Hello, I’m Daniel.

I'm a second-year Ph.D. student in Statistics at Columbia University. My research interests broadly span the theory and practice of modern machine learning, including uncertainty quantification, generalization in deep learning, and the statistical foundations of AI. I'm especially interested in how ideas from high-dimensional statistics can inform the design and understanding of scalable learning systems.

Previously, I studied Computer Science and Mathematics at UC Berkeley, where I worked with Professor Shankar Sastry on computer vision and robotics. We built a scalable system for reconstructing table-tennis matches from monocular video and used it to train an uncertainty-aware controller that anticipates opponent actions, improving responses to high-speed hits in simulation.

Outside of research, I enjoy traveling, running, and playing basketball.