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.
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.
3 projects
2 projects
1 project
Math and Computer Science Double Major
PhD Graduate Student in Mathematics and Statistics
Stay tuned...
Sep 2019 - Present
PhD Advisors: Philippe Rigollet and Jörn Dunkel
Thesis: In progress
GPA: 4.9/5.0
Sep 2015 - Jun 2019
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
Spring 2022
Fall 2021
2015 - 2018
2015 - 2019
“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
“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
“Inferring causal networks of dynamical systems through transient dynamics and perturbation”, Undergraduate Research Symposium, UW, June 2019
Jul 2018 - Aug 2018
Jun 2017 - Sep 2017
Aug 2016 - Dec 2016
Jun 2016 - Aug 2016
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85%
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Image Analysis
AutoDiff (PyTorch)
Cluster Computing
Numerical Analysis
Data Visualization
Adobe Illustrator
Sep 2019 - Jun 2024
Sep 2019 - Jun 2020
Jun 2019
Jun 2019
Jun 2016 - Aug 2016
Sep 2015
2015 - 2019
2015 - 2019
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