Profile Image

George Stepaniants

MIT Math & IDSS PhD Student

georgestepaniants@gmail.com gstepan@mit.edu (425) 894-3098 Cambridge, MA
Download CV
Linkedin Google Scholar Github Twitter
Snaptic logo
  • About
  • Research
  • Teaching
  • CV
  • Contact
  • Dark Mode Icon
Profile Image

George Stepaniants

MIT Math & IDSS PhD Student

Download CV
Linkedin Google Scholar Github Twitter

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.

Image service 1

Differential Equations

3 projects

Image service 2

Optimal Transport

2 projects

Image service 3

Neural Networks

1 project

Education

College

University of Washington, Seattle

BSc (2015-2019)

Math and Computer Science Double Major

College

Massachusetts Institute of Technology

PhD (2019-2024)

PhD Graduate Student in Mathematics and Statistics

College

Future Prospects

Stay tuned...

Research

Portfolio 1

Learning partial differential equations in reproducing kernel Hilbert spaces

George Stepaniants

JMLR, 2023
Portfolio 2

GULP: a prediction-based metric between representations

Enric Boix-Adsera, Hannah Lawrence, George Stepaniants, Philippe Rigollet

NeurIPS, 2022
Portfolio 3

Fast and smooth interpolation on Wasserstein space

Sinho Chewi, Julien Clancy, Thibaut Le Gouic, Philippe Rigollet, George Stepaniants, Austin Stromme

AISTATS, 2021
Portfolio 4

Inferring causal networks of dynamical systems through transient dynamics and perturbation

George Stepaniants, Bingni W Brunton, J Nathan Kutz

Physical Review E, 2020
Portfolio 5

The Lebesgue Integral, Chebyshev's Inequality, and the Weierstrass Approximation Theorem

George Stepaniants

Undergraduate Academic Report, 2017

Teaching

Spring 2022 | MIT

18.032 Differential Equations

Fall 2021 | MIT

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

(18.032) Differential Equations (theory focused)

Spring 2022

(18.600) Introduction to Probability

Fall 2021

University of Washington - Community Service

Teaching assistant at University of Washington Math Circle

2015 - 2018

Math tutoring from K12 to college-level subjects

2015 - 2019

Publications

Manuscripts in Review

George Stepaniants. “Learning partial differential equations in reproducing kernel Hilbert spaces.” arXiv preprint arXiv:2108.11580 (2021).

Journal Articles

George Stepaniants, Bingni W. Brunton, and J. Nathan Kutz. “Inferring causal networks of dynamical systems through transient dynamics and perturbation.” Physical Review E 102.4 (2020): 042309.

Conference Proceedings

Enric Boix-Adserà, Hannah Lawrence, George Stepaniants, and Philippe Rigollet. “GULP: a prediction-based metric between representations.” Advances in Neural Information Processing Systems.
Sinho Chewi, Julien Clancy, Thibaut Le Gouic, Philippe Rigollet, George Stepaniants, and Austin Stromme. “Fast and smooth interpolation on Wasserstein space.” International Conference on Artificial Intelligence and Statistics. PMLR, 2021.

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 Lab

Jun 2017 - Sep 2017

Engineering Intern at Allen Institute for Artificial Intelligence (AI2)

Full Stack Development and Data Analysis/Visualization

Aug 2016 - Dec 2016

Natural Language Processing Intern at ABBYY

Parser Accuracy Scoring

Jun 2016 - Aug 2016

Image Analysis Intern at Pfizer

Imaging Algorithms for Automated Brain Slice Imaging

Programming 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

Fill out the contact form below to send me an email.