Bio
I am a senior teaching faculty member at Columbia University, focusing on Machine Learning, Algorithms and Theory. My primary area of research is Machine Learning and High-dimensional Statistics.
Previously, I worked at Janelia Research Campus, HHMI as a Research Specialist developing statistical techniques to quantitatively analyze neuroscience data. I have also worked at Amazon as a Research Scientist developing risk assessment models for real-time fraud detection.
I received my PhD in Computer Science from UC San Diego specializing in Machine Learning.
Teaching
Current Courses (Spring 2024)
Research
I enjoy working on various aspects of machine learning problems and high-dimensional statistics. I am especially interested in understanding and exploiting the intrinsic structure in data (eg. manifold or sparse structure) to design effective learning algorithms. Here is a representative list of my publications.
Selected Publications
- Time-accuracy tradeoffs in Kernel prediction: controlling prediction quality Journal of Machine Learning Research (JMLR), 2017 pdf
- Sample complexity of learning Mahalanobis distance metrics Neural Information Processing Systems (NIPS), 2015 pdf
- Distance preserving embeddings for general n-dimensional manifolds Journal of Machine Learning Research (JMLR), 2013 pdf