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. I joined Purdue university in 2007 and was tenured in 2013. 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.

To Applicants

If you are interested in joining my group and have passion in machine learning/statistics with a solid background in probability, linear algebra and/or optimization, welcome to contact me via email. Please attach your CV in your email.

Recent professional service

Action Editor, Journal of Machine Learning Research

Area Chair, International Conference on Machine Learning, 2014. 

News

-   Profs. N. Li and Y. Qi received a gift grant with from IBM TJ Watson Research Center to conduct research on big data for information security. [more info.]

-  Here are some slides of my recent talks:
 Recent papers:
  • DinTucker:  Scaling up Gaussian process models on multidimensional arrays with billions of elements, S. Zhe, Y. Qi, Y. Park, I.M. Molloy, and S. N Chari, arXiv 2013. A collaborative project between Purdue and IBM. [pdf]
  • EigenGP: sparse Gaussian process regression based on adaptive eigenfunctions, H. Peng, and Y. Qi, arXiv 2013. [Link]
  • Generating summary risk scores for mobile applications, C. Gates, N. Li, H. Peng, B. Sarma, Y. Qi, R. Potharaju, C. Nita-Rotaru, and I. Molloy, Accepted by IEEE Transactions on Dependable and Secure Computing.
  • Joint association discovery and diagnosis for Alzheimer's Disease by supervised heterogeneous multiview learning, S. Zhe, Z. Xu, Y. Qi, and P. Yu, in Proceedings of the Pacific Symposium on Biocomputing, The Big Island of Hawaii, 2014. [pdf]
  • Z. Xu, F. Yan, and Y. Qi, Bayesian Nonparametric Models for Multiway Data Analysis, to appear in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013 [preprint].
  • Joint network and node selection for pathway-based genomic data analysis,   Shandian Zhe, Syed A.Z. Naqvi, Yifan Yang, and Yuan Qi,  Bioinformatics, 2013.  [pdf]
  • Message Passing with relaxed moment matching,  Y. Qi, Y. Guo, ICML, 2013. [pdf]
  • Association Between hTERT rs2736100 Polymorphism and Sensitivity to Anti-cancer Agents, J. Kim, Y. Jones-Hall, R. Wei, J. Myers, Y. Qi, G.T. Knipp, and W. Liu, In Frontiers in Genetics, Volume 4, Number 162, 2013.
  • Distributed Autonomous Online Learning: Regrets and Intrinsic Privacy-Preserving Properties, F. Yan, S. Sundaram, S. V. N. Vishwanathan, and Y. Qi,  IEEE Transactions on Knowledge and Data Engineering, 2013.  [pdf]
  • Self-adjusting Models for Semi-supervised Learning in Partially-observed Settings, F. Akova, B. Rajwa, Y. Qi, and M. Dundar, IEEE International Conference on Data Mining, 2012. 
  • Using Probabilistic Generative Models For Ranking Risks of Android Apps, H. Peng, C. Gates, B. Sarma, N. Li, Y. Qi, R. Potharaju, C. Nita-Rotaru and I. Molloy, ACM Conference on Computer and Communications Security, Oct., 2012. [pdf]
  • Minimizing Private Data Disclosures in the Smart Grid, W. Yang, N. Li, Y. Qi, W. Qardaji, S. McLaughlin and P. McDaniel, ACM Conference on Computer and Communications Security, Oct., 2012. [pdf]
  • Infinite Tucker decomposition: nonparametric Bayesian models for multiway data analysis, Z. Xu, F. Yan, Y. Qi, Proceedings of ICML, 2012. [pdf]
  • Bayesian nonexhaustive learning for online discovery and modeling of emerging classes, M. Dundar, F Akova, Y. Qi, B. Rajwa, Proceedings of ICML, 2012. [pdf]
  • My postdoc Zenglin Xu and I  gave a tutorial on Probabilistic Matrix and Tensor models for Network and Multiway Data Analysis at AAAI 2012

    Our research is featured in Marketwatch news report and the August 2012 issue of Drug Development & Discovery magazine. The article "Bringing Stem Cells to the Forefront" describes the work of my group on Bayesian analysis of  massive  cytometric data on a supercomputer  for  rare cancer stem cell identification.

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

                  
Associate Professor 
Department of Computer Science
Department of Statistics
Department of Biology (by courtesy)
Purdue University