NSF Postdoctoral Fellow
Caltech Computing + Mathematical Sciences
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.
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.
As an applied mathematician, I study how the partial, incomplete, and noisy data we collect from physical systems leads to degrees of freedom along which a physical model cannot be resolved. Across a wide range of problems concerning mechanics of materials, fluid flows, biophysical processes, and network dynamics, my goal is to develop mathematical theory and data-driven machine learning architectures that allow us to learn the maximal amount of information from such partially observed systems. My teaching philosophy is inspired by my research, showing students how to discover interesting mathematical ideas in field-specific literature, translate them into well-posed mathematical theories, and when possible, bring these theories to life as numerical algorithms.
My research develops statistical and machine learning methods to model spatial and time varying processes from data; specifically in physical, biological and engineering problems where nonlocal effects are present. Examples include inference of dynamic networks, Green’s functions, autoregressive or memory kernel models, and fractional order equations. These methods are also applicable to partially observed systems, which often display nonlocal interactions in the observed coordinates as well as in time. I also explore how data and systems can be compared using statistical tools such as optimal transport.
3 projects
2 projects
1 project
Math and Computer Science Double Major
NSF GRFP Graduate Student in Mathematics and Statistics
NSF MSPRF Postdoctoral Researcher
at Computing + Mathematical Sciences
Sep 2024
Sep 2024
Sep 2019
Jun 2023
Sep 2019
Jun 2019
My goal as an educator is to teach students how to work on the interface of different disciplines and leverage tools from mathematical theory in physics, life sciences, probability theory, and statistics to solve their problems. My teaching philosophy is to train students to
I am dedicated to mentoring and student outreach, and am taking important steps in education and research mentorship at Caltech as well as in my cultural Armenian community. I continue to expand my outreach and service in communities that historically have had less access to education in mathematics and science.
Fall 2024
Summer 2023 - Fall 2023
Summer 2023
Fall 2021 - Spring 2021
2015 - 2019
2015 - 2016
2020
2015 - 2019
2015 - 2019
Sep 2024 - Jun 2027
NSF Mathematical Sciences Postdoctoral Research Fellow (MSPRF)
Department of Computing and Mathematical Sciences (CMS)Postdoctoral Advisor: Andrew Stuart
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
Sep 2024 - Jun 2027
Sep 2024
Sep 2019 - Jun 2024
Dec 2023
Jun 2023
Sep 2019 - Jun 2020
Jun 2019
Jun 2019
Jun 2016 - Aug 2016
Sep 2015
“Minisymposium on Data-Driven Learning of Dynamical Systems from Partial Observations”, SIAM Conference on Mathematics of Data Science, Atlanta, October 2024
“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
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