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Research

“People who know what they’re doing don’t make interesting work.”

–Jacob Collier

I am genericallly interested in probability theory. My PhD work focuses on developing limit theories for stochastic dynamics on networks, the goal being to understand how microscopic structues induce complex, global phenomena. For me, the value of mathematics lies in providing a framework for precisely describing the world we see and I am excited to establish mathematical foundations for machine learning, biology, economics, and quantum sciences.

Below is a selected list of my published work, organized in terms on topics (coauthors listed in alphabetical order unless specified). For a complete, chronological ordered (and automatically updated) list, see my Google Scholar profile.

Machine Learning and signal processing

• A variational framework for residual-based adaptivity in neural PDE solvers and operator learning (with Juan D. Toscano, Vivek Oommen, Jerome Darbon, George Em Karniadakis). Submitted. ArXiv.

• Explicitly solvable continuous-time inference for partially-observed Markov processes (with Andrew W. Eckford, Alexander G. Strang, and Peter J. Thomas). IEEE Transactions on Signal Processing (2022). Journal. Arxiv.

Mathematical Biology

• Capacity of a class of fully observed multistate Poisson-type signal transduction channels (with Andrew W. Eckford, and Peter J. Thomas). IEEE International Symposium on Information Theory (2025).

Quantum Algorithms and Networks

• Inferring quantum network topology using local measurements (with Brian Doolittle, Eric Chitambar, Jeffrey Larson, and Zain H. Saleem). PRX Quantum (2023). Journal. Arxiv.

• Quantum circuit cutting for classical shadows (with Michael A. Perlin and Zain H. Saleem). ACM Transactions on Quantum Computing(2024). Journal. Arxiv

• Online detection of golden circuit cutting points (with Ethan Hansen, Shuai Xu, and others). IEEE Conference on Quantum Computing and Engineering (2023). Conference. Arxiv.