Gregory J Stein
Curriculum Vitae

Updated as of March 2026. See my Google Scholar page for the latest publications.

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Education
Awards
Professional Experience
Talks
Research Funding
Teaching & Mentorship
Service & Leadership
Publications

Education

Ph.D. in Electrical Engineering & Computer Science
Sep 2015 – Feb 2020 | Cambridge, MA MIT Computer Science & Artificial Intelligence Laboratory
Concentration: Robot Autonomy & Machine Learning | gpa 5.0/5.0

S.M. in Electrical Engineering & Computer Science
Sep 2013 – Aug 2015 | Cambridge, MA MIT Research Laboratory of Electronics
Concentration: Numerical Modeling & Ultrafast Laser Physics | gpa 5.0/5.0

B.S. in Engineering Physics
Sep 2009 – May 2013 | Ithaca, NY Cornell University College of Engineering
Concentration: Numerical Modeling & Ultrafast Laser Physics | gpa 4.0/4.3


Awards

Nominated for GMU Stearns Center Teaching Excellence Award , AY2023

Best Paper Finalist | Conference on Robot Learning , Oct 2018
Nominated a top-3 paper out of over 300 submissions.

Best Oral Presentation | Conference on Robot Learning , Oct 2018
Selected best presentation for talk to over 400 conference attendees.

Trevor R. Cuykendall Memorial Award | Cornell University , Jun 2013
Awarded for outstanding teaching assistantship in Applied & Engineering Physics.

Summa Cum Laude | Cornell University , Jun 2013
Awarded for exemplary grade point average.

Honors in Engineering Physics | Cornell University , Jun 2013
Awarded for exceptional undergraduate thesis.


Professional Experience

George Mason University, Fairfax, VA
RAIL Group
Aug 2020 – present Assistant Professor of Computer Science

  • Runs the Robotic Anticipatory Intelligence and Learning (RAIL) Group at GMU.

MIT
Robust Robotics Group
May 2020 – July 2020 Research Specialist Limited

  • Continued research post-PhD with Prof. Nicholas Roy at MIT.

MIT
Robust Robotics Group
Aug 2015 – May 2020 Ph.D. Graduate Researcher | Robotics & Machine Learning

  • Thesis Committee: Nicholas Roy (Advisor), Leslie P. Kaelbling, George Konidaris, Phillip Isola
  • Developed method for planning with high-level, topological actions to tractably estimate cost when navigating through unknown environments, improving expected cost of travel through real-world environments by 20% yet with orders of magnitude less data than Deep Reinforcement Learning.
  • Formulated sparse map representation to overcome noise limitations of monocular SLAM to enable noise-robust topological planning in uncertain environments.
  • Created method for realistic synthetic data generation with unsupervised image-to-image translation; demonstrated real-world quadrotor obstacle avoidance trained only on synthetic data.

MIT
Optics and Quantum Electronics Group
Aug 2013 – Jul 2015 SM Graduate Researcher | Optics & Numerical Simulation

  • Advisor: Franz X. Kärtner
  • Designed and implemented numerical simulation tool to support optics experiments for lab of 12.
  • Co-authored 8 journal and 15 conference publications for work on numerical simulation.

Niantic
Computer Vision Team
Jan 2018 – Apr 2018 Intern (Jan) | Computer Vision Consultant (Feb – Apr)

  • Taught weekly seminars on computer vision fundamentals—including Sparse Feature Learning, Deep Learning, Monocular SLAM and Bundle Adjustment—to nascent Computer Vision Team.
  • Produced a weekly literature deep dive (as consultant) to educate Computer Vision Team on state-of-the-art results and further support development of Niantic's world-scale AR platform.


Talks

“Learning, Introspection, and Anticipation: Making Robot Planners Comfortable with Missing Knowledge.” MIT. Robotics Seminar. 1 hour invited talk , March 2026

“Learning, Introspection, and Anticipation for Effective and Reliable Task Planning Under Uncertainty.” University of Virginia. CS Seminar. 1 hour invited talk , October 2025

“Learning, Introspection, and Anticipation for Effective and Reliable Task Planning Under Uncertainty.” UT Austin. AI Seminar. 1 hour invited talk , September 2025

“Using Foundation Models for Performant Planning in Uncertain Environments.” Robot Planning in the Era of Foundation Models Workshop at RSS. Seminar. 20 minute invited talk , June 2025

“Learning, Introspection, and Anticipation for Effective and Reliable Task Planning Under Uncertainty.” Virginia Tech Innovation Campus. Seminar. 1 hour invited talk , February 2025

“Learning, Introspection, and Anticipation for Effective and Reliable Task Planning Under Uncertainty.” Cornell University. Robotics Seminar. 1 hour invited talk , February 2025

“Learning, Introspection, and Anticipation for Effective and Reliable Task Planning Under Uncertainty.” University of Milan. Robotics Seminar. 1 hour invited talk , January 2025

“Learning and Introspection for Effective and Reliable Task Planning Under Uncertainty: Towards Household Robots Comfortable with Missing Knowledge.” Learning Effective Abstractions for Planning Workshop at CoRL. 30 minute invited talk , November 2024

“Learning, Introspection, and Anticipation for Effective and Reliable Task Planning Under Uncertainty.” Lehigh University. Robotics Seminar. 1 hour invited talk , November 2024

“Learning, Introspection, and Anticipation for Effective and Reliable Task Planning Under Uncertainty.” The AI Institute, Boston. 1 hour invited talk , October 2024

“Learning, Introspection, and Anticipation for Effective and Reliable Task Planning Under Uncertainty.” Northeastern University. Robotics Seminar Series. 1 hour invited talk , October 2024

“Learning, Introspection, and Anticipation for Effective and Reliable Task Planning Under Uncertainty.” Stevens Institute of Technology, Stevens Institute for Artificial Intelligence (SIAI) Seminar Series. 1 hour invited talk , September 2024

“Performance, Reliability, and Online Improvement for Long-Horizon Planning under Uncertainty.” Arizona State University, Robotics Seminar , December 2023

“Ensuring Reliability and Building Trust for Long-Horizon Planning under Uncertainty.” Invited to give an hour-long (virtual) talk at Intel for their Deep Learning in Practice seminar series. Virtual , April 2022

“Ensuring Reliability and Building Trust for Long-Horizon Planning under Uncertainty.” ONR PI Meeting at MIT. Invited to present an overview of my lab's work for an ONR-led PI meeting , October 2022

“Learning-Augmented Anticipatory Planning: Building capable and trustworthy robots that plan despite missing knowledge” Brown University. Invited to give an hour long talk in a robotics focused weekly seminar series , August 2022

“Towards Interpretable Decision-Making Under Uncertainty via Subgoal-Based Representations for Planning” AAAI-20 Fall Symposium on Cognitive Systems for Anticipatory Thinking, Washington, DC. November 2020. (Canceled due to COVID-19)


Research Funding

Principal Investigator. Tactical Behavior, Coordination, and Obfuscation under Extreme Uncertainty. Army Research Lab. Team: Gregory J. Stein, Daigo Shishika, Xuan Wang, and Xuesu Xiao. Dates: 12/1/2024–11/30/2028. $1,670,000 (Share 25%: $417,500). , 12/1/2024–11/30/2028

Key Personel. Full-Spectrum Intelligence, Surveillance, and Reconnaissance (ISR) Innovation and Integration. USAF, AFMC. Dates: 12/15/2023–8/17/2024. $4,910,000 (Key Personnel, Share 0%). , 12/15/2023–8/17/2024

Principal Investigator. Fast and Reliable Online Retraining and Adaptation for Robot Planning Despite Missing World Knowledge. National Science Foundation. Dates: 6/1/2023–5/31/2026. $499,481 (Share 100%: $499,481). , 6/1/2023–5/31/2026


Teaching & Mentorship

Student Mentorship

Name Expected Graduation My Role
Abhishek Paudel SP26 Advisor
Raihan Islam Arnob SP26 Advisor
Roshan Dhakal SP26 Advisor
Hoang-Dung Bui SP26 Advisor
Arnab Debnath SP26 Co-Advisor (with Jana Košecká)
Abhish Khanal FA26 Advisor
MD Ridwan Hossain Talukder SP27 Advisor
Philip Baldoni SP27 Advisor (part time)
Mohamed Agzhal SP28 Co-Advisor (with Ziyu Yao)
Nathaniel Mack SP30 Advisor (part time)

Courses Taught

TermCourse# Students
SP25CS682: Computer Vision Online/Pre-Recorded47
FA24CS485/ECE370 (cross-list) Autonomous Robotics36
SP24CS482: Computer Vision (UGrad)61
FA23CS695: Decision Making & Reinforcement Learning36
SP23CS482: Computer Vision (UGrad)60
FA22CS685: Autonomous Robotics18
SP22CS695: Foundations of Planning27
FA21CS682: Computer Vision (Grad)7
SP21CS482: Computer Vision (UGrad)44
FA20CS682: Computer Vision (Grad)19

Service & Leadership

Mason Committee Service

PhD Committee (Member), FY2025
MS-CS Committee (Member), FY2024
MS-CS Committee (Member), FY2023, switched from PhD committee during `busy season'
PhD Committee (Member), FY2023
CS Tenure-Track Hiring Committee (Member), FY2022
MS-CS Committee (Member), FY2021

Mason PhD/MS Student Advising Committee Service

Abhish Khanal, CS PhD, Comprehensive Exam, AY25
Aniket Datar, CS PhD, Comprehensive Exam, AY25
Mohammad Nazeri, CS PhD, Comprehensive Exam, AY25
Chenhui Pan, CS PhD, Comprehensive Exam, AY24
Amir Raj, CS PhD, Comprehensive Exam, AY24
Raihan Islam Arnob, CS PhD, Proposal Defense, AY24
Navid Rajabi, CS PhD, Thesis Defense, AY24
Michael Crawshaw, CS PhD, Proposal Defense, AY24
Abhishek Paudel, CS PhD, Proposal Defense, AY24
Hoang-Dung Bui, CS PhD, Proposal Defense, AY24
Mohamed Aghzal, CS PhD, Comprehensive Exam, AY24
Minh Duc Nguyen, CS PhD, Comprehensive Exam, AY24
Dibyendu Das, CS PhD, Comprehensive Exam, AY24
Arnab Debnath, CS PhD, Proposal Defense, AY24
Joseph Prince Matthew, ECE PhD, PhD Defense, AY24
Roshan Dhakal, CS PhD, Thesis Proposal, AY23
Changyang Li, CS PhD, Thesis Defense, AY23
Kevin Murray, CS PhD, Thesis Defense, AY23
Navid Rajabi, CS PhD, Thesis Proposal, AY23
Anuj Pokhrel, CS PhD, Comprehensive Exam, AY23
Gaurab Pokharel, CS PhD, Comprehensive Exam, AY23
Abhish Khanal, CS PhD, Comprehensive Exam, AY23
Yimeng Lee, CS PhD, Thesis Defense, AY23
Kyle L. Jackson, CS PhD, Thesis Defense, AY23
Yimeng Lee, CS PhD, Thesis Proposal, AY23
Kyle L. Jackson, CS PhD, Thesis Proposal, AY23
Joseph Prince Matthew, ECE PhD, PhD Proposal Defense, AY23
Md. Ridwan Hossain Talukder, CS PhD, Comprehensive Exam, AY23
Raihan Islam Arnab, CS PhD, Comprehensive Exam, AY23
Dzung Bui, CS PhD, Comprehensive Exam, AY23
Philip Baldoni, CS PhD, Comprehensive Exam, AY23
Kyle L. Jackson, CS PhD, Pre-Defense, AY23
Yimeng Li, PhD, Comprehensive Exam Committee, AY22
Roshan Dhakal, PhD, Comprehensive Exam Committee, AY22
Abhishek Paudel, PhD, Comprehensive Exam Committee, AY22
Arnab Debnath, PhD, Comprehensive Exam Committee, AY22
Yimeng Li, PhD, Dissertation Proposal Committee, AY22
Michael Crawshaw, PhD, Comprehensive Exam Committee, AY21

Other Mason Service

Director/Co-Director: Autonomous Robotics Lab Fall 2022–Present. In my role as lab director, I am an ambassador of robotics research and regularly give tours for interested students or visitors.

Served on College-Level AI Working Group Served as AI/Robotics expert to discuss integration of generative AI into Mason's research and curriculum. Fall 2024

Mentored 3 High-School Summer Interns Students hosted in the RAIL Group, recruited via the GMU ASSIP program. Summer 2025

Mentored 4 High-School Summer Interns Students hosted in the RAIL Group, recruited via the GMU ASSIP program. Summer 2024

Reviewed 1 application for IDIA Faculty Fellowship Fall 2023

Reviewed 2 applications for IDIA Pre-Doc Fellowship Fall 2023

Mentored 4 High-School Summer Interns Students hosted in the RAIL Group, recruited via the GMU ASSIP program. Summer 2023

Advisor for MS/ECE Capstone Project Served as faculty-advisor a large Capstone project, a requirement for MS/ECE undergraduates, consisting of a 5 student sub-team (specific to the Vision sub-team, which I co-led) and provided guidance and instruction for the large ( 15 student) full team. AY2022

Mentored 3 High-School Summer Interns Students hosted in the RAIL Group, recruited via the GMU ASSIP program., Summer 2022

External PhD/MS Student Committees

External Reader + PhD Defense Service, Christopher Bradley, Massachusetts Institute of Technology

External Reader + PhD Defense Service, W. Nicholas Greene, Massachusetts Institute of Technology

Thesis Committee Member + PhD Defense Service, Raj Korpan, City University of New York

External Service

IEEE Big Data Workshop on Multimodal AI 2025 Program Committee Member & Reviewer, Dec 2025

Co-Organizer, Learning Effective Abstractions for Planning Workshop at CoRL Co-Organized Workshop at the Conference at Robot Learning, October 2025, Co-organized with Naman Shah, Tom Silver, Utkarsh Mishra, Luci Shi, Beomjoon Kim, and Georgia Chalvatzaki.

Program Committee Member, AAMAS 2026 Reviewed 8 papers.

Associate Editor, ICRA 2026 Assigned 10 papers.

Reviewer (2025) Reviewer for RSS, IROS, T-ASE, CASE, CoRL, ICRA

IEEE Big Data Workshop on Multimodal AI 2024 Program Committee Member & Reviewer, Dec 2024

IROS Session Co-Chair, Oct 2024

Mentored 4 High-School AI Final Projects Students were from the Ideaventions Academy. June 2024

Meta-Reviewer for RSS Pioneers Managed 4 applications

Associate Editor, ICRA 2025 Assigned 7 papers.

Reviewer (2024) Reviewer for RSS, ICSR, T-ASE, RA-L, ICRA

IEEE Big Data Workshop on Multimodal AI 2023 Program Committee Member & Reviewer, Dec 2023

Reviewer (2023) Reviewer for ICLR, CoRL, ICRA, RSS Pioneers

Contributed to the National Robotics Roadmap Attended a workshop at Penn to contribute to the next National Robotics Roadmap. Sep 2023

Mentored 2 High-School AI Final Projects Students were from the Ideaventions Academy. May 2023

IROS Session Co-Chair, Oct 2023

Reviewer (2022) Reviewer for ICLR, IROS, CoRL, IEEE Intelligent Systems

NSF Review Panelist Served on an NSF review panel for the CISE directorate.

Conference on Robot Learning Co-Organized Workshop, Dec 2022 Title: Learning, Perception, and Abstraction for Long-Horizon Planning, Co-organized with Rohan Chitnis, Yezhou Yang, Tom Silver, and Jana Košecká

IEEE Big Data Workshop on Multimodal AI 2022 Program Committee Member & Reviewer, Dec 2022

Reviewer (2021) Reviewer for CoRL, NeurIPS, ICLR, RA-L

ICRA Session Co-Chair, May 2021

Reviewer (2020) Reviewer for top conferences: ICLR, CoRL, ICRA, RSS

MIT EECS Communication Lab Communication Advisor, Aug 2016 – Dec 2019. Coached over 100 MIT students in 1-on-1 sessions to improve their technical communication.

MIT Resources for Easing Friction and Stress Peer Counselor, Jan 2017 – Dec 2019. Counseled students across the EECS department on mental wellness and managing stress.

Princeton CITP Workshops on AI and Ethics Invitee, Mar 2018 & Nov 2018. Invited as a technical expert at the intersection of AI, Machine Learning, and Robotics to participate in two workshops on AI and Ethics co-organized by the Princeton University Center for Information Technology Policy.

MIT EECS Visiting Committee Graduate Student Body Co-organizer, Feb 2017. Prepared report and made recommendations for how to improve student wellness and advising quality to panel of experts, reflecting two months of surveys, interviews, and town-hall meetings.

MIT EECS Graduate Student Association Board Member, Dec 2015 – Dec 2016. Elected Communications Vice President by peers for organization representing 630 graduate students; Co-organized week-long orientation for 120 EECS graduate students.


Publications

For a full list of publications, see our publications page.

Publications (Physics)

  • Krishna Murari, Giovanni Cirmi, Hüseyin Cankaya, Gregory J Stein, Benoit Debord, Frederic Gérôme, Felix Ritzkosky, Fetah Benabid, Franz X Kärtner. “Sub-50 fs pulses at 2050 nm from a picosecond Ho:YLF laser using a two-stage Kagome-fiber-based compressor.” Photonics Research. 10(3). 2021 [Impact Factor: 7.1]
  • Krishna Murari, Gregory J. Stein, Huseyin Cankaya, Benoit Debord, Frederic Gerome, Giovanni Cirmi, Oliver D Mücke, Peng Li, Axel Ruehl, Ingmar Hartl, Fetah Benabid, and Franz X Kärtner. “Kagome-fiber-based pulse compression of mid-infrared picosecond pulses from a Ho:YLF amplifier”. Optica 3.8 (2016), pp. 853–853.
  • Gregory J. Stein, Phillip D Keathley, Peter Krogen, Houkun Liang, Jonathas P Siqueira, Chun-Lin Chang, Chien-Jen Lai, Kyung-Han Hong, Guillaume M Laurent, and Franz X Kärtner. “Water-window soft x-ray high-harmonic generation up to the nitrogen K-edge driven by a kHz, 2.1 μm OPCPA source”. Journal of Physics B: Atomic, Molecular and Optical Physics 49.15 (2016).
  • Cheng Jin, Gregory J. Stein, Kyung-Han Hong, and Chii D Lin. “Generation of bright, spatially coherent soft X-ray high harmonics in a hollow waveguide using two-color synthesized laser pulses”. Physical Review Letters 115.4 (2015).
  • Chun-Lin Chang, Peter Krogen, Kyung-Han Hong, Luis E Zapata, Jeffrey Moses, Anne-Laure Calendron, Houkun Liang, Chien-Jen Lai, Gregory J. Stein, Phillip D Keathley, Guillaume Laurent, and Franz X Kärtner. “High-energy, kHz, picosecond hybrid Yb-doped chirped-pulse amplifier”. Optics Express 23.8 (2015), pp. 10132–10144.
  • Houkun Liang, Peter Krogen, Ross Grynko, Ondrej Novak, Chun-Lin Chang, Gregory J. Stein, Darshana Weerawarne, Bonggu Shim, Franz X Kärtner, and Kyung-Han Hong. “Three-octave-spanning supercontinuum generation and sub-two-cycle self-compression of mid-infrared filaments in dielectrics”. Optics Letters 40.6 (2015), pp. 1069–1072.
  • Chun-Lin Chang, Peter Krogen, Houkun Liang, Gregory J. Stein, Jeffrey Moses, Chien-Jen Lai, Jonathas P Siqueira, Luis E Zapata, Franz X Kärtner, and Kyung-Han Hong. “Multi-mJ, kHz, ps deep-ultraviolet source”. Optics Letters 40.4 (2015), pp. 665–668.
  • Chien-Jen Lai, Kyung-Han Hong, Jonathas P Siqueira, Peter Krogen, Chun-Lin Chang, Gregory J. Stein, Houkun Liang, Phillip D Keathley, Guillaume Laurent, Jeffrey Moses, Luis E Zapata, and Franz X Kärtner. “Multi-mJ mid-infrared kHz OPCPA and Yb-doped pump lasers for tabletop coherent soft x-ray generation”. Journal of Optics 17.9 (2015).
  • Kyung-Han Hong, Chien-Jen Lai, Jonathas P Siqueira, Peter Krogen, Jeffrey Moses, Chun-Lin Chang, Gregory J. Stein, Luis E Zapata, and Franz X Kärtner. “Multi-mJ, kHz, 2.1 μm optical parametric chirped-pulse amplifier and high-flux soft x-ray high-harmonic generation”. Optics Letters 39.11 (2014), pp. 3145–3148.
  • Andrew J Lohn, Barney L Doyle, Gregory J. Stein, Patrick R Mickel, Jim E Stevens, and Matthew J Marinella. “Rutherford forward scattering and elastic recoil detection (RFSERD) as a method for characterizing ultra-thin films”. Nuclear Inst. and Methods in Physics Research, B 332 (Aug. 2014), pp. 99–102.