Alan Qi

Assistant Professor at Purdue University in Computer Science and Statistics

Title:

Scalable Bayesian learning for complex and massive data

Abstract:

Computational analysis of complex data has become a driving force for scientific discovery and engineering applications. It is, however, often a challenging task due to the high dimensionality and the massive size of datasets. To address these challenges, we build sparse, relational, dynamic and nonparametric Bayesian models driven by various applications and develop efficient, scalable inference methods.  In this talk, (i) I will describe a novel sparse Bayesian model that integrates generative and conditional models to select correlated variables, such as whole genome SNPs. (This model addresses the p>>n problem where p is the number of variables and n is the number of data points); (ii) I will present a Bayesian online learning algorithm that, unlike previous approaches, learns a dynamic compact representation of massive data and make predictions accordingly (the n>>p problem); And (iii) I will describe a parallel Bayesian inference method on graphics processing units to extract latent topic and clusters from data with both a large number of variables and samples (both n and p are large).  In addition, I will present applications of these works, for example, in identifying genetic variations and biomarkers for the early diagnosis of Alzheimer’s disease, and modeling rare cell populations in flow cytometry data for the discovery of cancer stem cells.

Bio:

I obtained PhD from MIT in 2005 and worked as a postdoctoral researcher at MIT from 2005 to 2007. In 2007, I joined Purdue university as an Assistant Professor of Computer Science and Statistics. I 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.

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