My research focuses on developing new methods that enable efficient machine learning from massive datasets. More specifically, I am interested in designing techniques that can gain insights from the underlying data structure by utilizing complex and higher-order interactions between data points. The extracted information can be used to efficiently explore and robustly learn from datasets that are too large to be dealt with by traditional approaches. My methods have immediate application to high-impact problems where massive data volumes prohibit efficient learning and inference, such as huge image collections, recommender systems, Web and social services, video and other large data streams.
Before joining UCLA, I was a postdoctoral research fellow in Computer Science at Stanford University working with Jure Leskovec. I received my Ph.D. in Computer Science from ETH Zurich advised by Andreas Krause. I received an ETH medal for Outstanding Doctoral Thesis, and was selected as a Rising Star in EECS by MIT.