Purdue University - Department of Computer Science -

Yuan Qi

Adjunct Associate Professor of Computer Science

Joined department: Fall 2007


PhD, MIT Media Lab, (2005)

Dr. Alan Qi has a joint position in Departments of Computer Science and Statistics. His main research interests include Bayesian learning, computational biology, and other applications of machine learning. Dr. Qi received his PhD in Media Laboratory from the Massachusetts Institute of Technology (MIT) in 2005 and worked a postdoctoral associate at MIT Computer Science and Artificial Intelligence Laboratory from 2005 to 2007.

Dr. Qi has worked extensively on probabilistic graphical models and Bayesian inference. For his Ph.D. research, he worked with T. P. Minka to develop Expectation Propagation algorithms on a variety of graphical models including hybrid dynamic systems, Markov random fields, and conditional random fields. For his postdoctoral research, he collaborated with D.K. Gifford and T.S. Jaakkola on computational biology and Bayesian learning. His recent work focuses on efficient Bayesian methods, such as online learning and parallel inference, for massive data, Gaussian process (and other nonparametric Bayesian) approaches,relational learning on graphs and networks (e.g., biological and social networks), biological pathway modeling and analysis, and SNP analysis.

Dr. Qi has taught at the ACM International Conference on Multimedia, Campus IT Summer School in Spain, and at MIT. He also performed research at MIT, Gatsby unit at the University College London, and Microsoft Research.

He is currently supervising 8 Ph.D. students and a postdoc researcher. His research is supported by four NSF grants, a Showalter Trust award, and a Microsoft award.

Selected Publications

Y. Qi and T.S. Jaakkola, "Parameter Expanded Variational Bayesian Methods", Advances in Neural Information Processing Systems, 19, MIT Press, Cambridge, MA, 2007
Y. Qi, A. Rolfe, K. D. MacIsaac, G. K. Gerber, D. Pokholok, J. Zeitlinger, T. Danford, R. D. Dowell, E. Fraenkel, T. S. Jaakkola, R. A. Young and D. K. Gifford, "High-resolution Computational Models of Genome Binding Events", Nature Biotechnology, , vol. 24, 963-970, August, 2006
Y. Qi, M. Szummer, and T. P. Minka, "Diagram Structure Recognition by Bayesian Conditional Random Fields", Proceedings of International Conference on Computer Vision and Pattern Recognition, ,2005

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