I work on probabilistic modeling and machine learning systems that quantify uncertainty. My research bridges statistical foundations with modern deep learning, focusing on methods that are both theoretically grounded and practically scalable. Primary interests include Bayesian inference, variational methods, generative modeling, and robustness under distribution shift.

Currently developing research in uncertainty quantification for neural networks, scalable approximate inference, and generative models for structured data. I approach problems through the lens of information theory and statistical mechanics—seeking to understand fundamental limits and tradeoffs in learning systems.

I combine rigorous mathematical analysis with empirical investigation. Every research direction is grounded in specific technical problems, not aspirational goals. My approach privileges understanding over adoption—diving deep into fundamental questions about learning systems rather than chasing trends.