Data are being generated at an unprecedented pace in various scientific and engineering areas including biomedical engineering, and materials science, and social science. These data provide us with precious opportunities to reveal hidden relationships in natural or synthetic systems and predict their functions and properties. With growing complexity, however, the data impose new computational challenges—for example, how to handle the high dimensionality, nonlinear interactions, and the massive volume of the data.
To address these new challenges, I have been developing advanced Bayesian models to capture the data complexity and designing scalable algorithms to learn the models efficiently from data. In this talk, I will describe two of my recent works along this line: 1) efficient learning of novel nonparametric models on tensors to discover communities in social networks and parse noisy email networks, and 2) parallel inference for new hierarchical Bayesian models to identify rare cancer stem cells from massive single-cell tensor-valued data. I will present experimental results on real world data — demonstrating the superior predictive performance of the proposed approaches — and discuss other applications of these approaches such as in patient drug response analysis and neuroscience.
Speaker Biography
Alan Qi obtained his PhD from MIT in 2005 and worked as a postdoctoral researcher at MIT from 2005 to 2007. In 2007, he joined Purdue University as an assistant professor of Computer Science and Statistics (and Biology by courtesy). He received the A. Richard Newton Breakthrough Research Award from Microsoft Research in 2008, the Interdisciplinary Award from Purdue University in 2010, and the NSF CAREER award in 2011. His research interest lies in scalable Bayesian learning and their applications. His group not only develops sparse, nonparametric, and dynamic learning algorithms, but also collaborates with domain experts— such as biomedical researchers at Harvard and Purdue, materials scientists at MIT, and psychologists at Toronto University— for a wide range of scientific and engineering applications.