Research Overview

My primary research interests lie in machine learning, particularly at the intersection of applied probability, dynamical systems, and modern neural networks. While my work focuses on leveraging foundational questions to advance the science and engineering of learning systems, the theory is closely connected to implementation challenges and a broad spectrum of practical applications across the sciences.

Some central themes of interest:

• Generative AI: how to improve existing generative modeling methods, particularly for learning sequences, using tools from dynamical systems
• Sequence modeling: how to use ML to model and learn sequences, particularly those with underlying non-trivial dynamics
• Robustness: how to leverage randomness to make ML safer and more reliable
• Optimization: how to understand and improve optimization algorithms in ML meaningfully