Assistant Professor of Computer Science
Assistant Professor of Statistics
Assistant Professor of Biology (courtesy)
Joined department: Fall 2007
Education:
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 5 Ph.D. students. His research is supported by two 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.
Research Funding
Wojciech Szpankowski, Ruben Aguilar, Mikhail J. Atallah, Christopher Clifton, Supriyo Datta, Ananth
Y. Grama, Suresh Jagannathan, Jennifer Neville, Yuan Qi, and Doraiswami Ramkrishna, Emerging
Frontiers of Science of Information, National Science Foundation, 8/1/2010-7/31/2015.
Yuan Qi, A temporal view of the Plasmodium-red blood cell interactome, National Institutes
of Health, 4/1/2010-3/31/2014.
Yuan Qi, Career: Scalable Bayesian learning for multi-source and multi-aspect data, National
Science Foundation, 1/1/2011-12/31/2015.
Yuan Qi, CDI Type I: Collaborative Research: Integration of Relational Learning with ab-initio
methods for prediction of material properties, National Science Foundation, 1/1/2010-12/31/2012.
Yuan Qi, Development of Bayesian NGS Data Analysis Tools for Identifying Novel Somatic Spliing
Isoforms Using Tumor Normal Paired RNA-Seq Data, Eli Lilly and Company, 11/1/2011-3/30/2012.
Yuan Qi, RI:Small: Relational Learning and Inference for Network Models, National Science
Foundation, 9/1/2009-8/31/2012.
Last Updated: January 18, 2012 05:26pm
