This is my old home page.
I have moved to Purdue. Shortly, you should automatically
be taken to my new homepage. If not, please follow the link
below:
http://www.cs.purdue.edu/~alanqi
Research
Description
Machine learning, Computational and systems biology, and Bayesian
inference
Papers
Cortical Surface Shape
Analysis Based
on Spherical Wavelets, P. Yu, P. E. Grant, Y. Qi, X. Han, F.
Segonne, R. Pienaar, E. Busa, J. Pacheco, N. Makris, R. L. Buckner, P.
Golland, and B. Fischl, IEEE Transaction on Medical
Imaging, 26(4):582-597, 2007. [html]
Parameter
Expanded Variational
Bayesian Methods,
Y. Qi and T.S. Jaakkola, in Advances in Neural Information
Processing Systems 19, MIT Press, Cambridge, MA, 2007. [pdf]
Expectation Propagation for Signal Detection in Flat-fading
Channels,
Yuan Qi and Thomas Minka, in IEEE transactions on Wireless
Communications, vol. 6, no. 1, 348-355, 2007. [Preprint][IEEE notice] Obsolete
version: MIT Media Lab Technical Report
Vismod-TR-555.
Abstract of the previous version appears in the Proceedings of IEEE
International Symposium on
Information
Theory, 2003, Yokohama, Japan.
An
efficient
fixed-lag smoothing algorithm for hybrid dynamic Bayesian networks with
its application to wireless communications.
Modularity and Dynamics of Cellular
Networks, Y. Qi and H. Ge, PLoS
Computational Biology, vol. 2, no. 12, 1502-1510, December, 2006. [html
/ pdf]
High-resolution Computational Models
of Genome Binding Events,
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, Nature Biotechnology, vol. 24, 963-970,
August,
2006. [link].
Approximate Expectation Propagation
for Bayesian Inference on Large-scale Problems, Y. Qi, T. S.
Jaakkola, and D.K. Gifford, CSAIL Tech Report, [pdf].
EP inference on large networks for protein-DNA binding detection.
Semi-supervised Analysis of Gene
Expression
Profiles for Lineage-specific Development in the Caenorhabditis Elegans Embryo, Yuan
(Alan)
Qi, Patrycja E. Missiuro, Ashish Kapoor, Craig P. Hunter, Tommi S.
Jaakkola, David K. Gifford and Hui Ge, Bioinformatics, vol. 22,
no. 14, e417-e423, 2006. [Abstract]
and [pdf].
Bayesian Conditional Random
Fields,
Yuan Qi, Martin Szummer, and Thomas P. Minka, to
appear in Journal of Machine Learning Research.
Hyperparameter and Kernel Learning for Graph Based Semi-Supervised
Classification, Ashish Kapoor, Yuan (Alan) Qi, Hyungil Ahn, and
Rosalind W. Picard, Advances in Neural Information Processing Systems
18, MIT Press, Cambridge, MA, 2006.
Diagram
Structure Recognition by Bayesian Conditional Random Fields,
Yuan Qi, Martin Szummer, and Thomas P. Minka, in the
Proceedings of
International Conference on Computer
Vision and Pattern Recognition, 2005.
[pdf/ps]
Introducing Automatic Relevance Determination to CRFs for
feature selection and using contextual information for joint object
recognition.
Extending
Expectation Propagation for Graphical Models, Yuan
Qi, Ph.D. thesis, MIT, 2005. [pdf]
Bayesian
Conditional
Random Fields, Yuan Qi, Martin Szummer, and Thomas P. Minka, in the
proceedings of AISTATS 2005. [
paper/pdf]
Predictive Automatic Relevance
Determination by Expectation Propagation, Yuan Qi, Thomas P. Minka,
Rosalind W. Picard, and Zoubin Ghahramani, in the Proceedings of
Twenty-first International
Conference on Machine Learning, July 4-8, 2004, Banff, Alberta, Canada.
[paper/pdf]
and [slides/ppt]
Bayesian
sparse classifiers, which were applied to gene expression
classification.
Tree-structured
Approximations
by Expectation Propagation, Thomas Minka and Yuan Qi, Advances in
Neural Information Processing Systems 16, 2004. [
pdf]
An
efficient inference algorithm for loopy graphs.
Questions and answers about philosophy of
science,
causation, and human/machine learning, Yuan Qi, October
2002,
[pdf/ps].
Hessian-based
Markov Chain Monte-Carlo Algorithms, Yuan Qi and
Thomas P. Minka, First Cape Cod Workshop on Monte Carlo Methods, Cape
Cod, Massachusetts, September,
2002. [
slides/ps]
Combining
optimization techniques with MCMC leads to new fast sampling methods
(HMH
and AMIT).
Context-sensitive Bayesian Classifiers and Application
to Mouse Pressure Pattern Classification, Yuan Qi, and
Rosalind
W. Picard, in the proceedings of International Conference on Pattern
Recognition, Québec City, Canada, August 2002. [slide/ps]
and [Paper/pdf].
A simple
probabilistic way to combine multiple classifiers which are trained on
different subsets of a given training set.
Bayesian Spectrum Estimation of Unevenly Sampled Nonstationary Data,
Yuan Qi, Thomas P. Minka, and Rosalind W. Picard, MIT Media Lab
Technical
Report Vismod-TR-556. [Abstract]
and [Paper/pdf].
Check out this web
page
that summarizes experimental results, including comparison with
classical
methods, e.g., Multitaper methods. The short version of this paper does
not include sparsification techniques and appears in ICASSP 02,
Orlando, Florida, May
2002. [Poster/pdf]
and [Paper/pdf].
Hybrid Independent Component Analysis and Support Vector Machine
Learning Scheme for Face Detection, Y. Qi, D. DeMenthon, and D.
Doermann,
International Conference on Acoustics, Speech, and Signal Processing
(ICASSP01),
Salt Lake City,Utah, May, 2001. [ps]
Learning Algorithms for Video and Audio Processing: Independent
Component
Analysis and Support Vector Machine based Approaches, Yuan
Qi,
Technical Report LAMP-TR-056, CAR-TR-951, CS-TR-4174, Center for
Automation
Research, University of Maryland at College Park, August, 2000.
Subband-based Independent Component Analysis, Yuan Qi, S.A.
Shamma,
P.S. Krishnaprasad, in the proceedings of ICA2000, Helsinki,
Finland, June 2000.
Selected Presentations
Extending expectation
propagation
for graphical models, CMU CALD Machine learning lunch, April, 2004
Bayesian learning
for conditional models, MIT CSAIL seminar, September, 2005
Software
The software package Joint Binding
Deconvolution (JBD) accompanying the paper
"High-resolution Computational Models
of Genome Binding Events", [download].
Matlab implementation of our new spectrum estimation algorithm, [download]
.
Teaching
I was a teaching assistant for MAS
622J Pattern Recognition in 2002. Besides my TA duty, I also did
guest
lectures on Kalman filtering and smoothing, Junction tree algorithm,
and
Bayesian point machines.
Some photos I have taken in Brazil (Amazon, Iguazu Falls), Spain
(Valencia, Madrid, and
Barcelona),
Japan (Kyoto and Tokyo), and US (Yellowstone).
Last modified: Jan 12, 2007
