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
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.
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
- 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
- Here are some slides of my recent talks:
Gaussian process models from big data,
University of Cambridge, 2012.
It covers my recent work on Bayesian tensor
decomposition, nonparametric stochastic
blockmodels, and EigenGPs (where GP meets
learning with big data, UCL, 2012.
It covers Bayesian online learning (virtual vector
machine) and EigenGP.
Bayesian learning for complex data, CSE
of Georgia Institute of Technology, 2011. It covers
EigenNet (selecting correlated variables), virtual
vector machine, and parallel inference of LDA on
- 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
- 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
- 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,
- 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
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
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