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...

Spring 2022 | MIT
## 18.032 Differential Equations

Fall 2021 | MIT
## 18.600 Introduction to Probability

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

92%

90%

85%

80%

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

- Reading
- History
- Guitar
- Jazz
- Records
- Dancing
- Coffee

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