He holds a PhD degree from MIT EECS. His research interests include machine learning, natural language processing, time series analysis, and computational genomics, with a focus on medical applications. He proposed Subgraph Augmented Non-negative Tensor Factorization (SANTF) for building a clinical model that improves both accuracy and interpretability, by turning EHR into graph representations and applying tensor factorization to mining graph features. He has extended SANTF to time series analysis and computational genomics.