Invited Speaker, Banff BIRS Workshop on Efficient and Reliable Deep Learning (June 2025).
Invited Speaker, Fields Institute Symposium on Machine Learning and Dynamical Systems (June 2025).
NSF Mathematical Sciences Postdoctoral Research Fellowship (MSPRF) awardee (September 2024).
Recipient of IMS Lawrence D. Brown Ph.D. Student Award (one of three nationwide, September 2024).
Presented at Armenian Statistics Summer School under Calouste Gulbenkian Travel Grant (June 2023).
Invited Speaker, CIRM Meeting on Mathematical Statistics (December 2021).
NSF Graduate Research Fellowship (GRFP) and MIT Presidential Fellowship recipient (September 2019).
Elected to Phi Beta Kappa Honors Society (June 2019).
Background
Develops methods to learn mathematical and physical laws from simulated and experimental data, integrating numerical analysis, mechanics, statistics, and machine learning to succeed in data-limited regimes with scientifically informed inductive biases.
Addresses three core challenges: (I) alignment and pooling of scientific data from disparate sources; (II) inference of governing physical laws under noisy and sparse data; (III) design of memory-dependent (autoregressive) and higher-order models to compensate for partial observability in dynamical systems.
Applies methodological advances to domains including biochemistry, materials science, and fluid mechanics.
Teaching philosophy emphasizes guiding students to discover mathematical ideas in domain-specific literature, formalize them into well-posed theories, and implement them as reproducible numerical algorithms.
Seeking tenure-track positions in applied and computational mathematics, data science, statistics, and engineering departments during 2025–2027.