We work on a wide range of machine learning problems that are motivated by challenges from modeling messy data in the wild. We develop new algorithms and theories in deep learning, unsupervised learning, robust ML, adaptive data analysis, etc. We are also very interested in applications in genomics, health and biotech. Our group is affiliated with Biomedical Data Science, CS and EE at Stanford and is a part of the Chan-Zuckerberg Biohub.
The latest news from our lab:
We are excited to be a pilot project of the Human Cell Atlas! Read More ›
Have you used PCA? Then you need to try our contrastive PCA. Read More ›