrate the broad interests of the research group. Potential research students are encouraged and welcome to produce their own suggestions in these research areas or other research areas in the field. All applicants are invited to contact the academic associated with the project when making an application.

Machine Learning for the Pharmacology of Ageing

Contact:  alex freitas.

Recently, there has been a growing interest in ageing research, since the proportion of elderly people in the world’s population is expected to increase substantially in the next few decades. As people live longer, it becomes increasingly more common for a person to suffer from multiple age-related diseases. Since old age is the ultimate cause or the greatest risk factor for most of these diseases, progress in ageing research has the potential to lead to a more cost-effective treatment of many age-related diseases in a holistic fashion. In this context, researchers have collected a significant amount of data about ageing-related genes and medical drugs affecting an organism’s longevity – mainly about simpler model organisms, rather than humans. This data is often freely available on the web, which has facilitated the application of machine learning methods to the pharmacology or biomedicine of ageing, to try to discover some knowledge or patterns in such datasets. This project will focus on developing machine learning algorithms for analysing data about the pharmacology of ageing, i.e., data about medical drugs or chemical compounds that can be used as an intervention against ageing, mainly in model organisms. The broad type of machine learning method to be developed will be supervised machine learning (mainly classification), but the specific type of algorithm to be developed will be decided later, depending on the student’s interest and suitability to the target datasets. Note that, although this is an interdisciplinary project, this is a project for a PhD in Computer Science, so the student will be expected to develop a novel machine learning method. As examples of interdisciplinary papers on machine learning for ageing research, see e.g. (the first paper is particularly relevant for this project, whilst the second includes a broader discussion about machine learning for ageing research):

Relevant References:

D.G. Barardo, D. Newby, D. Thornton, T. Ghafourian, J.P. de Magalhaes and A.A. Freitas. Machine learning for predicting lifespan-extending chemical compounds. Aging (Albany NY), 9(7), 1721-1737, 2017.

Fabris, J.P. de Magalhaes, A.A. Freitas. A review of supervised machine learning applied to ageing research. Biogerontology, 18(2), 171-188, April 2017.

Machine Learning with Fairness-Aware Classification Algorithms

This project involves the classification task of machine learning, where an algorithm has to predict the class of an object (e.g. a customer or a patient) based on properties of that object (e.g. characteristics of a customer or patient). There are now many types of classification algorithms, and in general these algorithms were designed with the only (or main) goal of maximizing predictive performance. As a result, the application of such algorithms to real-world data about people often leads to predictions which have a good predictive accuracy but are unfair, in the sense of discriminating (being biased) against certain groups or types of people – characterized e.g. by values of attributes like gender or ethnicity. In the last few years, however, there has been a considerable amount of research on fairness-aware classification algorithms, which take into account the trade-off between achieving a high predictive accuracy and a high degree of fairness. The project will develop new classification algorithms to cope with this trade-off, focusing on classification algorithms that produce interpretable predictive models, rather than black box models.

[1] Friedler, A.A., Scheidegger, C., Venkatasubramanian, S., Choudhary, S., Hamilton, E.P. and Roth, D. A comparative study of fairness-enhancing interventions in machine learning. Proc. 2nd ACM Conf. on Fairness, Accountability and Transparency (FAT’19), 329-338. ACM Press, 2019.

[2] Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. A survey on bias and fairness in machine learning. arXiv preprint: arXiv:1908.09635. 2019.

Cognition-enabled lifelong robot learning of behavioural and linguistic experience

Contact:  ioanna giorgi.

Present-day cognitive robotics models draw on a hypothesised developmental paradigm of human cognitive functions to devise low-order skills in robots, such as perception, manipulation, navigation and motor coordination. These methods exploit embodied and situated cognition theories that are rooted in motor behaviour and the environment. In other words, the body of a physical artefact (e.g., a robot) and its interactions with the environment and other organisms in it contribute to the robot’s cognition. However, it is not clear how these models can explain or scale up to the high-level cognitive competence observed in human behaviour (e.g., reasoning, categorisation, abstraction and voluntary control). One approach to model robot learning of behavioural and cognitive skills is in incremental and developmental stages that resemble child development. According to child psychology and behaviour, conceptual development starts from perceptual clustering (e.g., prelinguistic infants grouping objects by colour) and progresses to nontrivial abstract thinking, which requires a fair amount of language . Thus, to solve the problem of modelling high-level cognitive skills in robots, language, in interaction with the robot’s body, becomes inseparable from cognition. This project is aimed at following a cognitive and developmental approach to robot learning that will allow robots to acquire behavioural and linguistic skills at a high level of cognitive competence and adaptation as humans. This learning should be lifelong : humans apply earlier-learned skills to make sense of continuous novel stimuli, which allows them to develop, grow and adjust to more complex practices. One such cognitive robot can be used across various themes: human-robot interaction using theory of mind (ToM) skills for robots, social robots and joint human-robot collaboration.

Note: The Cognitive Robotics and Autonomous Systems (CoRAS) laboratory at the School of Computing has access to several humanoids (NAO) and socially interactive robot platforms (Buddy Pro, Q.BO One, Amy A1), mobile robots (Turtlebot Waffle Pi, Burger), pet-like companion robots and gadgets like AR Epson glasses and Microsoft HoloLens.

Attention model for agent social learning during human-robot interaction.

Successful human-robot interaction requires that robots learn by observing and imitating human behaviour. The theory of learning behaviour through observation is referred to as social learning . Behavioural learning can also be enhanced by the environment itself and through reinforcement (i.e., establishing and encouraging a pattern of behaviour). One important component of such learning is cognitive attention , which deals with the degree to which we notice a behaviour. Cognitive attention renders some inputs more relevant while diminishing others, with the motivation that more focus is needed for the important stimuli in the context of social learning. Attention brings forth positive reinforcement (reward) or negative reinforcement (punishment). If the reward is greater than the punishment, behaviour is more likely to be imitated and reciprocated. In human-robot interaction, attention is crucial for two reasons: 1) to respond or reciprocate the behaviour appropriately during the interaction, and 2) to learn or imitate that behaviour for contingencies. This project is aimed at devising a cognitive attention model of a robot for social learning. The model will include memory, reasoning, language and multi-sensory data processing, i.e., “natural” stimuli during the interaction such as from vision, speech and sensorimotor experience. It can be based on a cognitive architecture approach or alternative computational approaches. The solution should ideally be encompassing multiple aspects of interaction (verbal and non-verbal), but it can also focus on such specific aspects (e.g., visual attention or intention reading).

How can a robot learn skills from a human tutor

Contact:  giovanni masala.

The aim of this project is to enhance robot learning from a human tutor, similar to a child who learns from a human teacher. The agent will develop the ability to communicate through natural language from scratch, by interacting with a tutor, recognising their verbal and non-verbal inputs as well as emotions, and, finally, grounding the word meaning in the external environment. The project will start from an existing neuro-cognitive architecture under development [1], based on a Human-like approach to learning, progressively incrementing knowledge and language capabilities through experience and ample exposure, using a corpus based on early language lexicons (preschool literature). The robot will integrate with visuospatial information-processing mechanisms for embodied language acquisition, exploiting affective mechanisms of emotion detection for learning and cognition. The agent will be embodied into a humanoid robot as opposed to a computer or a virtual assistant, to enable real-world interactions with the humans and the external environment, to learn and refine its natural language understanding abilities guided or depending on the teacher’s emotions and visual input (object associations with the words, facial expression, and gestures). Emotions will influence the cognitive attention of the robotic agent, modulating the selectivity of attention on specific tasks, words, and objects, and motivating actions and behaviour.

[1] Golosio B, Cangelosi A, Gamotina O, MASALA GL, A Cognitive Neural Architecture Able to Learn and Communicate through Natural Language. PLoS ONE 10(11): e0140866, 2015.

Note: The Cognitive Robotics and Autonomous Systems (CoRAS) laboratory at the School of Computing has access to several humanoids (NAO) and socially interactive robot platforms (Buddy Pro, Q.BO One, Amy A1), mobile robots (Turtlebot Waffle Pi, Burger), pet-like companion robots and devices like AR Epson glasses and Microsoft HoloLens.

Explainability and Interpretability of Machine/Deep learning techniques in medical imaging

In medicine is very important the acceptance of Machine Learning systems not only in terms of performance but also considering the degree to which a human can understand the cause of a decision. Nowadays, the application of Computer Aided Detection Systems in radiology is often based on Deep Learning Systems thanks to their high performance. In general, more accurate models are less explainable  and there is a scientific interest in the field of Explainable Artificial Intelligence, to develop new methods that explain and interpret ML models. There is not a concrete mathematical definition for interpretability or explainability, nor have they been measured by some metric; however, a number of attempts have been made in order to clarify not only these two terms but also related concepts such as comprehensibility. A possible target (but other medical diseases are allowed) of this research is a model to discover the severity of Breast Arterial Calcifications. Breast arterial calcification (BAC) is calcium deposition in peripheral arterioles. There is increasing evidence that BAC is a good indicator of the risk of cardiovascular disease. The accurate and automated detection of BACs in mammograms remains an unsolved task and the technology is far from clinical deployment. The challenging task is to develop an explainable model applicable to BAC detection, able to discriminate between severe and weak BACs in patients’ images.

Autonomous car makes me sick

Contact:  palaniappan ramaswamy.

With the rapid advancements in autonomous car technology, we will soon see cars driving on their own on the roads. While some may dread this lack of control in fear of safety, generally it is much safe and the real issue lies elsewhere. Do you know that many of us will feel sick – motion sickness will become a huge problem and there is not much ongoing work to mitigate this situation.  In this project, we will explore using transcutaneous auricular vagus nerve stimulation (taVNS) as an intervention technology. VNS is a medically approved technology for conditions such as epilepsy. But here we will study the non-invasive version of VNS in mitigating the effects of motion sickness. Functional near infra-red spectroscopy (fNIRS) will be utilised to assess the effect of the taVNS on motion sickness. Some prior signal processing knowledge will be required but knowledge on VNS and fNIRS can be gained from the project. 

Stress management

The fundamental aspect of human experience is awareness. Combined with the ability to think, imagine and understand it results into the beautiful cosmic play we experience. However, with it comes along a multitude of problems, often illusory in nature – such as stress, anxiety, anger, negativity, etc. It isn’t hard to guess that in such states our behaviour is significantly altered, usually in harmful ways for both – us and the environment. There are techniques such as meditation, music, humour which can help us come back to our “real” senses and feel happier/peaceful again. So the fundamental enquiry would be about what sort of things do help us achieve a happier state, and moreover what’s their impact on both short term and long term brain functioning. This project will study this aim using EEG.

Information Visualisation Directed by Graph Data Mining

Contact:  peter rodgers.

Data visualisation techniques are failing in the face of large data sets. This project attempts to increase the scale of graph data that can be visualised by developing data mining techniques to guide interactive visualisation. This sophisticated combining of information visualisation and data mining promises to greatly improve the size of data understandable by analysts, and will advance the state of the art in both disciplines. On successful completion, publications in high quality venues are envisaged. This project is algorithmically demanding, requiring good coding skills. The implementation language is negotiable, but Java, JavaScript or C++ are all reasonable target languages. Data will be derived from publicly available network intrusion or social network data sets. Tasks in this research project include: (1) implementing graph display software and interface. (2) developing project specific visualisation algorithms. (3) integrating graph pattern matching and other graph data mining systems into the visualisation algorithms.

Visual Analytics for Set Data

Visual Analytics is the process of gaining insights into data through combining AI and information visualization. At present, visual analytics for set based data is largely absent. There are a large number of sources for set based data, including social networks as well as medical and biological information. This project will look at producing set mining algorithms which can then be used to support set visualization methods such as Euler/Venn diagrams or Linear diagrams. Firstly, the use of existing data mining methods will produce useful information about sets and the data instances in them. After this effort, more complex algorithms for subset and set isomorphism will be developed to allow for pattern matching within set data. These set mining methods will be integrated into Euler diagram based exploratory set visualization techniques.

Using Soft Nanomembrane Electronics for Home-based Anxiety Monitoring

Contact:  jim ang.

Sensor-enhanced virtual reality systems for mental health care and rehabilitation. New immersive technologies, such as  virtual reality (VR) and augmented reality (AR) are playing an increasingly important role in the digital health revolution. Significant research has been carried out at University of Kent, in collaboration with medical scientists/practitioners, psychiatrists/psychologists, digital artists and material scientists (for novel sensor design and integration with VR). Such projects include designing VR for dementia care, eating disorder therapy, eye disorder therapy and VR-enabled brain-machine interactions. This PhD research can take on the following directions: (1) Co-design of VR for a specific healthcare domains, involving key stakeholders (e.g. patient representatives, clinicians, etc) to  understand the design and deployment opportunities and challenges in realistic health contexts. (2) Deploy and evaluate VR prototypes to study the impact of the technologies in the target groups. (3) Design and evaluate machine learning algorithms to analyse behavioural and physiological signals for clinical meaningful information, e.g. classification of emotion, detection of eye movement, etc. 

Relevant publications: 

[1] M Mahmood, S Kwon, H Kim, Y Kim, P Siriaraya, J Choi, B Otkhmezuri, K Kang, KJ Yu, YC Jang, CS Ang, W Yeo (2021) Wireless Soft Scalp Electronics and Virtual Reality System for Motor Imagery ‐ Based Brain–Machine Interfaces. Advanced Science. 8(19). 

[2] S Mishra, K Yu, Y Kim, Y Lee, M Mahmood, R Herbert, CS Ang, W Yeo, J Intarasirisawat, Y Kown, H Lim (2020). Soft, wireless periocular wearable electronics for real-time detection of eye vergence in a virtual reality toward mobile eye therapies. Science Advances. 6 (11), eaay1729. 

[3] L Tabbaa, CS Ang, V Rose, P Siriaraya, I Stewart, KG Jenkins, M Matsangidou (2019) Bring the Outside In: Providing Accessible Experiences Through VR for People with Dementia in Locked Psychiatric Hospitals, Proceedings of the CHI 2019 Conference on Human Factors in Computing Systems. 

[4] M Matsangidou, B Otkhmezuri, CS Ang, M Avraamides, G Riva, A Gaggioli, D Iosif, M Karekla (2020). “Now I can see me” designing a multi-user virtual reality remote psychotherapy for body weight and shape concerns. Human–Computer Interaction. 1-27.

Optimisation of Queries over Virtual Knowledge Graphs

Contact:  elena botoeva.

Virtual Knowledge Graphs (also known as Ontology-Based Data Access) provide user-friendly access to Big Data stored in (possibly multiple) data sources, which can be traditional relational ones or more novel ones such as document and triple stores. In this framework an ontology is used as a conceptual representation of the data, and is connected to the data sources by the means of a mapping. User formulates queries over the ontology using a high-level query language like SPARQL; user queries are then automatically translated into queries over the underlying data sources, and the latter are executed by the database engines. Efficiency of the whole approach is highly dependent on optimality of the data source queries. While the technology is quite developed when the underlying data sources are relational, there are still many open problems when it comes to novel data sources, such as MongoDB, graph databases etc. The objective of this PhD project is to design novel techniques for optimising data source queries arising in the context of Virtual Knowledge Graphs.

Heuristics for Scalable Verification of Neural Networks

Due to the success of Deep Learning neural networks are now being employed in a variety of safety-critical applications such as autonomous driving cars and aircraft landing. Despite showing impressive results at various tasks, neural networks are known to be vulnerable (hence, not robust) to adversarial attacks: imperceptible to human eye perturbations to an input can lead to incorrect classification. Robustness verification of neural networks is currently a very hot topic both in academia and industry as neural networks. One of the main challenges in this field is deriving efficient techniques that can verify networks with hundred thousands / millions of neurons in reasonable time, which is not trivial given that exact verification is not tractable (NP- or coNP-complete for ReLU-based neural networks depending on the exact verification problem). Incomplete approaches generally offer better scalability but at the cost of completeness. The aim of the proposed PhD project will be to learn heuristics for efficient verification of neural networks.

Understanding Spiking Neural Networks

Contact:  dominique chu.

Spiking Neural Networks (SNN) are brain-like neural networks. Unlike standard rate coding neural networks, signals are encoded in time. This makes them ideal for processing data that has a temporal component, such as time-series data, video or music. Another advantage of SNNs is that there exists neuromorphic hardware that can efficiently simulate SNNs. SNNs are generally thought to be “more powerful” than standard rate coding networks. However, it is not clear precisely in what sense they are more powerful, or what precisely it is that makes them more powerful. The idea of this project is to investigate this claim using a combination of mathematical and computational methods. As such the project will require an interdisciplinary research methodology at the interface between mathematics, computer science and neuroscience. The project would be suitable for a student who wishes to become and expert in an up-and-coming method in artificial intelligence. It has the scope for both theoretical investigations, but will also require implementing neural networks.

Training algorithms for spiking neural networks

Spiking neural networks encode information through the temporal order of the signals. They are more realistic models of the brain than standard artificial neural networks and they are also more efficient in encoding information. Spiking neural networks are therefore very popular in brain simulations. A disadvantage of spiking neural networks is that there are not many efficient training algorithms available. This project will be about finding novel training algorithms for spiking neural networks and to compare the trained networks with standard artificial neural networks on a number of benchmark AI tasks. An important part of this project will be not only to evaluate how well these spiking neural networks perform in relation to standard networks, but also to understand whether or not they are, as is often claimed, more efficient in the sense that they need smaller networks or fewer computing resources. The main approach of the model will be to gain inspiration from existing theories about how the how the human brain develops and learns. These existing theories will then be adapted so as to develop efficient training algorithms. This project will be primarily within AI, but it will also provide the opportunity to learn and apply techniques and ideas from computational neuroscience.

Machine learning systems to improve medical diagnosis

Contact:  daniel soria.

Research shows that machine learning methods are extremely useful to discover or identify patterns that can help clinicians to tailor treatments. However, the implementation of those data mining procedures may be challenging because of high dimensional data sets, and the choice of proper machine learning methods may be tricky. 

The aim of the research project will be to design and develop new intelligent machine learning systems with high degree of flexibility suitable for disease prediction/diagnosis, that are also easily understandable and explicable to non-experts in the field. Data will be sought from the UK Biobank, to examine whether the selected features are correlated with the occurrence of specific diseases (e.g., breast cancer), whether these relationships persist in the presence of covariates, and the potential role of comorbidities (e.g., obesity, diabetes and cardiovascular diseases) in the assessment of the developed models

How creative are crime-related texts and what does this tell us about cyber crime?

Contact:  shujun li ,   anna jordanous.

The main aim of the PhD project is to investigate if crime-related texts can be evaluated in terms of creativity using automatic metrics. Such a study will help understand how crime-related texts are crafted (by criminals and by automated tools, possibly via a hybrid human-machine teaming approach), how they have evolved over time, how they are perceived by human receivers, and how new methods can be developed to educate people about tactics of cyber criminals. The four tasks of the PhD project will include the following: (1) collecting a large datasets of crime-related texts; (2) developing some objective (automatable) creativity metrics using supervised machine learning, targeted towards evaluating the creativity of crime-related texts (e.g., phishing emails, online hate speech, grooming, cyber bullying, etc.); (3) applying the creativity metrics to the collected data to see how malevolent creativity has evolved over years and for different crimes; (4) exploring the use of generative AI algorithms to create more creative therefore more deceptive crime-related texts.

Computational creativity and automated evaluation

Contact:  anna jordanous.

In exploring how computers can perform creative tasks, computational creativity research has produced many systems that can generate creative products or creative activity. Evaluation, a critical part of the creative process, has not been employed to such a great extent within creative systems. Recent work has concentrated on evaluating the creativity of such computational systems, but there are two issues. Firstly, recent work in evaluation of computational creativity has consisted of the system(s) being evaluated by external evaluators, rather than by the creative system evaluating itself, or evaluation by other creative software agents that may interact with that system. Incorporation of self-evaluation into computational creativity systems *as part of guiding the creative process* is also under explored. In this project the candidate will experiment with incorporating evaluation methods into a creative system and analyse the results to explore how computational creativity systems can incorporate self-evaluation. The creative systems studied could be in the area of musical or linguistic creativity, or in a creative area of the student’s choosing. It is up to the student to decide whether to focus on evaluation methods for evaluating the quality of output from a creative system or the creativity of the system itself (or both). The PhD candidate would be required to propose how they would will explore the above scenarios, for a more specific project. Anna is happy to guide students in this and help them develop their research proposal.

Expressive musical performance software

Traditionally, when computational software performs music the performances can be criticised for being too unnatural, lacking interpretation and, in short, being too mechanical. However much progress has been made within the field of expressive musical performance and musical interpretation expression. Alongside these advances have been interesting findings in musical expectation (i.e. what people expect to hear when listening to a piece of music), as well as work on emotions that are present within music and on how information and meaning are conveyed in music. Each of these advances raises questions of how the relevant aspects could be interpreted by a musical performer. Potential application areas for computer systems that can perform music in an appropriately expressive manner include, for example, improving playback in music notation editors (like Sibelius), or the automated performance of music generated on-the-fly for ‘hold’ music (played when waiting on hold during phone calls). Practical work exploring this could involve writing software that performs existing pieces, or could be to write software that can improvise, interpreting incoming sound/music and generating an appropriate sonic/musical response to it in real time.

Brain-like Computer  

Contact:  frank wang.

The human brain consists of about one billion neurons. Each neuron forms about 1,000 connections to other neurons, amounting to more than a trillion connections. If each neuron could only help store a single memory, running out of space would be a problem. You might have only a few gigabytes of storage space, similar to the space in an iPod or a USB flash drive. Yet neurons combine so that each one helps with many memories at a time, exponentially increasing the brain’s memory storage capacity to something closer to around 2.5 petabytes (or a million gigabytes). The way our brain organizes data may help us manage continuously increasing data, especially in Cloud computing and Big Data. In this project, you are expected to simulate a brain-like computer. Such a computer should be categorised into the unconventional computer group, which is different from traditional Turing machine (with stored programmes) or Von Neumann computer (with an operating system).

My relevant papers: Adaptive Neuromorphic Architecture , Memristor Neural Networks , Grid-Oriented Storage (IEEE Transactions on Computers) .

My relevant keynote talk at Cambridge: Brain and Brain-Inspired Artificial Intelligence )

New Quantum Computer

Contact: frank wang.

Most recently, Frank Wang published an article on Quantum Information Processing (Springer Nature) to report a new quantum computer that can break Landauer’s Bound. Among a number of physical limits to computation, Landauer’s bound limits the minimum amount of energy for a computer to process a bit of information. In light of this study, we may have to presume the demise of this bound despite the many mysteries uncovered with it over the past 60 years.

My relevant papers: Breaking Landauer’s bound in a spin-encoded quantum computer (Springer Nature) , Can We Break the Landauer Bound in Spin-Based Electronics (IEEE Access) .

My relevant keynote talk at Cambridge: A New Quantum Computer Not Bound By Landauer’s Bound )

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• School of Electronic Engineering and Computer Science
• PhD studentships

DeepMind PhD Studentship in AI or Machine Learning

About the Studentship

Queen Mary University of London is inviting applications for the DeepMind PhD Studentship for September 2023. 

The DeepMind PhD Studentship programme is established at Queen Mary University of London in partnership with leading British AI company, DeepMind. 

The PhD Studentship supports and encourages under-represented groups, namely female and Black researchers, to pursue postgraduate research in AI or Machine Learning. 

The PhD DeepMind Studentship will cover tuition fees and offer a London weighted stipend of £19,668 per year minimum together with an annual £2,200 travel and conference allowance and a one-off equipment grant of £1,700.

• 3-year fully-funded PhD Studentship
• Access to cutting-edge facilities and expertise in AI
• Partnership and mentorship with DeepMind employees working at the cutting edge of AI research and technologies.

Who can apply Queen Mary is on the lookout for the best and brightest students in the fields of AI and Machine Learning. 

Successful applicants will have the following profile:

• Identify as female and/or are of Black ethnicity, each being under-represented groups in the field of Artificial Intelligence and Computer Science
• Should hold, or is expected to obtain an MSc in Computer Science, Electronic Engineering, AI, Physics or Mathematics or a closely related discipline; or can demonstrate evidence of equivalent work experience
• Having obtained distinction or first-class level degree is highly desirable
• Programming skills are strongly desirable; however, we do not consider this to be an essential criterion if candidates have complementary strengths. 

We actively encourage applications from candidates who are ordinarily resident in the UK. The studentship is also open to International applicants. 

About the School of Electronic Engineering and Computer Science at Queen Mary

The PhD Studentship will be based in the School of Electronic Engineering and Computer Science (EECS) at Queen Mary University of London. As a multidisciplinary School, we are well known for our pioneering research and pride ourselves on our world-class projects. We are 8th in the UK for computer science research (REF 2021) and 7th in the UK for engineering research (REF 2021). The School is a dynamic community of approximately 350 PhD students and 80 research assistants working on research centred around a number of research groups in several areas, including Antennas and Electromagnetics, Computing and Data Science, Communication Systems, Computer Vision, Cognitive Science,  Digital Music, Games and AI, Multimedia and Vision, Networks, Risk and Information Management, Robotics and Theory

For further information about research in the school of Electronic Engineering and Computer Science, please visit: http://eecs.qmul.ac.uk/research/ .

How to apply

Queen Mary is interested in developing the next generation of outstanding researchers - whether in academia, industry or government – therefore the project undertaken under this Studentship is expected to fit into the wider research programme of School. Applicants should select a supervisor (a first and second choice) from the School at application stage. Visit our website for information about our research groups and supervisors:  eecs.qmul.ac.uk/phd/phd-opportunities/

Applicants should submit their interest by returning the following to  [email protected] by 12pm (noon), 10 April 2023:

• Indicate first and second choice academic supervisor 
• CV (max 2 pages) 
• Cover letter (max 4,500 characters)
• Research proposal (max 500 words) 
• 2 References 
• Certificate of English Language (for students whose first language is not English) 
• Other Certificates  

Application deadline: 10 April 2023

Applications will be reviewed by a panel of academic staff: May 2023

Interviews:  April/May 2023

Start date:  September 2023