I am an NSF MSPRF postdoctoral fellow at the California Institue of Technology in the Department of Computing and Mathematical Sciences working with Prof. Andrew Stuart.
Broadly, my research is on the intersection of physical applied mathematics, machine learning, and statistics. 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. I also am excited about finding new applications of optimal transport for dataset alignment and inference and comparison of dynamical systems.

I recieved my PhD from the Massachusetts Institute of Technology (MIT) in 2024 in the Department of Mathematics co-advised by Prof. Philippe Rigollet and Prof. Jörn Dunkel funded by the NSF GRFP and MIT Presidential Fellowship.
I was also part of the Interdisciplinary Doctoral Program in Statistics (IDPS) through the Institute for Data, Systems, and Society (IDSS).
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 statistical and machine learning methods to learn models of 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

Seattle

Math and Computer Science Double Major

of Technology

NSF GRFP Graduate Student in Mathematics and Statistics

of Technology

NSF MSPRF Postdoctoral Researcher

at Computing + Mathematical Sciences

Spring 2022 | MIT
## 18.032 Differential Equations

Fall 2021 | MIT
## 18.600 Introduction to Probability

Sep 2019 - Jun 2024

PhD Advisors: Philippe Rigollet and Jörn Dunkel

Thesis: *Inference from limited observations in statistical, dynamical, and functional problems*

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

Summer 2023 - Fall 2023

Summer 2023

Fall 2021 - Spring 2021

2015 - 2018

2015 - 2019

“Discovering dynamics and parameters of nonlinear oscillatory and chaotic systems from partial observations”, Fourth Symposium on Machine Learning and Dynamical Systems, Fields Institute, July 2024

“Covariance Alignment with Optimal Transport”, Yale Applied Mathematics Seminar, New Haven, April 2024

“Gromov-Wasserstein Theory and Application to Metabolomics”, SIAM Conference on Uncertainty Quantification, Trieste, Italy, February 2024

“Gromov-Wasserstein Theory and Application to Metabolomics”, Statistics and Learning Theory Summer School, Tsaghkadzor, Armenia, July 2023

“Optimal transport for automatic alignment of untargeted metabolomic data”, Harvard Applied Math Graduate Student Seminar, Cambridge, March 2023

“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

“Discovering dynamics and parameters of nonlinear oscillatory and chaotic systems from partial observations”, Dynamics Days, UC Davis, January 2024

“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

“Covariance alignment: from maximum-likelihood estimation to Gromov-Wasserstein”, Cornell ORIE Young Researchers Workshop, Cornell, October 2023

“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 2024 - Jun 2027

Sep 2019 - Jun 2024

Dec 2023

Jun 2023

Sep 2019 - Jun 2020

Jun 2019

Jun 2019

Jun 2016 - Aug 2016

Sep 2015

2020

2015 - 2019

2015 - 2019

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

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