Yuan (Alan) Qi

Research Interest

My research interest lies in machine learning, Bayesian statistics, and their applications in various scientific and engineering disciplines — such as computational biology and computational materials science. I  address critical computational and statistical challenges associated with  massive data analysis, helping the study of complex biological, social, and synthetic systems. To this end, I design relational, sparse, and nonparametric Bayesian graphical models driven by various applications, and develop scalable Bayesian inference algorithms for these models.

Brief 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.

News

• Message passing with relaxed moment matching, Y. Qi, and Y. Guo, arXiv:1204.4166v1 [cs.LG], 2012.
• EigenGP: Gaussian processes with sparse data-dependent eigenfunctions, Y. Qi, B. Dai, and Y. Zhu, arXiv:1204.3972v1 [cs.LG], 2012.
• Infinite Tucker decomposition: nonparametric Bayesian models for multiway data analysis, Z. Xu, F. Yan, Y. Qi, Proceedings of ICML, 2012. [previous version: arXiv:1108.6296v2,[cs.LG]].
• Bayesian nonexhaustive learning for online discovery and modeling of emerging classes, M. Dundar, F Akova, Y. Qi, B. Rajwa, Proceedings of ICML, 2012.
• EigenNet: A Bayesian hybrid of generative and conditional models for sparse learning, Y. Qi, F. Yan, NIPS, 2011.
• t-divergence based approximate inference, N. Ding, S V N Vishwanathan, Y. Qi, NIPS, 2011.

My postdoc Zenglin Xu and I will give a tutorial on Probabilistic Matrix and Tensor Blockmodels for Network Modeling at AAAI 2012.

Our research in news! See the Marketwatch report on how our group uses Purdue's newest supercomputer to perform parallel Bayesian inference for the analysis of massive flow cytometry data with an end goal of identifying rare cancer stem cells.

Our project "Scalable Bayesian learning for multi-source and multi-aspect data" now is supported by the NSF CAREER award.

Our group co-organized the workshop of Machine Learning for Social Computing at NIPS 2010.

Assistant Professor

Department of Computer Science

Department of Statistics

Department of Biology (by courtesy)

Purdue University