About Me
I am a fourth-year PhD student at the Massachusetts Institute of Technology (MIT) in the Department of Mathematics co-advised by Prof. Philippe Rigollet and Prof. Jörn Dunkel.
I am also part of the Interdisciplinary Doctoral Program in Statistics (IDPS) through the Institute for Data, Systems, and Society (IDSS).
Broadly, my research is on the intersection of statistics and physical applied mathematics. I study how statistical and machine learning algorithms can be used to infer and predict systems governed by ordinary and partial differential equations appearing in biology, chemistry, economics, and other fields.
Prior to MIT, I graduated in 2019 from the University of Washington (UW) with a Bachelors of Science in Mathematics and Computer Science where I performed research on network inference methods in the Department of Applied Mathematics under Prof. Nathan Kutz.
Research Highlights
My research develops and applies statistical and machine learning methods to create functional tools that can be directly applied to study spatial and time varying dynamics (ODEs and PDEs) in real-world systems. Examples include inference of network structures from time series data and learning Green's functions from responses to forcing. Other recent projects have included the applications of optimal transport to trajectory inference and to matching problems in biology.
Differential Equations
3 projects
Optimal Transport
2 projects
Neural Networks
1 project
Education
University of Washington, Seattle
BSc (2015-2019)
Math and Computer Science Double Major
Massachusetts Institute of Technology
PhD (2019-2024)
PhD Graduate Student in Mathematics and Statistics
Future Prospects
Stay tuned...
Research
Learning partial differential equations in reproducing kernel Hilbert spaces
George Stepaniants
JMLR, 2023
GULP: a prediction-based metric between representations
Enric Boix-Adsera, Hannah Lawrence, George Stepaniants, Philippe Rigollet
NeurIPS, 2022
Fast and smooth interpolation on Wasserstein space
Sinho Chewi, Julien Clancy, Thibaut Le Gouic, Philippe Rigollet, George Stepaniants, Austin Stromme
AISTATS, 2021
Inferring causal networks of dynamical systems through transient dynamics and perturbation
George Stepaniants, Bingni W Brunton, J Nathan Kutz
Physical Review E, 2020
The Lebesgue Integral, Chebyshev's Inequality, and the Weierstrass Approximation Theorem
George Stepaniants
Undergraduate Academic Report, 2017Teaching
18.032 Differential Equations
18.600 Introduction to Probability
Education
Sep 2019 - Present
Massachusetts Institute of Technology (PhD)
Department of Mathematics and Institute for Data, Systems, and Society (IDSS)PhD Advisors: Philippe Rigollet and Jörn Dunkel
Thesis: In progress
GPA: 4.9/5.0
Sep 2015 - Jun 2019
University of Washington, Seattle (BSc)
Department of Mathematics and Department of Computer Science (double major)Undergraduate Research Advisors: Nathan Kutz and Bing Brunton
Research Topic: Inferring causal networks of dynamical systems through transient dynamics and perturbation
GPA: 3.87/4.00
Teaching Experience
Massachusetts Institute of Technology - Teaching Assistant
Spring 2022
Fall 2021
University of Washington - Community Service
2015 - 2018
2015 - 2019
Publications
Manuscripts in Review
Journal Articles
Conference Proceedings
Talks and Presentations
Invited Talks
“Learning PDEs in a Reproducing Kernel Hilbert Space”, SIAM Conference on Mathematics of Data Science, San Diego, September 2022
“Learning PDEs in a Reproducing Kernel Hilbert Space”, Meeting on Mathematical Statistics, CIRM, Marseille, December 2021
Contributed Talks
“Learning and predicting complex systems dynamics from single-variable observations”, APS March Meeting, Chicago, March 2022
“Learning PDEs in a Reproducing Kernel Hilbert Space”, LIDS Stats & Tea, MIT, December 2021
“Inferring causal networks of dynamical systems through transient dynamics and perturbation”, Econometrics Lunch, MIT, December 2021
“Fusion of Genetically Incompatible Fungal Cells”, UCLA Computational and Applied Math REU Presentation, IPAM, August 2018
“Quantifying Rupture Risk of Brain Anuerysms”, MATDAT18: NSF Materials and Data Science Hackathon, Alexandria, June 2018
https://matdat18.wordpress.ncsu.edu/files/2018/06/Team12.pdf“Hyperparameter Selection”, AI2 Research Internship Final Presentation, Seattle, August 2017
“Beaker Experimentation Platform”, AI2 Research Internship Midterm Presentation, Seattle, August 2017
“Image Analysis in Parkinson's Research”, Pfizer Research Internship Final Presentation, Cambridge, August 2016
Poster Presentations
“Inferring causal networks of dynamical systems through transient dynamics and perturbation”, Undergraduate Research Symposium, UW, June 2019
Internship and Research Experience
Jul 2018 - Aug 2018
Computational and Applied Math Research Experience for Undergrads (REU) at UCLA
Undergraduate Researcher in Mycofluidics LabJun 2017 - Sep 2017
Engineering Intern at Allen Institute for Artificial Intelligence (AI2)
Full Stack Development and Data Analysis/VisualizationAug 2016 - Dec 2016
Natural Language Processing Intern at ABBYY
Parser Accuracy ScoringJun 2016 - Aug 2016
Image Analysis Intern at Pfizer
Imaging Algorithms for Automated Brain Slice ImagingProgramming Languages
Python
92%
Matlab
90%
Julia
85%
R
80%
Computational Skills
Image Analysis
AutoDiff (PyTorch)
Cluster Computing
Numerical Analysis
Data Visualization
Adobe Illustrator
Academic Awards
NSF Graduate Research Fellowship
Sep 2019 - Jun 2024
MIT Presidential Fellow
Sep 2019 - Jun 2020
Phi Beta Kappa Honors Society Member
Jun 2019
Mary Gates Research Scholarship (merit-based)
Jun 2019
University of Washington Dean's List
Jun 2016 - Aug 2016
University of Washington Early Acceptance at 16 (UW Academy)
Sep 2015
Service and Leadership
Founded and led Armenian Student Association at the University of Washington (ASAUW)
2015 - 2019
Competition judge at the University of Washington Math Olympiad
2015 - 2019
Hobbies
- Reading
- History
- Guitar
- Jazz
- Records
- Dancing
- Coffee
Contact
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