ac 297r cse capstone research project course

 Wednesdays @ 12:45pm - 3:00pm SEC LL2.223 (Allston Campus)

Capstone research project course, ac297r, fall 2022 weiwei pan, founded by the institute for applied computational science (iacs)'s  scientific program director,  pavlos protopapas , the capstone research course is a group-based research experience where students work directly with a partner from industry, government, academia, or an ngo to solve a real-world data science/ computation problem. students will create a solution in the form of a software package, which will require varying levels of research. upon completion of this challenging project, students will be better equipped to conduct research and enter the professional world. every class session includes a guest lecture concerning various essential skills for one's career -- from public speaking, reading and writing research papers, how to work remotely on a team, everything about start-ups, and more..

The Ohio State University

• BuckeyeLink
• Find People
• Search Ohio State

Capstone Design: Research-Focused Projects

Description / conditions .a,.b{fill:#c2c2c2;}.b{opacity:0;} single-arrow-square, course detail .a,.b{fill:#c2c2c2;}.b{opacity:0;} single-arrow-square, course goals and learning objectives .a,.b{fill:#c2c2c2;}.b{opacity:0;} single-arrow-square, topics and contact hours .a,.b{fill:#c2c2c2;}.b{opacity:0;} single-arrow-square, grading and texts .a,.b{fill:#c2c2c2;}.b{opacity:0;} single-arrow-square, abet student learning outcomes .a,.b{fill:#c2c2c2;}.b{opacity:0;} single-arrow-square, embedded literacies (ug courses only) .a,.b{fill:#c2c2c2;}.b{opacity:0;} single-arrow-square, attachments / additional notes or comments .a,.b{fill:#c2c2c2;}.b{opacity:0;} single-arrow-square.

• Data Science
• Introduction

Harvard Griffin GSAS strives to provide students with timely, accurate, and clear information. If you need help understanding a specific policy, please contact the office that administers that policy.

• Application for Degree
• Credit for Completed Graduate Work
• Ad Hoc Degree Programs
• Dissertations
• English Language Proficiency
• PhD Program Requirements
• African and African American Studies
• American Studies
• Ancient Studies
• Anthropology
• Archaeology
• Celtic Medieval Languages and Literatures
• Comparative Literature
• Computational Science and Engineering
• Critical Media Practice
• Film and Visual Studies
• Historical Linguistics
• History of Science
• Latinx Studies
• Linguistic Theory
• Medieval Studies
• Mind, Brain, and Behavior
• Romance Languages and Literatures (French, Italian, Portuguese, or Spanish)
• Science, Technology, and Society
• Slavic Literary/Cultural Studies
• Studies of Women, Gender, and Sexuality
• Translation Studies
• Year of Graduate Study (G-Year)
• Master's Degrees
• Grade and Examination Requirements
• Conduct and Safety
• Financial Aid
• Non-Resident Students
• Registration

The Data Science secondary field is available to any student enrolled in a PhD program in the Harvard Kenneth C. Griffin Graduate School of Arts and Sciences upon approval of a plan of study by the Data Science Program Committee and the director of graduate studies in the student’s home department.

Data Science lies at the intersection of statistical methodology, computational science, and a wide range of application domains. This secondary field offers strong preparation in statistical modeling, machine learning, optimization, management and analysis of massive data sets, and data acquisition. Students completing the Data Science secondary field will be exposed to topics such as reproducible data analysis, collaborative problem solving, visualization and communication, and security and ethical issues that arise in data science.

The Data Science secondary field is overseen by the joint leadership of the Computer Science and Statistics faculties and administered by the Institute for Applied Computational Science (IACS). All questions should be directed to Daniel Weinstock , associate director of graduate studies (ADGS) in Applied Computation.

Interested students should consult with their director of graduate studies no later than the first semester of the third year of study and reach out to the ADGS to express interest in applying. The ADGS will provide information about the application, which should include a proposed plan of study.

Applications, which must be approved by the home department DGS, may be submitted twice a year, in the spring semester (deadline: March 1) and fall semester (deadline: October 1) for the following academic term. The ADGS will respond to all applications within one month.

Requirements

Each student’s plan of study for the secondary field will include:

1. Core Courses

At least 3 of the Data Science core courses:

• AC 209a*        Data Science 1: Introduction to Data Science
• AC 209b*         Data Science 2: Advanced Topics in Data Science
• AM 207           Advanced Scientific Computing: Stochastic Methods for    Data Analysis, Inference, and Optimization
• CS 207            Systems Development for Computational Science
• AC 221            Critical Thinking in Data Science

*Students can, with the permission of the program committee, count CS 109a/b in place of AC 209a/b.

2. Electives

Two electives in Computer Science or Statistics. Students may choose from a offered by the Computer Science and Statistics faculties.

Alternatively, students may choose to satisfy the elective requirement by taking additional core courses. Students may also choose, as a substitute for one elective, either AC 297r, the IACS Capstone Project course, or AC298r, the interdisciplinary seminar in Computational and Data Science.

3. Oral Examination

As a final requirement, an oral examination by a faculty committee on a data science research topic. Typically students will present on a part of their dissertation thesis work. Students will be evaluated on their ability to explain their work to the interdisciplinary IACS audience and their command of the Data Science methods they have used. The oral presentation should explain how the courses taken to satisfy the Data Science secondary field impact their research.

Advising and Other Activities

Daniel Weinstock, ADGS in Applied Computation, will be responsible for frontline advising of students in the Data Science secondary field. Students interested in the secondary field are encouraged to reach out to Dr. Weinstock before submitting an application. Students enrolled in the secondary field will be able to participate in the activities of the IACS community, including technical and interdisciplinary colloquia, skill-building workshops, and tech-treks to local companies working to apply computation and data science in many different domains.

Explore Events

• Utility Menu

Guide to the ALM Capstone Project

Customstyles.

• Course Catalog

Data Science Capstone

This capstone course is the culmination of the Master of Liberal Arts, data science, where students execute their research proposal from  CSCI S-597 . It  gives students the opportunity to collaborate on a complex research topic using their data science skills.  At the completion of the capstone, students are able to demonstrate their ability to think critically about data, communicate with diverse audiences, and advance innovation in ways that benefit society.

Capstone Proposal Tutorial and Capstone Sequencing

The semester prior to capstone enrollment (no earlier), you register for the on-campus precapstone: CSCI E-597 Data Science Precapstone . Ordinarily the on-campus precapstone tutorial is offered during the three-week January session and one, three-week summer session.

The Precapstone prepares students to explore interdisciplinary research topics from a variety of industries and areas. Through workshops and collaborating with experts from different disciplines, students identify research topics, apply the appropriate data science methods, and use data to advance innovative solutions. Students receive guidance and advising to work effectively in teams, refine project proposals, and build the domain knowledge necessary in their selected area. By the end of the course, each team submits a detailed research proposal, including project rationale, methods, and expected outcomes, which they intend to execute during CSCI E-599a.

The semester right after the precapstone, you enroll in the online capstone, CSCI E-599a Data Science Capstone , as your final one-and-only course, either in the fall or the spring. Due to the heavy demands of the capstone, it is considered a full-time course.  All other degree requirements must be fulfilled so you can draw upon your entire ALM training to produce a final project worthy of a Harvard degree.

Sample Pathway

You need to complete 12 courses (48 credits) to earn the degree. 

• You'll register for the precapstone in the summer as your 11th course. Then in the fall, you'll register for the capstone as your 12th and final course.
• You'll register for the precapstone in the January term as your 11th course. Then in the spring, you'll register for the capstone as your 12th and final course.

Bruce Huang, EdD, PhD, Director of Master's Degree Program in Information Technology, Harvard Extension School

• Utility Menu

SEAS Design & Project Fair

• Computational Science and Engineering Capstone Project (APCOMP 297R)

The Computational Science and Engineering (CSE) capstone project is intended to integrate and apply the skills and ideas CSE students acquire in their core courses and electives. By requiring students to complete a substantial and challenging collaborative project, the capstone course prepares students for the professional world and ensure that they are trained to conduct research. Students deal with real-world problems, messy data sets, and the chance to work on an end-to-end solution to a problem using computational methods.

• Quest for the Best Cat Photo
• Entity Linking using Kensho-Derived Wikimedia Dataset

Title : Quest for the Best Cat Photo

Members : Kyra Ballard, Andrew Fu, Shravan Nageswaran, Emily Xie

About : Our aim is to help increase the adoption rates of cats at shelters in order to prevent their euthanasia. While high quality photos are crucial in helping pets find new owners, it’s difficult to take good shots of cats given their skittish nature. Hence, we have partnered with Adoptimize and Austin Pets Alive in researching a model that, given a video of a cat, finds the optimal frame. To achieve this goal, we deployed a variety of computer vision methods to extract features, which are then used in a logistic regression that we tested and refined.

Title : Entity Linking using Kensho-Derived Wikimedia Dataset

Members : David Zheng, Dean Hathout, Tyler Yoo, Weiru Chen

About : Our project focuses on the foundational NLP task of entity linking (aka entity disambiguation), the task of assigning identities to entities in a body of text. Partnering with Kensho Technologies in Cambridge, we developed a model that leverages both the Kensho-Derived Wikimedia Dataset and a context-based approach to make entity predictions. We achieve accuracies of 85.5%, which is close to the state of the art, and discuss opportunities for further development and optimization.

• 2021 COURSE PROJECTS
• Applied Physics
• Computer Science
• Engineering Sciences
• Master in Design Engineering (MDE)

You don't smell human...

Want direct access to our course data? Contact us .

Data Science: Capstone

Show what you’ve learned from the Professional Certificate Program in Data Science.

Associated Schools

Harvard T.H. Chan School of Public Health

What you'll learn.

How to apply the knowledge base and skills learned throughout the series to a real-world problem

Independently work on a data analysis project

Course description

To become an expert data scientist you need practice and experience. By completing this capstone project you will get an opportunity to apply the knowledge and skills in R data analysis that you have gained throughout the series. This final project will test your skills in data visualization, probability, inference and modeling, data wrangling, data organization, regression, and machine learning.

Unlike the rest of our Professional Certificate Program in Data Science, in this course, you will receive much less guidance from the instructors. When you complete the project you will have a data product to show off to potential employers or educational programs, a strong indicator of your expertise in the field of data science.

Instructors

Rafael Irizarry

You may also like.

Data Science: Inference and Modeling

Learn inference and modeling: two of the most widely used statistical tools in data analysis.

Data Science: Probability

Learn probability theory — essential for a data scientist — using a case study on the financial crisis of 2007–2008.

High-Dimensional Data Analysis

A focus on several techniques that are widely used in the analysis of high-dimensional data.

Hien Nguyen

Software developer

Software engineer experienced in back end system, integration , devops, software architectures, optimization, distributed computing, large-scale system, cloud services, data structures and algorithms.

Salary request: 84,000 e/ year

• Java- Node.js - Python - SQL - NoSQL - Cloud - Ubuntu, Centos
• Software architectures - MQTT - Big data
• Multi-thread - Distributed - Scalability - Performance tuning - Optization
• Spring frameowork(Spring boot, Data, MVC,Rest/Webflux, Cloud,AOP, Batch,Integration )
• Caching : Redis, Memcached
• Build tool : Maven, Gradle
• Indexing tool : ElasticSearch, Apache Solr
• ORM : Hibernate, JPA
• Report and analytics : Jaspersoft
• Testing : Junit, Mockito, Robot framework ,TestNG, DBunit, JMetter, JBehave , Cucumber , BDD, TDD, unit testing , integration test,usability testing, acceptance testing
• OOP - Lambda - Streams - Microservices(Netflix) - CQRS - DDD - Event sourcing - MQTT - STOMP
• Design pattern: Singleton

Databasese :

• MySQL/MariaDB - PostgreSQL - MS SQL server - MongoDB - Cassandra - Neo4J
• Migration tool: Flyway, Liquibase
• AWS : DynamoDB, S3

Server - CD/CI - DevOps - Network

• Unix, Linux(Ubuntu, Centos7)
• Version control : Git, Svn
• Container : Docker, Vagrant
• Network : TCP/IP - https - TLS - SSL - SSH key (DSA, RSA), Keystore, Telnet, Network proxy, RSocket
• Apache Hadoop, Spark, HBase,HDFS, Apache Kafka

Machine learning

• JS (Vue.js) - AngularJS - Typescript - Engine template
• Webpack - bower - gulp - npm

• Large scale system: Multi thread, Concurrency, Reactive streams, Distributed system,
• Testing : Junit, Mockito, Robot framework ,Selenium, TestNG, DBunit, JMetter, JBehave , Cucumber , BDD, TDD, unit testing , integration test
• Lambda - Streams - Microservices(Netflix) - CQRS - DDD - Event sourcing - MQTT - STOMP
• Microservices, OOP,
• AWS - GCP - Azure
• Cloud native : Pivotal
• GCP :BigTable
• Container, Kubernetes, Ansible, Jenkins, Build/ release pipeline, Code coverage

Software architecture

• Distributed system
• Modular system
• Microservices
• Protocol : IP/TCP - UDP - OSI - SMTP - SFTP - Real time streaming RTCP - Remove desktop protocol (RDP), MQTT,AMQP
• SSL TLS Firewall
• Search, Sort, Divide and conquer, Greedy, DP, DFS, BFS, BS, Hashing algorithms,etc

Design patterns

• Creational DP : Builder, Prototype,Singleton,Factory
• Behavior pattern :Chain of responsibility,Command, Iterator, Interpreter, Mediator, State, Template, Observer
• Structural pattern : Adapter, Decorator, Facade, Proxy, Bridge,Composite

Machine learning | Deep learning

• Javascript frameworks - Typescript - Node.js

Training certificate

• Azure fundamentals
• Architect great solutions in Azure(Microsoft)

My portfolio projects

Online shopping services.

• Serverless architecture - Cart API (AWS API gateway & AWS Lambda).
• Crawl service with batch request - run AWS EC2 instance.
• Bot service - AWS Lambda functions - Amazon Lex Shopping Lex.
• Detect object service - index in AWS ElasticSearch.
• Object upload with AWS S3 service.
• Integration service with AWS Lambda - API gateway - SQS.
• Logging - Monitor - Alarm.
• ElasticSearch ingest processor.
• Java | Spring framework | AWS (EC2, SQS, CloudWatch, SNS, ElasticSearch, Lex, S3) | AWS DynamoDB, AWS RDS

Enterprise integrations project

• Real time chat service.
• Spark streaming web logs.
• CD/CI with minikube and Docker.

Spring framework | Redis | RabbitMQ | Test (spin up Docker containers for Integration tests) | PostgreSQL, Cassandra

Enterprise batch project

• Read / Write datafiles(xml, csv file ) in batches to MySQL/ Cassandra
• Run jobs : Import to Cassandra, perform 2 join CSV file - order by join column & Back up job : with Quazt scheduling
• Transaction with Spring batch
• Transform and save csv data file - write to MySQL database
• CSV file processing sent to Apache Kafka producer for further processing
• Transfer large data from MySQL to ElasticSearch for index search with ELK stack
• Import index from ElasticSearch to Kibana - ELK stack
• Performance tuning: Scalability : distributed batch processing, set up and config 2 nodes(local) in Apache Kafka and Zookeeper

Spring batch | ElasticSearch | ELK | Apache Kafka | Zookeeper | Database: MySQL-Cassandra-PostgreSQL

Facial recognition detection desktop application

Real time facial recognition detection system

JavaFX | Apache Maven | OpenCV | Tesseract OCR | LIBSVM

Flight booking

API provided for booking flight operations

Netflix Microservices architecture | Spring framework) | RabbitMQ | Redis | Docker | ElasticSearch | ELK | Databases: MySQL, Postgresql, Cassandra, MongoDB

Forum/Blog application

Forum web application for company employees

Spring framework(Boot,Data,Batch,Integration)-ORM(Hibernate) | ElasticSearch | Test(JUnit,Mockito) | Docker | Databases: MySQL/MariaDB

Big data collections project

A collections of big data projects

• IBM stock project
• Customer data analysis data collected in JSON
• Fraud detection system
• Analyze the performance of Hadoop vs. Spark

Apache Hadoop | BigML | Apache Spark | HDFS | MySQL

Job portal web app

SpringMVC | Hibernate | MySQL | RabbitMQ | Redis

Ware house web app

Warehouse API with microservice architecture

Microservices architecture - Spring boot

Hotel booking web app

Full stack web app for booking and searching for available hotels

Restful API with microservices using Netflix stack - Apache Kafka - Apache Avro - ELK - Elasticsearch - Junit - Front end (Vue.js)

Company portal

Company manager web app that manage all the workflow from employee

Spring framework - Hibernate - JPA - MySQL - Spring Cache - Captcha API - Elasticsearch - Scheduled task to back up MySQL database - Hibernate cache - FTP client - JMS

Mobile app clone Meetup app - Full stack development

Back-end: NodeJS(Express.js framework), REST

Database: MongoDB

Front-end: TS, Angular 2, Ionic 2

Webshop API

Spring boot | Postgresql | Docker | Redis | Logs SSL

Movie booking

Moving booking API

Spring boot | RESTful API | Docker | Testing : JUnit/Mockito/MockMVC/AssertJ/Hamcrest - MongoDB

Project Euler

Project Euler with Java, Python, Javascript(still on going)

Chrome extension

A chrome extension that helps you to keep track of the words, infor that you want to save or take note for personal purposes.

Tech stack:

NodeJS, Socket.io, Bootstrap

Product management app

Back-end: NodeJS, ExpressJS, REST, MongoDB(database)

Database : MongoDB ()

Front-end: TypeScript, Angular 2, Ionic 2

Slack clone

Angular 1, Firebase 3, AngularFire 2 Bootstrap3 Grunt Npm, Bower

Learning creating desktop app with node, chromium and electron-This is CRUD desktop app for book store.

Electron,ReactJS,D3.js,Bootstrap,Material design

NIBS business plan competition finalist

Being the web developer for the project

This is my ongoing projects:

SQL databases performance tuning techniques - PostgreSQL - MySQL

Reactive- Event sourcing projects

A collection of event sourcing technique and reactive programming projects

Reactor framework - RxJava- Streams - Akka - Reactive web flux - RabbitMQ

Database : Cassandra - MongoDB

My current pursuing education : Master of Computer Science (Harvard)

Course syllabus.

Doing course assignments, projects and reading research paper

Google Kickstart 2020

2020 google kickstart round b.

First time join Google Kickstart experience

1 is Bike Tour (5pts, 7pts).

Problem 2 is bus routes(10pts, 13 pts), problem 3 robot path decoding (11pts, 16 pts)., problem 4 wandering robot (14pts, 24pts), google summer of code 2020, proposal that get selected to google summer of code 2020.

Google Summer of Code is a global program focused on bringing more student developers into open source software development. Students work with an open source organization on a 3 month programming project during their break from school.

Cool things about Google Summer of Code is that you will get kind of salary ranging from 3000 - 6000 $ paid to seletected students who successfully finish the project(https://developers.google.com/open-source/gsoc/help/student-stipends). And you can get the chance to get seletected for full time role Google interview (https://developers.google.com/open-source/gsoc/help/post-todo)

Here is my proposal for Apache organisation

Apache RocketMQ Scaler for KEDA

Apache RocketMQ Scaler for KEDA project

Context KEDA allows for fine-grained autoscaling (including to/from zero) for event-driven Kubernetes workloads. KEDA serves as a Kubernetes Metrics Server and allows users to define autoscaling rules using a dedicated Kubernetes custom resource definition. KEDA has a number of “scalers” that can both detect if a deployment should be activated or deactivated, and feed custom metrics for a specific event source. In this topic, you need to implement the RocketMQ scalers. You should learn before applying for this topic Helm/Apache RocketMQ Operator/Apache RocketMQ Docker Image Apache RocketMQ multi-replica mechanism(based on DLedger) How KEDA works Mentor [email protected], [email protected] Difficulty: Major Potential mentors: duheng, mail: duheng (at) apache.org Project Devs, mail: dev (at) rocketmq.apache.org

My proposal

Medium Medium

Email: [email protected]

• © Welcome to my portfolio projects
• Developer: Hien Nguyen

The Capstone Experience

About capstones.

Capstone are senior-level project courses that allow you to solve a substantial problem with knowledge gained from many areas in computer science and engineering. Students work in teams to define a problem, develop a solution, produce and demonstrate an artifact that solves the problem, and present their work. Class time focuses on the project design and implementation, but it may also include lectures on the practical application of advanced topics. Interdisciplinary projects that require interaction with other departments are encouraged.

A Capstone course is not simply an advanced course in a particular sub-area, nor is it an unstructured project course. A Capstone is designed to be a culmination of your learning, and a chance to develop and express many skills at once: For example, technical expertise and communication ability.

Capstone Goals

• Projects must be large enough to require teams of several students to work on over one quarter.
• Students must apply concepts from more than one sub-area of CSE (at the 300-level and above).
• The work must involve a substantial design effort.
• Students must present their work using formal oral presentations and written reports.
• Efforts must culminate in an interesting, working artifact.

Capstone Course List

Capstones 2023 - 2024.

• Taught by: Shwetak N. Patel
• Prerequisites: Either EE 271 or CSE 369; either CSE 466, EE 472, or CSE 474/EE 474
• Description: Capstone design experience. Prototype a substantial project mixing hardware, software, and communications. Focuses on embedded processors, programmable logic devices, and emerging platforms for the development of digital systems. Provides a comprehensive experience in specification, design, and management of contemporary embedded systems.
• Taught by: Tim Althoff
• Prerequisites: CSE 332 and CSE 312, and at least one of CSE 446, CSE 442, or CSE 344.
• Description: This Data Science Capstone focuses on the complete end-to-end process of data analysis performed with code: the iterative, and often exploratory, steps that analysts go through to turn data into results. Our focus is not limited to statistical modeling or machine learning, but rather the complete process, including transformation, exploration, modeling, and evaluation choices. Students will work in groups of four on a single project that will tie together and apply previous experiences from CSE 312, 332, 446, 442, 344, and other classes. Students are expected to already possess knowledge of appropriate machine learning, visualization and database methods, and will focus on independently applying those methods in the context of your project. There will therefore be limited lecture material in this course. Course staff will instead work closely with students to critique and advise on their group project. Students will experience the end-to-end data analysis process from transformation and exploration of data to modeling and evaluation. Your group will brainstorm on a project during the first week, before collaboratively exploring the data and implementing a complete data analysis workflow. This capstone course gives hands-on experience with selecting a data science question, and with crafting and evaluating a data science process to answer that question. question.

Winter 2024

• Taught by: Barbara Mones
• Prerequisites: CSE 458; CSE 459
• Description: Apply the knowledge gained in previous animation courses to produce a short animated film. Topics include scene planning, digital cinematography, creature and hard surface modeling, animatics and basics of character animation, and rendering techniques.
• Taught by: ECE
• Taught by: Zoran Popovic
• Prerequisites: CSE 351, 332 and ideally one 400-level course
• Description: TBA
• Taught by: Amy Zhang
• Prerequisites: None, but CSE 440 is strongly suggested
• Description: In this capstone course, students will work in groups to apply software engineering and system design skills they have learned over their four years in computer science towards building a novel social computing system to address a social challenge. We will follow a human-centered design process for groups to ideate, prototype, test, implement, and showcase their novel system. Along the way, students will gain a broad understanding of the current major pressing issues and state of the art of knowledge in social computing, while taking a critical lens toward social computing systems they use every day. Along with the capstone project, we will have readings, group discussions, reflections, and guest speakers working in social computing.
• Taught by: Maya Cakmak
• Prerequisites: Senior standing in CSE or permission of the instructor
• Description: The main goal of this course is to open up new career options in robotics for computer science and engineering students. To that end, the course will teach you the basics of robotics and give you implementation experience. You will learn to use libraries and tools within the most popular robot programming framework ROS (Robot Operating System). We will touch on robot motion, navigation, perception, planning, and interaction through mini-lectures, labs, and assignments, eventually integrating these components to create autonomous or semi-autonomous robotic functionalities. The project will give you team-work experience with large scale software integration and it will get you thinking about opportunities for using robots to address societal challenges.

Spring 2024

• Taught by: Sheng Wang

Prerequisites: CSE 312; CSE 331; CSE 332

• Description: Designs and implements a software tool or software analysis for an important problem in computational molecular biology.
• Taught by: Yoshi Kohno
• Prerequisites: CSE 484

Description: Student teams will be tasked with creating a computer security themed product. The work will progress from product conception to requirements to design to implementation to evaluation. Along the way, students will incorporate key computer security tools and practices, including threat modeling, penetration testing, and bug fixing. Examples include password managers, censorship resistance systems, and mobile payment systems.

• Taught by: Ira Kemelmacher-Shlizerman

Prerequisites: CSE 332, and at least 1, CSE 400 level course recommended

• Description: Virtual and Augmented reality are promising technologies that are certain to make an impact on the future of business and entertainment. In this capstone, students will work in small project teams to build applications and prototype systems using state of the art Virtual Reality (VR) and Augmented Reality (AR) technology. Seattle is a nexus of VR tech, with Oculus Research, Valve, Microsoft (hololens), Google (cardboard, jump), and teams in the area. We will be developing on the latest VR/AR headsets and platforms, and will bring in leading VR experts for lectures and to supervise student projects. Students will experience the end-to-end product cycle from design to deployment, and learn about VR/AR technology and applications. The capstone culminates in a highly anticipated demo day where the students demonstrate their creations to other students, faculty and industry luminaries. (See Video)
• Taught by: Noah Smith
• Prerequisites: 446 or 447 strongly recommended but not required
• Description: This class will provide students with an intensive 10-week experience in successfully completing a challenging, well-scoped research project. Participants will work in small groups (approximately 3 people in each group) to hone their technical skills to quickly absorb and adapt new technical knowledge, gain experience in complex programming, perform thorough experiments and analysis, and learn how to find a path when faced with negative results.
• Taught by: Simon Peter
• Prerequisites: CSE 451
• Description: This course is intended to give students a thorough understanding of design and implementation issues for modern operating systems. We will cover key design issues in implementing an operating system, such as memory management, inter-core synchronization, scheduling, protection, inter-process communication, device drivers, and file systems, paying particular attention to system designs that differ from the traditional monolithic arrangements of Unix/Linux and Windows.
• Taught by: Rajesh Rao
• Prerequisites: Senior standing in CSE or permission of the instructor.

Description: Design, build and present a prototype device or software tool that solves an important problem in neural engineering. Examples include interfaces based on combining AI with brain-, muscle-, and/or eye-tracking signals to control computers or robotic devices, virtual reality approaches to improving neural function, and machine learning-based software tools for analyzing large-scale neural data.

• Taught by: Steve Tanimoto
• Prerequisites: CSE 332 or instructor permission
• Description: Each team analyzes a wicked problem and develops a game that stimulates player engagement with the problem and approaches to solving it. Tools and techniques include Python, large language models, multiplayer supports, problem-solving theory from AI, formulation frameworks, simulation models, iterative design, Scrum-based agile development, and playtesting.
• Taught by: Richard Anderson
• Prerequisites: CSE 332; CSE 351; either CSE 331 or CSE 352
• Description: Students will work on a group project that makes use of Information and Communication Technologies (ICTs) to address global needs with an emphasis on developing countries. While ICTs are having an enormous impact on livelihoods worldwide, deployment environments vary dramatically based on available infrastructure and technologies accessible to people. Areas of projects could include: health information systems, data collection technologies, applications for basic mobile phones, user interface design for low literate populations, behavior change communication, voice based social networks, community cellular networks, open source projects for global good, low-cost smartphones, satellite image analysis or mobile financial services targeting domains including health, education, agriculture, finance, and livelihood.
• 5 credits (satisfies DIV requirement)

Capstones 2022 - 2023

• Description: Data analysis is a central activity for scientific research and is increasingly a critical part of decision making in government and business. However, producing reliable data analysis outcomes is challenging since the decisions made throughout the analysis process can dramatically affect the eventual outcome. This Data Science Capstone focuses on the complete end-to-end process of data analysis performed with code: the iterative, and often exploratory, steps that analysts go through to turn data into results. Our focus is not limited to statistical modeling or machine learning, but rather the complete process, including transformation, exploration, modeling, and evaluation choices. Students will work in groups of four on a single project that will tie together and apply previous experiences from CSE 312, 332, 446, 442, 344, and other classes. Students are expected to already possess knowledge of appropriate machine learning, visualization and database methods, and will focus on independently applying those methods in the context of your project. There will therefore be limited lecture material in this course. Course staff will instead work closely with students to critique and advise on their group project. Students will experience the end-to-end data analysis process from transformation and exploration of data to modeling and evaluation. Your group will brainstorm on a project during the first week, before collaboratively exploring the data and implementing a complete data analysis workflow. This capstone course gives hands-on experience with selecting a data science question, and with crafting and evaluating a data science process to answer that question. question.

Winter 2023

• Taught by: ECE Department
• Taught by: Haduong
• Prerequisites: CSE 351, 332 and ideally one 400
• Description: Coming soon...
• Taught by: Roesner

Spring 2023

• Taught by: Wang
• Taught by: Zhang
• Prerequisites: TBD
• Taught by: Cakmak
• Explain basics of robot navigation, perception, planning, interaction;
• Enumerate challenging problems in robotics;
• Use important tools in ROS, contribute to ROS, find available packages in ROS;
• Operate a robot platform using ROS tools;
• Articulate the importance of interface design and robustness of functionalities in robotics.
• Taught by: N. Smith
• Prerequisites: none listed
• Taught by: S. Peter
• Taught by: Shyam Gollakota
• Prerequisites: None
• Description: Create cool and interesting projects where you get to use various mobile systems and networking technologies. The capstone will include background material on Android programming, networking as well as how various sensors like GPS, IMU, acoustic work to enable tracking, localization, augmented reality and ranging applications. This class will provide students with an intensive 10-week experience in successfully completing an intellectually-exciting project in mobile systems and networking. Participants will work in small groups to learn new technical skills to quickly absorb and adapt new technical knowledge, gain experience in mobile programming and networking, implement their ideas on mobile devices and perform thorough experiments and analysis. Other than programming, no prerequisites are required.
• Taught by: R. Anderson
• Description: Students will work on group project that use of Information and Communication Technologies (ICTs) to address global needs with an emphasis on developing countries. While ICTs are having an enormous impact on livelihoods worldwide, deployment environments vary dramatically based on available infrastructure and technologies accessible to people. Areas of projects could include: health information systems, data collection technologies, applications for basic mobile phones, user interface design for low literate populations, behavior change communication, voice based social networks, community cellular networks, open source projects for global good, low-cost smartphones, satellite image analysis or mobile financial services targeting domains including health, education, agriculture, finance, and livelihood.

Capstones 2021 - 2022

• Taught by: Patel,Shwetak N.
• Taught by: Althoff
• Description: Student teams design and implement a software project involving multiple areas of the CSE curriculum. Course emphasizes the development process, rather than the product.
• Taught by: Heimerl
• Prerequisites: Recommended: HCI (440) or Operating Systems (451) or Networks (461)
• Description: Public Interest Technology Capstone Experience. Develop tools and technologies in partnership with communities around Seattle and Tacoma that assist in small organizations running Internet access networks. Focus on core network development as well as HCI and user-facing systems. Provides a comprehensive experience designing, building, and deploying technology in the real world with the goal of doing social good.

Winter 2022

• Taught by: E.E.
• Description: coming soon...
• Taught by: Anderson, Richard
• Prerequisites: CSE 351 and 332

Spring 2022

• Taught by: Kohno
• Taught by: Popovic
• Taught by: Reinecke
• Description: Students will work in groups of three or four on a single project that parallels the experience of delivering an interactive prototype within a company or with a customer. Students are expected to already possess knowledge of appropriate HCI methods, and will focus on independently applying those methods in the context of your project. There will therefore be little lecture material in this course. Course staff will instead work closely with students to critique and advise on their group project. Students will experience the end-to-end product cycle from design to deployment.
• (1) an ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics
• (2) an ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, , and economic factors
• (3) an ability to communicate effectively with a range of audiences
• (4) an ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts
• (5) an ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives
• (6) an ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions
• (7) an ability to acquire and apply new knowledge as needed, using appropriate learning strategies
• Description: Students work in substantial teams to design, implement, and release a software project involving multiple areas of the CSE curriculum. Emphasis is placed on the development process itself, rather than on the product. Teams are expected to develop a work plan, and to track and document their progress against it.

Capstones 2020 - 2021

CSE/EE 475: Embedded Systems (Taught by CSE) - Bruce Hemingway

Prereq: CSE 369 and 474

CSE 481DS: Data Science Capstone - Tim Althoff

Pre-req: CSE 332, 312 and one of (446, 442, 344)

Description: Data analysis is a central activity for scientific research and is increasingly a critical part of decision making in government and business. However, producing reliable data analysis outcomes is challenging since the decisions made throughout the analysis process can dramatically affect the eventual outcome. The Data Science Capstone focuses on the complete end-to-end process of data analysis performed with code: the iterative, and often exploratory, steps that analysts go through to turn data into results. Our focus is not limited to statistical modeling or machine learning, but rather the complete process, including transformation, exploration, modeling, and evaluation choices. Students will work in groups of three or four on a single project that will tie together and apply previous experiences from CSE 312, 332, 446, 442, 344, and other classes. Students are expected to already possess knowledge of appropriate machine learning, visualization and database methods, and will focus on independently applying those methods in the context of your project. There will therefore be little lecture material in this course. Course staff will instead work closely with students to critique and advise on their group project. Students will experience the end-to-end data analysis process from transformation and exploration of data to modeling and evaluation. Your group will brainstorm on a project during the first week, before collaboratively exploring the data and implementing a complete data analysis workflow. This capstone course gives hands-on experience with selecting a data science question, and with crafting and evaluating a data science process to answer that question. CSE students should have completed CSE 332 and CSE 312, and at least one of CSE 446, CSE 442, or CSE 344. There are no other requirements for participating in this capstone class.

Winter 2021

CSE 460: Animation Capstone - Barbara Mones (Note: requires application and admission in summer)

CSE/EE 475: Embedded Systems Capstone - ECE Faculty

CSE 481i: Sound and Media Capstone - Bruce Hemingway

• Pre-req: CSE 351, 332 and ideally one 400

Description: This capstone will build projects utilizing computer audio and video techniques for human interfacing, sound and video recording and playback, encoding and decoding, synchronization, sound synthesis, recognition, and analysis/resynthesis. Projects may contain any types of media. Students will work in teams to design, implement, and release a software project utilizing some of the techniques such as those in the links below.

We have two Oculus-VR development kits , two Tobii EyeX Eye-tracking Controllers , and 15 Leap Motion controllers for use in building musical/audio/media interfaces. We also have three Nvidia Jetson TX1 Developer Kit s for high-performance Deep Neural Network learning and computer vision.

CSE 481 S: Security Capstone - Kohno

Prereq: CSE 484 (CE students graduating in fall who have not completed 484, please send email asap to [email protected] )

CSE 482: Accessibility Capstone - Anat Caspi

Prereq: (recommended) CSE 490 D, (recommended) CSE440

Notes: This course has a DIV designation and fulfills the diversity requirement

Description: Accessibility is quickly emerging as a leading consideration for product design and engineering. Disability is part of the human condition – almost everyone will be temporarily or permanently impaired at some point in life, and those who survive to old age will experience increasing difficulties. Disability is complex and heterogeneous, and the technological interventions to accommodate different abilities are wide ranging and vary with context. Many familiar technologies like voice recognition, text-to-speech, and gaze detection were initially engineered to assist people with disabilities gain more access and increase participation in daily life. Students will work in interdisciplinary project teams that include community members with expertise on project needs. Groups will follow participatory design practices and apply design and engineering skills to create technology solutions that increase independence and improve quality of life for people of all abilities. Teams will complete one end-to-end product iteration cycle: ideation, design, specification refinement, prototype and usability testing  

Spring 2021

CSE 428 A: Computational Biology Capstone

Description: Designs and implements a software tool or software analysis for an important problem in computational molecular biology. 

CSE/EE 475 A: Embedded Systems Capstone - ECE Faculty

CSE 481 C: Neural Engineering Capstone - Rajesh Rao

Prerequisites: (Recommended) CSE 490N, (Recommended) CSE 446 or CSE 473

CSE 481 D: Games Capstone - Zoran Popovic

CSE 481 H: HCI Capstone - Reinecke

• Students will work in groups of three or four on a single project that parallels the experience of delivering an interactive prototype within a company or with a customer. Students are expected to already possess knowledge of appropriate HCI methods, and will focus on independently applying those methods in the context of your project. There will therefore be little lecture material in this course. Course staff will instead work closely with students to critique and advise on their group project. Students will experience the end-to-end product cycle from design to deployment.
• CSE 481 N: Natural Language Processing Capstone - Noah Smith
• Prereq: CSE 447, CSE446 (ML) is recommended
• Description : Algorithms that deal with text or speech, either as inputs as outputs, are increasingly part of our everyday lives.  Systems that translate accurately between languages, read many documents and summarize or answer questions about them, and even hold conversations with us, are on the horizon. Successfully designing and implementing such systems requires understanding and integration of ideas from linguistics, statistics, and computation, and testing them rigorously requires a strong grasp of experimental methodology.  This capstone course gives hands-on experience with selecting a natural language processing problem and with crafting and evaluating a solution.

CSE 481 V : Virtual and Augmented Reality - Ira Kemelmacher-Shlizerman

Description: Virtual and Augmented reality are promising technologies that are certain to make an impact on the future of business and entertainment. In this capstone, students will work in small project teams to build applications and prototype systems using state of the art Virtual Reality (VR) and Augmented Reality (AR) technology.  Seattle is a nexus of VR tech, with Oculus Research, Valve, Microsoft (hololens), Google (cardboard, jump), and teams in the area.  We will be developing on the latest VR/AR headsets and platforms, and will bring in leading VR experts for lectures and to supervise student projects.  Students will experience the  end-to-end product cycle from design to deployment, and learn about VR/AR technology and applications. The capstone culminates in a highly anticipated demo day where the students demonstrate their creations to other students, faculty and industry luminaries. ( See Video )

• CSE 482 K: Technology for Resource Constrained Environments - Richard Anderson
• Prereqs: CSE 351 and 332
• Description:  Students will work on group project that use of Information and Communication Technologies (ICTs) to address global needs with an emphasis on developing countries.  While ICTs are having an enormous impact on livelihoods worldwide, deployment environments vary dramatically based on available infrastructure and technologies accessible to people.    Areas of projects could include: health information systems,  data collection technologies,  applications for basic mobile phones,  user interface design for low literate populations,  behavior change communication, voice based social networks, community cellular networks,  open source projects for global good, low-cost smartphones, satellite image analysis or mobile financial services targeting domains including health, education, agriculture, finance, and livelihood.   

Smart Green Innovations in Industry 4.0 for Climate Change Risk Management pp 441–449 Cite as

Digital Technologies of the Project “Moscow ‘Smart City—2030’”: The Transport Sector

• Aleksandr A. Matenkov   ORCID: orcid.org/0000-0003-3831-1245 3 ,
• Ruslan I. Grin   ORCID: orcid.org/0000-0003-4343-9219 3 ,
• Markha K. Muzaeva   ORCID: orcid.org/0000-0003-0843-5685 3 &
• Dali A. Tsuraeva   ORCID: orcid.org/0000-0002-2445-6729 3  
• First Online: 17 May 2023

408 Accesses

Part of the book series: Environmental Footprints and Eco-design of Products and Processes ((EFEPP))

The research deals with the priority areas of digitalization in the transport sector in interpreting the strategy “Moscow ‘Smart City—2030’.” The research aims to study the priority areas of digitalization of transport flows of the metropolis and the potential impact of digitalization on the functioning of the territory. By applying the methods of content analysis and the regulatory-legal method in the research, the authors assessed the position of the city authorities on the most sought-after areas of innovation in the transport sector and determined the composition of socio-economic benefits of digitalization of the transport sector. The analysis of statistical indicators of the development of the transport sector of the Moscow urban agglomeration has confirmed the growing need to improve the efficiency of transport infrastructure in the broad sense, including an increase in the level of connectivity of the city districts and the level of sustainability of the transport system. The results show certain disproportions between the priority areas of transport development and the actual needs of the urban infrastructure, as well as the presence of significant legal constraints in implementing uncrewed transport concepts. It is demonstrated that there is a certain consensus between the municipal authorities and the population on the issue of assigning the transport sector among the priorities for implementing digital technology. The specifics of the metropolitan area (high concentration of capital and innovation activity) allow for considering Moscow as a model example of the introduction of innovative technologies. In this regard, it is necessary to optimize the legal restrictions on the introduction of innovations in the field of transport (on the model of a legal sandbox, Regulatory Sandbox).

• Digitalization
• Digital technologies
• Innovations

JEL Classification

This is a preview of subscription content, log in via an institution .

Buying options

• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Durable hardcover edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Government of the Russian Federation (2022) Decree “On approval of the strategy of spatial development of the Russian Federation until 2025” (13 Feb 2019 No. 207-r, as amended on 30 Sep 2022). Moscow, Russia. Retrieved from https://docs.cntd.ru/document/552378463 . Accessed 10 Dec 2022

Polidi T (2017) Liberation of cities: How agglomerations will help Russian economy. RBK Daily. Retrieved from https://www.rbc.ru/opinions/economics/11/10/2017/59dde2ce9a79475a5f5e5df5 . Accessed 10 Dec 2022

Demidova A, Gubernatorov E (2017) A quarter of the world’s population got predicted life in giant cities by 2030. RBK Daily. Retrieved from https://www.rbc.ru/business/06/07/2017/595df2c19a794776e863d1b3 . Accessed 10 Dec 2022

Ivanitskaya NV, Baybulov AK, Safronchuk MV (2020) Modeling of the stress-strain state of a transport tunnel under load as a measure to reduce operational risks to transportation facilities. J Phys: Conf Ser 1703(1):012024. https://doi.org/10.1088/1742-6596/1703/1/012024

Article   Google Scholar  

Ministry of Transport of the Russian Federation (2020) Building a transport system of the future: the traffic control center’s performance report 2020. Retrieved from https://www.polisnetwork.eu/wp-content/uploads/2021/02/MTCC_EN.pdf . Accessed 10 Dec 2022

Bakhur V (2021) Cisco broadband index survey: Russians consider Internet access no less important than utilities. CNews. Retrieved from https://www.cnews.ru/news/line/2021-07-19_issledovanie_cisco_broadband_index . Accessed 12 Dec 2022

Autonews (2021) Authorities named the total number of cars in Moscow. Retrieved from https://www.autonews.ru/news/61c853cb9a794703b66ac3d4 . Accessed 10 Dec 2022

Department of Transport of Moscow (2017) Digitalization of Moscow transport: department of transport of Moscow. Retrieved from https://report2010-2017.transport.mos.ru/pdf/ar/en/mega-projects_digitalization.pdf . Accessed 10 Dec 2022

Yadova EN, Levich PA (2020) Analysis of preparedness to the modern (or up to date) technologies in conceptual frame of STS and RRI. Technologos 2:25–41. https://doi.org/10.15593/perm.kipf/2020.2.03

RAI Amsterdam (2021) Three smart cities in traffic management: Perth, Moscow, Mexico City. Retrieved from https://www.intertraffic.com/news/traffic-management/three-smart-cities-in-traffic-management-perth-mexico-city-moscow/ . Accessed 10 Dec 2022

Department of Information Technology of Moscow (2018) Concept of Moscow 2030. Retrieved from https://2030.mos.ru/netcat_files/userfiles/documents_2030/opros.pdf . Accessed 10 Dec 2022

KPMG (2020) Autonomous vehicles readiness index. Retrieved from https://home.kpmg/xx/en/home/insights/2020/06/autonomous-vehicles-readiness-index.html . Accessed 12 Dec 2022

Safronchuk MV, Sergeeva MV (2021) The concept of economic growth through digital economy perspective. In: Popkova EG, Sergi BS (eds) Modern global economic system: evolutionary development vs. revolutionary leap. Springer, Cham, Switzerland, pp 1264–1271. https://doi.org/10.1007/978-3-030-69415-9_138

Ivanov OV, Shamanina EA (2021) PPP as a tool to achieve sustainable development goals and implement the concept of “Quality infrastructure investments”. In: Zavyalova EB, Popkova EG (eds) Industry 4.0: exploring the consequences of climate change. Palgrave Macmillan, Cham, Switzerland, pp 309–322. https://doi.org/10.1007/978-3-030-75405-1_28

Safronchuk MV, Ivanitskaya NV, Baibulov AK (2022) Global labor market and challenges of digitalization. In: Popkova EG (eds) Imitation market modeling in digital economy: game theoretic approaches. Springer, Cham, Switzerland, pp 142–150. https://doi.org/10.1007/978-3-030-93244-2_17

Download references

Author information

Authors and affiliations.

MGIMO University, Moscow, Russia

Aleksandr A. Matenkov, Ruslan I. Grin, Markha K. Muzaeva & Dali A. Tsuraeva

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Aleksandr A. Matenkov .

Editor information

Editors and affiliations.

RUDN University, Moscow, Russia

Elena G. Popkova

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Cite this chapter.

Matenkov, A.A., Grin, R.I., Muzaeva, M.K., Tsuraeva, D.A. (2023). Digital Technologies of the Project “Moscow ‘Smart City—2030’”: The Transport Sector. In: Popkova, E.G. (eds) Smart Green Innovations in Industry 4.0 for Climate Change Risk Management. Environmental Footprints and Eco-design of Products and Processes. Springer, Cham. https://doi.org/10.1007/978-3-031-28457-1_45

Download citation

DOI : https://doi.org/10.1007/978-3-031-28457-1_45

Published : 17 May 2023

Publisher Name : Springer, Cham

Print ISBN : 978-3-031-28456-4

Online ISBN : 978-3-031-28457-1

eBook Packages : Engineering Engineering (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

• ABOUT THE INSTITUTE
• HIGHER EDUCATION
• PRE-UNIVERSITY PROGRAMS
• CERTIFICATION TESTING
• ACCOMMODATION
• GETTING TO THE INSTITUTE
• DOCUMENTS REQUIRED
• THE PROCEDURE

Foundation (pre-UNI) department for foreign citizens

Foundation (pre-UNI) department offers Russian language courses for foreign citizens who would like to pursue higher education at Pushkin Institute or other universities of Russia. The courses abide by “Requirements for a minimum content and level of training of graduates of faculties and departments of pre-university education for foreign citizens.”

Profiles of training at the Foundation (pre-UNI) department

• humanitarian
• medical and biological
• natural science
• engineering and technology

The courses include:

• – humanitarian profile – Russian language, social studies, history and literature;
• – natural science profile – Russian language, mathematics, physics and chemistry;
• – engineering, technical and technological profile – Russian language, mathematics, physics and computer science;
• – economic profile – Russian language, social studies, history and mathematics;
• – medical and biological profile – Russian language, chemistry, biology and physics.

Total amount of the curriculum is 2,376 academic hours.

The tuition fee for the 2023-2024 academic year is 270,080 roubles.

Training periods : from September 01, 2023 to June 30, 2024 and from October 02, 2023 to July 31, 2024.

Enrollment for training

To enroll in full-time education, you should:.

For visa support, you need not later than 2 months before the start of your studies:

• Send by email [email protected] pages of your passport with a photo, personal data, the date of issue of the passport, its validity period (including renewal).
• Fill out the form

The staff of the Institute will respond to you by email within 10 days after receiving the questionnaire. The Institute draws up and sends you an official invitation. If there is no response, send a letter indicating the name, surname, citizenship and date of filling out the questionnaire to the address [email protected] .

To enroll in distance learning, you should:

Send a completed application form and scanned copies of your passport pages with photo, personal data, passport issue date and expiration date in JPEG format (*.jpg, *.jpeg) to the Institute not later than 2 weeks before the start of training. The passport must be valid during the distance learning period. The Institute staff will respond to you by email within 10 days after receiving the questionnaire.

• [email protected]
• [email protected]
• [email protected]

Search form

You are here, cse 281: capstone project ii (3), current catalog description:.

Second of a two semester capstone course sequence that involves the design, implementation, and evaluation of a computer science software project; conducted by small student teams working from project definition to final documentation; each student team has a CSE faculty member serving as its advisor; The second semester emphasis is on project implementation, verification & validation, and documentation requirements. It culminates in a public presentation and live demonstration to external judges as well as CSE faculty and students. Prerequisite: Senior standing and CSE 280.

Instructor: James Mikitka, Corey Montella, Stephen Urban (Fall 2022)

Relationship between course outcomes and Student Outcomes where CSE281 substantially support the Student Outcome #1 and #3.

SO1: Analyze a complex computing problem and to apply principles of comuting and other relevant disciplines to identify solutions.

SO 3:Communicate effectively in a variety of professional contexts.

• Undergraduate
• Course Schedule by Semester
• CSE Course Index
• Capstone Projects
• Accreditation