About Me

I am a Ph.D. candidate in Applied Mathematics at the University of Houston, driven by a curiosity about how complex systems organize, adapt, and regulate themselves. My work lies at the intersection of probability, computation, and biology, where I develop mathematically grounded models for stochastic systems and data-driven inference. I am particularly interested in Bayesian methods, reinforcement learning for scientific computing, and the emerging area of biocomputing — exploring how biological systems can inform new computational paradigms.

My research spans two main directions.

• In one line of work, I develop Bayesian frameworks for inferring gene regulatory networks, recovering how genes interact and regulate one another using stochastic models of gene expression.
• In another, I investigate how reinforcement learning can be used to adapt and improve Hamiltonian Monte Carlo, enhancing the efficiency and stability of high-dimensional Bayesian inference.

Together, these projects reflect my broader goal of building interpretable, scalable algorithms for scientific discovery.

Outside of research, I enjoy dancing, cooking, reading fiction, traveling, and occasionally unwinding with a good Netflix series. I value both analytical rigor and creative expression — whether I’m building mathematical models, experimenting in the kitchen, or getting lost in a compelling story.