phd in computer science princeton university

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Parastoo Abtahi

Research Interests: Human-Computer Interaction, Augmented Reality, Spatial Computing

Research Interests: machine learning, artificial intelligence, computational statistics

Andrew Appel

Research Interests: Software verification, computer security, programming languages, compilers ACM Fellow, 1998; SIGPLAN Distinguished Service Award, 2002

Sanjeev Arora

Research Interests: Developing mathematical and conceptual understanding to enable better and safer AI Member, National Academy of Sciences, 2018; Member, American Academy of Arts and Sciences, 2015; ACM Prize in Computing, 2011, Fulkerson Prize, 2012; Gödel Prize, 2010 and 2001; ACM Fellow, 2009.

David August

Research Interests: Computer Architecture and Compilers

Lennart Beringer

Jack Brassil

Mark Braverman

Research Interests: Complexity theory; algorithms; game theory; machine learning; applications of computer science in healthcare and medicine Abacus Medal, 2022; Presburger Award, 2016; SFCM Stephen Smale Prize, 2014; Packard Fellow, 2013

Sebastian Caldas

Research Interests: Machine learning

Bernard Chazelle

Research Interests: Natural Algorithms, Dynamical Systems, Dynamic Networks, Computational Geometry, Discrepancy Theory Member, American Academy of Arts and Sciences, 2004; ACM Fellow, 1996

Research Interests: My research is in natural language processing (NLP) and machine learning. I am particularly interested in the intersection of text understanding and knowledge representation & reasoning and applications in question answering, information extraction, and conversational systems.

Marcel Dall'Agnol

Research Interests: Sublinear algorithms, quantum computation, interactive proof systems, cryptography

Tri Dao (appointment starts September 1, 2024)

Jia Deng (on leave)

Adji Bousso Dieng

Research Interests: Machine learning, Artificial Intelligence for Science (AI4Science)

Robert Dondero

Zeev Dvir (on leave)

Benjamin Eysenbach

Research Interests: reinforcement learning, machine learning

Christiane Fellbaum

Research Interests: Natural Language Processing, Lexical semantics, the syntax-semantics interface, syntactic alternations, computational linguistics, large-scale lexical resources, idioms, ontology Humboldt Foundation Wolfgang Paul Award, 2001; LREC Antonio Zampolli Prize, 2006

Adam Finkelstein

Research Interests: Computer graphics, animation ACM Fellow, 2014

Robert Fish

Research Interests: Networking and Telecommunications, Computer-Human Interaction, Online Learning and MOOCs, R&D Innovation Methodologies

Research Interests: computer vision, machine learning, deep learning, explainable AI

Michael Freedman

Research Interests: Distributed systems, security, networking ACM Fellow, 2019; ACM Grace Murray Hopper Award, 2018; Presidential Early Career Award (PECASE), 2011; ONR Young Investigator, 2009

Donna Gabai

Tom Griffiths

Aarti Gupta

Research Interests: Formal verification, program analysis, logic decision procedures ACM Fellow, 2017

Research Interests: Theoretical foundations of machine learning, design and analysis of efficient algorithms for machine learning and mathematical optimization.

Felix Heide

Research Interests: Computational Imaging, Computer Vision, Computer Graphics, Optics

Peter Henderson

Research Interests: machine learning, law, and policy

Kyle Jamieson (on leave)

Research Interests: Networking, Wireless Systems, Internet of Things

Alan Kaplan

Research Interests: Mobile software, middleware and protocols, communication and networking for consumer applications and services, software engineering

Brian Kernighan

Research Interests: Application-specific languages, document preparation, user interfaces, software tools, programming methodology Member, American Academy of Arts and Sciences, 2019; Member, National Academy of Engineering, 2002; USENIX Association Lifetime Achievement Award, 1997

Zachary Kincaid

Research Interests: programming languages, program analysis, program verification, automated reasoning

Aleksandra Korolova

Research Interests: Societal impacts of algorithms and AI; privacy; fair and privacy-preserving machine learning; algorithm auditing; targeted advertising; technology policy

Pravesh Kothari

Research Interests: Parallel architectures and systems; distributed systems; operating systems Member, National Academy of Engineering, 2012; ACM SIGOPS Hall of Fame, 2012; IEEE Fellow, 2011; ACM Fellow, 1998

Research Interests: Theoretical foundations for algorithmic decision making, societal impacts of algorithms and AI

Wyatt Lloyd

Research Interests: Distributed Systems

Alex Lombardi

Research Interests: theory and foundations of cryptography

Margaret Martonosi

Research Interests: Computer Architecture in Classical and Quantum Systems American Academy of Arts and Sciences, 2019; IEEE Computer Society Technical Achievement Award, 2018; Anita Borg Institute Technical Leadership Award, 2013; IEEE Fellow, 2010; ACM Fellow, 2009

Jonathan Mayer (on leave)

Research Interests: Technology law and policy, with emphasis on national security, criminal procedure, consumer privacy, network management, and online speech.

Research Interests: programming languages, distributed systems

Andrés Monroy-Hernández

Christopher Moretti

Research Interests: Distributed systems, computer science education

Radhika Nagpal

Research Interests: Robotics

Karthik Narasimhan

Research Interests: Natural language processing, reinforcement learning

Arvind Narayanan

Research Interests: Information privacy, fairness in machine learning, cryptocurrencies, tech policy

Ravi Netravali

Research Interests: Systems, networking, distributed systems

Rotem Oshman

Pedro Paredes

Research Interests: Theoretical Computer Science, Spectral Algorithms, Complexity Theory, Combinatorics, Probability and Computation

Larry Peterson

Research Interests: Networked systems Member, National Academy of Engineering, 2010; IEEE Kobayashi Computer & Communication Award, 2010; ACM SIGCOMM Award, 2013; ACM Fellow, 2000; IEEE Fellow, 2009

Iasonas Petras

Research Interests: Quantum computation, Information-Based Complexity

Yuri Pritykin

Research Interests: Computational biology, functional genomics, single-cell technologies, regulation of gene expression, immunology, cancer

Vikram Ramaswamy

Research Interests: Explainable AI, Fairness in AI and Computer Vision

Ben Raphael

Research Interests: Computational biology

Jennifer Rexford

Research Interests: Networking, network virtualization, Internet measurement, network management, network troubleshooting. SIGCOMM Award for Lifetime Contribution, 2018; ACM-W Athena Lecturer Award, 2016; National Academy of Engineering, 2014; Member, American Academy of Arts and Sciences, 2013; Member, ACM Fellow, 2008

Szymon Rusinkiewicz

Research Interests: Computer graphics; acquisition of 3D shape, reflectance, and appearance of real-world objects.

Olga Russakovsky (on leave)

Research Interests: I work on developing artificially intelligent systems that are able to reason about the visual world. My primary research area is computer vision, closely integrated with machine learning, human-computer interaction and fairness, accountability and transparency.

Sebastian Seung

Research Interests: Machine learning, computational biology Ho-Am Prize in Engineering, 2008; McKnight Scholar Award, 2000

Jaswinder Singh

Research Interests: Parallel computing systems and applications: parallel applications and their implications for software and architectural design; system software and programming environments for multiprocessors Presidential Early Career Award (PECASE), 1997

Research Interests: Computational molecular biology, as well as its interface with machine learning and algorithms. Presidential Early Career Award (PECASE), 2001; ACM Fellow, 2019; ISCB Fellow, 2018

Robert Tarjan

Research Interests: Data structures; graph algorithms; combinatorial optimization; computational complexity; computational geometry; parallel algorithms ACM Turing Award, 1986; Fellow, American Academy of Arts & Sciences, 1985; Member, National Academy of Sciences, 1987; Member National Academy of Engineering, 1988; Member American Philosophical Society, 1990; ACM Fellow, 1994; SIAM Fellow, 2009

Olga Troyanskaya

Research Interests: Bioinformatics; analysis of large-scale biological data sets (genomics, gene expression, proteomics, biological networks); algorithms for integration of data from multiple data sources; visualization of biological data; machine learning methods in bioinformatics ISCB Overton Prize, 2011; GSA Ira Herskowitz Award, 2014

David Walker (on leave)

Research Interests: Programming languages, type systems, compilers, domain-specific languages, software-defined networking

Kevin Wayne

Research Interests: Algorithms and data structures; computer science education ACM Distinguished Educator, 2014

Matthew Weinberg

Huacheng Yu

Research Interests: Data structures, streaming algorithms, communication complexity

Ellen Zhong

Research Interests: Machine learning, computational and structural biology, 3D computer vision, biological imaging

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Princeton University PhD in Computer Science

Computer Science is a concentration offered under the computer science major at Princeton University. We’ve pulled together some essential information you should know about the doctor’s degree program in computer science, including how many students graduate each year, the ethnic diversity of these students, whether or not the degree is offered online, and more.

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AS in Computer Science

Learn the applied programming skills needed to fill in-demand tech roles when you earn your online AS in Computer Science at Southern New Hampshire University.

BS in Computer Science

Learn the front-end design and back-end development skills employers look for in full stack software developers with this online bachelor's degree in computer science from Southern New Hampshire University.

BS in Computer Science - Software Engineering

With a software engineering degree, you'll learn the fundamental concepts and principles – a systematic approach used to develop software on time, on budget and within specifications – throughout your online college classes at SNHU.

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MS in Information Technology - Software Application Development

Learn to manage the development process for a software program with this specialized online master's from Southern New Hampshire University.

Does Princeton Offer an Online PhD in Computer Science?

Princeton does not offer an online option for its computer science doctor’s degree program at this time. To see if the school offers distance learning options in other areas, visit the Princeton Online Learning page.

Princeton Doctorate Student Diversity for Computer Science

Male-to-female ratio.

About 37.5% of the students who received their PhD in computer science in 2019-2020 were women. This is higher than the nationwide number of 19.1%.

Racial-Ethnic Diversity

Racial-ethnic minority graduates* made up 12.5% of the computer science doctor’s degrees at Princeton in 2019-2020. This is higher than the nationwide number of 10%.

*The racial-ethnic minorities count is calculated by taking the total number of students and subtracting white students, international students, and students whose race/ethnicity was unknown. This number is then divided by the total number of students at the school to obtain the racial-ethnic minorities percentage.

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Earning a graduate degree in computer science can lead

Earning a graduate degree in computer science can lead to positions in research institutions, government agencies, technology companies and colleges and universities. These are the top computer science schools. Each school's score reflects its average rating on a scale from 1 (marginal) to 5 (outstanding), based on a survey of academics at peer institutions. Read the methodology »

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Undergraduate Program

Computing is everywhere in modern life, whether it be as mundane as checking friends on a social network or as sophisticated as weather forecasting. More fundamentally, computational thinking has contributed to a broad spectrum of problems, from music composition to drug design.

The computer science department at Princeton offers undergraduate courses in the core areas of computer science and in an array of application and interdisciplinary areas. Because computational thinking is so important, we want to provide every Princeton undergraduate with at least one course in computer science.

Because of the broad range of topics within computer science and the diverse interests of undergraduates,  students may major in computer science through either the A.B. or B.S.E. degree program. We are the only department in the university with this flexibility. Beyond a small core set of courses, students are free to design their own program within a framework that insures in-depth exposure to algorithms and theoretical computer science, computer system design, and applications.

Another important aspect of the curriculum is independent work. Each student does at least one design or research project advised by a member of the faculty. This gives students the opportunity to engage in cutting-edge research or entrepreneurial product design. Many projects are interdisciplinary. For students who would like to study computer science in earnest, but secondary to another discipline, we also offer the Certificate Program in Applications of Computing.

Whether receiving the A.B. or the B.S.E in computer science, students have a wide range of opportunities after graduation. Many join major companies in computing and information technology. Others go to startups or form companies of their own. Other major employers are consulting firms and financial companies. Those students who choose to go on to graduate school do so at the highest ranked CS graduate school programs. Attending professional schools such as medical school or business school is also an option.

Vanderbilt to establish a college dedicated to computing, AI and data science

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Mar 25, 2024, 11:20 AM

Vanderbilt has begun work to establish a transformative college dedicated to computer science, AI, data science and related fields, university leaders announced today. In addition to meeting the growing demand for degrees in technological fields and advancing research in rapidly evolving, computing-related disciplines, the new, interdisciplinary college will collaborate with all of Vanderbilt’s schools and colleges to advance breakthrough discoveries and strengthen computing education through a “computing for all” approach.

The College of Connected Computing will be led by a new dean, who will report to Provost and Vice Chancellor for Academic Affairs C. Cybele Raver and to School of Engineering Dean Krishnendu “Krish” Roy. The search for the college’s dean is scheduled to begin in late August, and recruiting of faculty will begin in the coming months. It will be the first new college at Vanderbilt since the university and the Blair School of Music merged in 1981.

“Of all the factors shaping society, few are more influential than the rapid emergence of advanced computing, AI and data science,” Chancellor Daniel Diermeier said. “To continue to carry out our mission, prepare all our students for their careers and advance research across the university, Vanderbilt must contribute even more to the study, understanding and innovative application of these fast-changing disciplines. Our aim is to make Vanderbilt a global leader in these fields, ensuring our continued academic excellence and capacity for world-changing innovation.”

“Our new college will enable us to build upon our strong programs and catapult Vanderbilt to the forefront of breakthrough discovery and innovation—in key areas of computer science and also in a wide range of other disciplines that capitalize on advanced computational methods. In launching this new college, we will provide students with highest-caliber educational opportunities at the intersection of these pathbreaking fields,” Raver said. “The creation of this college represents a tremendous win and will be transformative for our entire university community.”

Raver noted the ways that Vanderbilt is forging a bold and distinct strategic path to address burgeoning research and educational opportunities, including increasing demand for expertise in computing-related fields. Moreover, she said, the global interest in AI “aligns perfectly” with Vanderbilt’s leading work in that field. She said a dedicated college will enable Vanderbilt to keep making groundbreaking discoveries at the intersections of computing and other disciplines and will more effectively leverage advanced computing to address some of society’s most pressing challenges.

“The establishment of this interdisciplinary, ‘cross-cutting’ college is a watershed moment—not only for the School of Engineering, but also for the entire university,” Roy said. “The future of education, research and thinking in all disciplines is now inherently tied to, and will be greatly influenced by, the knowledge and power of computing. The idea of ‘computing for all’ is fundamental to the future of learning.”  

Many of the specific details about the college—including its departments, degree programs and research infrastructure—will be informed by the recommendations of a task force on connected computing composed of faculty from across the university. In addition, Vice Provost for Research and Innovation Padma Raghavan will launch a Computing Catalyst working group that will engage faculty and staff leaders in computing from across campus and solicit their input on strategically expanding the university’s computing resources. “The decision to establish this new college is rooted in conversations with faculty,” Raver said. “We are continuing that faculty engagement with this working group, and we’re fortunate to have the advice of some of the best minds in these fields as we embark on this exciting journey.”   

The members of the Connected Computing Task Force include:

Krishnendu Roy , Chair   Bruce and Bridgitt Evans Dean of Engineering  University Distinguished Professor of Biomedical Engineering; Pathology, Microbiology and Immunology; and Chemical and Biomolecular Engineering        

Douglas Adams   Vice Dean of the School of Engineering   Daniel F. Flowers Chair Distinguished Professor of Civil and Environmental Engineering  Professor of Mechanical Engineering  Faculty Affiliate, VINSE        

Hiba Baroud   Associate Chair and Associate Professor of Civil and Environmental Engineering James and Alice B. Clark Foundation Faculty Fellow Associate Professor of Computer Science  Faculty Affiliate, VECTOR , Data Science Institute         

Gautam Biswas   Cornelius Vanderbilt Professor of Computer Science and Computer Engineering Professor of Engineering Management  Senior Research Scientist, ISIS   Faculty Affiliate, Data Science Institute        

Erin Calipari   Associate Professor of Pharmacology  Associate Professor of  Molecular Physiology & Biophysics Associate Professor of  Psychiatry & Behavioral Sciences Director, Vanderbilt Center for Addiction Research  Faculty Affiliate, Vanderbilt Brain Institute        

Laurie Cutting   Patricia and Rodes Hart Professor and Professor of Special Education  Professor of Psychology Professor of Pediatrics Professor of Electrical and Computer Engineering Professor of Radiology & Radiological Sciences Associate Provost in the Office of the Vice Provost of Research and Innovation Associate Director of the Vanderbilt Kennedy Center  Faculty Affiliate, Vanderbilt Brain Institute        

Benoit Dawant   Cornelius Vanderbilt Professor of Electrical Engineering Incoming Chair of the Department of Electrical and Computer Engineering  Director and Steering Committee Chair, Vanderbilt Institute for Surgery & Engineering  Professor of Biomedical Engineering Professor of Computer Science      

Abhishek Dubey   Associate Professor of Computer Science  Associate Professor of Electrical and Computer Engineering  Director, SCOPE lab at ISIS   Faculty Affiliate, Institute for Software Integrated Systems and Data Science Institute         

Bennett Landman   Stevenson Professor of Electrical and Computer Engineering and Chair of the Department of Electrical and Computer Engineering  Professor of Biomedical Engineering Professor of Computer Science Professor of Neurology Associate Professor of Biomedical Informatics Associate Professor of Psychiatry and Behavioral Sciences Associate Professor of Radiology and Radiological Sciences Faculty Affiliate, Vanderbilt Institute for Surgery and Engineering (VISE) , Vanderbilt Brain Institute , Vanderbilt Kennedy Center , Vanderbilt University Institute of Image Science (VUIIS) , Data Science Institute         

Michael Matheny   Professor of Biomedical Informatics  Professor of Biostatistics Professor of Medicine Director, Center for Improving the Public’s Health Through Informatics        

Sandeep Neema   Professor of Computer Science  Professor of Electrical and Computer Engineering  Chair of the Executive Council, Institute for Software Integrated Systems         

Ipek Oguz   Assistant Professor of Computer Science  Assistant Professor of Biomedical Engineering Assistant Professor of Electrical & Computer Engineering  Faculty Affiliate, Vanderbilt Institute for Surgery and Engineering (VISE)         

J.B. Ruhl   David Daniels Allen Distinguished Chair of Law  Director, Program in Law and Innovation   Co-Director, Energy, Environment and Land Use Program   Faculty Affiliate, Data Science Institute         

Jesse Spencer-Smith     Professor of the Practice of Computer Science  Adjunct Professor of Psychology Interim Director and Chief Data Scientist, Data Science Institute         

Jonathan Sprinkle   Professor of Computer Science  Professor of Electrical & Computer Engineering  Professor of Civil & Environmental Engineering  Faculty Affiliate, Institute for Software Integrated Systems         

Yuankai “Kenny” Tao   Associate Professor of Biomedical Engineering  Associate Professor of Ophthalmology & Visual Sciences  SPIE Faculty Fellow in Engineering Faculty Affiliate, Vanderbilt Institute for Surgery & Engineering        

Holly Tucker   Mellon Foundation Chair in the Humanities Professor of French  Director, Robert Penn Warren Center for the Humanities         

Kalman Varga   Vice Chair of the Department of Physics & Astronomy Professor of Physics  Director, Minor in Scientific Computing  Faculty Affiliate, VINSE        

Steven Wernke   Chair of the Department of Anthropology Associate Professor of Anthropology  Director, Vanderbilt Institute for Spatial Research (VISR) Faculty Affiliate, Data Science Institute    

Jules White Professor of Computer Science  Associate Professor of Biomedical Informatics  Senior Advisor to the Chancellor for Generative AI in Education and Enterprise Solutions  Faculty Affiliate, Institute for Software Integrated Systems , Data Science Institute         

Dan Work   Director of Graduate Studies in Civil Engineering Professor of Civil & Environmental Engineering  Professor of Computer Science  Faculty Affiliate, VECTOR , Institute for Software Integrated Systems , Data Science Institute           

Tracey George   ex officio   Vice Provost for Faculty Affairs and Professional Education  Charles B. Cox III and Lucy D. Cox Family Chair in Law and Liberty  Professor of Law       

Tiffiny Tung   Ex officio   Vice Provost for Undergraduate Education  Gertrude Conaway Vanderbilt Chair in the Social and Natural Sciences Professor of Anthropology   

  Members of the Vanderbilt community can learn more about this initiative and share feedback with the faculty working group by visiting vanderbilt.edu/about/computingtaskforce .  

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• Connected Computing Task Force
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Computer Science

Bachelor of arts in computer science.

The Bachelor of Arts with a major in computer science requires a minimum of 120 s.h., including at least 41 s.h. of work for the major. Students must maintain a g.p.a. of at least 2.00 in all courses for the major and in all UI courses for the major. A cumulative g.p.a. of at least 2.00 is required for graduation. Students also must complete the College of Liberal Arts and Sciences  GE CLAS Core .

The computer science major for the Bachelor of Arts is designed for students who would like to gain considerable knowledge in computer science and have flexibility in choosing electives. Students preparing for careers in the computing field are encouraged to supplement the base requirements with additional computer science courses. The program's flexibility makes it suitable for combination with other majors.

Coursework for the major includes computer science courses as well as courses in mathematics, statistics, and other supporting disciplines. Work for the major may not be taken pass/nonpass.

Bachelor of Arts students considering a switch to the Bachelor of Science program should choose their GE CLAS Core Natural Sciences courses carefully since students may be able to use the same courses to satisfy the computer science major natural science sequences requirement for the B.S. degree. See "Natural Science Sequences" under Requirements in the  B.S. in computer science  section of the Catalog.

Students who major in computer science may not also major or minor in computer science and engineering, data science, or informatics.

Listed below are the general categories of coursework required to earn the degree; for more specific information on courses, curriculum, and requirements of the Bachelor of Arts in computer science, visit the UI General Catalog .

Prerequisite Structure of Core Courses

Early Admission to the Graduate College

Undergraduate computer science students who have 6 s.h. or less to earn toward graduation may apply for early admission to the Graduate College. Early admission allows students in their final undergraduate semester to take courses for graduate credit in addition to the courses they need to complete their bachelor's degrees.

BA in Computer Science (General Catalog)

NOTICE: The University of Iowa Center for Advancement is an operational name for the State University of Iowa Foundation, an independent, Iowa nonprofit corporation organized as a 501(c)(3) tax-exempt, publicly supported charitable entity working to advance the University of Iowa. Please review its full disclosure statement.

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A delicate touch: teaching robots to handle the unknown

William Xie, a first-year PhD student in computer science, is teaching a robot to reason how gently it should grasp previously unknown objects by using large language models (LLMs). 

DeliGrasp , Xie's project, is an intriguing step beyond the custom, piecemeal solutions currently used to avoid pinching or crushing novel objects. 

In addition, Deligrasp helps the robot translate what it can 'touch' into meaningful information for people. 

"William has gotten some neat results by leveraging common sense information from large language models. For example, the robot can estimate and explain the ripeness of various fruits after touching them." Said his advisor, Professor Nikolaus Correll . 

Let's learn more about DeliGrasp, Xie's journey to robotics, and his plans for the conference Japan and beyond. 

How would you describe this research? 

As humans, we’re able to quickly intuit how exactly we need to pick up a variety of objects, including delicate produce or unwieldy, heavy objects. We’re informed by the visual appearance of an object, what prior knowledge we may have about it, and most importantly, how it feels to the touch when we initially grasp it. 

Robots don’t have this all-encompassing intuition though, and they don’t have end-effectors (grippers/hands) as effective as human hands. So solutions are piecemeal: the community has researched “hands” across the spectrum of mechanical construction, sensing capabilities (tactile, force, vibration, velocity), material (soft, rigid, hybrid, woven, etc…). And then the corresponding machine learning models and/or control methods to enable “appropriately forceful” gripping are bespoke for each of these architectures.

Embedded in LLMs, which are trained on an internet’s worth of data, is common sense physical-reasoning that crudely approximates a human’s (as the saying goes: “all models are wrong, some are useful”). We use the LLM-estimated mass and friction to simplify the grasp controller and deploy it on a two-finger gripper, a prevalent and relatively simple architecture. Key to the controller working is the force feedback sensed by the gripper as it grasps an object, and knowing at what force threshold to stop—the LLM-estimated values directly determine this threshold for any arbitrary object, and our initial results are quite promising.

How did you get inspired to pursue this research?

I wouldn’t say that I was inspired to pursue this specific project. I think, like a lot of robotics research, I had been working away at a big problem for a while, and stumbled into a solution for a much smaller problem. My goal since I arrived here has been to research techniques for assistive robots and devices that restore agency for the elderly and/or mobility-impaired in their everyday lives. I’m particularly interested in shopping (but eventually generalist) robots—one problem we found is that it is really hard to determine, let alone pick ripe fruits and produce with a typical robot gripper and just a camera. In early February, I took a day to try out picking up variably sized objects via hand-tuning our MAGPIE gripper’s force sensing (an affordable, open-source gripper developed by the Correll Lab). It worked well; I let ChatGPT calibrate the gripper which worked even better, and it evolved very quickly into DeliGrasp.

What would you say is one of your most interesting findings so far?

LLMs do a reasonable job of estimating an arbitrary object’s mass (friction, not as well) from just a text description. This isn’t in the paper, but when paired with a picture, they can extend this reasoning for oddballs—gigantic paper airplanes, or miniature (plastic) fruits and vegetables.

With our grasping method, we can sense the contact forces on the gripper as it closes around an object—this is a really good measure of ripeness, it turns out. We can then further employ LLMs to reason about these contact forces to pick out ripe fruit and vegetables!

What does the day-to-day of this research look like?

Leading up to submission, I was running experiments on the robot and picking up different objects with different strategies pretty much every day. A little repetitive, but also exciting. Prior to that, and now that I’m trying to improve the project for the next conference, I spend most of my time reading papers, thinking/coming up with ideas, and setting up small, one-off experiments to try out those ideas.

How did you come to study at CU Boulder? 

For a few years, I’ve known that I really wanted to build robots that could directly, immediately help my loved ones and community. I had a very positive first research experience in my last year of undergrad and learned what it felt like to have true personal agency in pursuing work that I cared about. At the same time I knew I’d be relocating to Boulder after graduation. I was very fortunate that Nikolaus accepted me and let me keep pursuing this goal of mine.

It’d be unfathomable if I could keep doing this research in academia or industry, though of course that would be ideal. But I’m biased toward academia, particularly teaching. I’ve been teaching high school robotics for 5 years now, and now teaching/mentoring undergrads at CU—each day is as fulfilling as the first. I have great mentors across the robotics faculty and senior PhD students we work in ECES 111, a giant, well-equipped space that 3 robotics labs share, and it’s great for collaboration and brainstorming. 

What are your hopes for this international conference (and what conference is it?)

The venue is a workshop at the 2024 International Conference on Robotics and Automation (ICRA 2024), happening in Yokohama, Japan from May 13-17. The name of the workshop is a mouthful: Vision-Language Models for Navigation and Manipulation (VLMNM).

A workshop is detached from the main conference, and kind of is its own little bubble (like a big supermarket—the conference—hosting a pop-up food tasting event—the workshop). I'm really excited to meet other researchers and pick their brains. As a first-year, I’ve spent the past year reading papers from practically everyone on the workshop panel, and from their students. I’ll probably also spend half my time exploring (eating) around the Tokyo area.  

• Graduate Student Stories
• Nikolaus Correll

A robotic gripper holds various objects. On the left, DeliGrasp allows objects to be held without deforming them by using an LLM to reason about their physical properties and hold them with appropriate delicacy. 

William Xie

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