About me
About me
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I am a Visiting Assistant Professor in the Department of Applied Mathematics at the University of California, Santa Cruz, working with Prof. Marcella M. Gomez's Team.
My research lies at the intersection of applied algebraic geometry, convex optimization, stochastic sampling, control theory, and scientific machine learning. I developed geometry-aware mathematical and computational methods for structured scientific models, with recent projects on cone-induced geometry, Langevin sampling for PSD-valued graph learning, K-Lorentzian and cone log-concave polynomials, and robust sampled-data control of electrically stimulated biological systems.
My long-term goal is to build mathematically principled and reliable scientific AI methods that respect the intrinsic geometry of the underlying models. For more details about my academic background, see Academic Background.
Research Highlights
Research Highlights
Geometry-aware Methods for Scientific AI
Sampling, Optimization, Bayesian Inference, Uncertainty quantification
Modeling and Control
I developed geometry-aware Langevin sampling methods for structured Bayesian inference, especially for PSD-valued graph models and matrix-constrained learning problems. This work uses log-determinant and cone-induced metrics to design samplers that better respect the intrinsic geometry of the model.
My work on K-Lorentzian and cone log-concave polynomials studies algebraic and geometric structures underlying log-concavity, stability, Rayleigh- type inequalities, and optimization over cones.
I work on sampled-data robust control of electrically stimulated engineered cell factories, with applications to regulating thyroid hormone T4 production using electric field stimulation.
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