Jean Honorio

Assistant Professor in the Computer Science Department at Purdue.
Lawson Building 2142-J, West Lafayette, IN 47907
e-mail: jhonorio at purdue.edu

Modern statistical problems are high dimensional (big data). My research in this area focus on developing computationally and statistically efficient algorithms, understanding their behavior using concepts such as convergence, sample complexity, and privacy, and designing new modeling paradigms such as models rooted in game theory. My theoretical and algorithmic work is directly motivated by, and contributes to, applications in political science (affiliation and influence), neuroscience (brain disorders such as addiction), and genetics (diseases such as cancer). [vita]

Prior to joining Purdue, I was a postdoctoral associate at MIT CSAIL, working with Tommi Jaakkola. My Erdös number is 3: Jean Honorio → Tommi Jaakkola → Noga Alon → Paul Erdös.

News

8/19/15. I moved to Purdue, please see my new webpage.
4/16/15. I will join the Computer Science Department at Purdue as an Assistant Professor in the Fall.
3/21/15. Travel schedule: 2/2 Purdue, 2/19 Rice, 2/26 MSU, 3/3 U Arizona, 3/10 NUS, 3/16 Vanderbilt, 3/24 Rutgers, 3/27 Stevens, 3/30 Stony Brook
10/7/14. I will be a Senior Program Committee Member at IJCAI 2015.
9/27/14. Best paper award for our paper "Predicting Cross-task Behavioral Variables from fMRI Data Using the k-Support Norm" at MICCAI/STMI.
9/23/14. Our manuscript "Learning the Structure and Parameters of Large-Population Graphical Games from Behavioral Data" was accepted at JMLR.
6/24/14. Presenting our paper "A Unified Framework for Consistency of Regularized Loss Minimizers" at ICML.

Code and data

games: Learning the structure/parameters of graphical games.
ising: Biased stochastic optimization for learning the structure/parameters of Ising models.
ggms: Regularizers for learning the structure/parameters of Gaussian graphical models (local constancy, multi-task, node selection, low rank).
simplec: Simple fully automated group classification on brain fMRI (various feature selection and classification techniques, including ours).
fmriksup and wlpyramid: k-support norm and graph kernels for fMRI analysis (from my co-author Katerina Gkirtzou).
kinect: Dataset for two-person interaction detection (from my co-author Kiwon Yun).

Publications (by year / by topic)

1. Statistical Machine Learning

Structured Prediction: From Gaussian Perturbations to Linear-Time Principled Algorithms. (Preprint)
Honorio J., Jaakkola T.
(Under submission.)

A Unified Framework for Consistency of Regularized Loss Minimizers.
Honorio J., Jaakkola T.
International Conference on Machine Learning. Beijing/China, 2014.

Tight Bounds for the Expected Risk of Linear Classifiers and PAC-Bayes Finite-Sample Guarantees.
Honorio J., Jaakkola T.
Artificial Intelligence and Statistics. Reykjavik/Iceland, 2014.

Two-Sided Exponential Concentration Bounds for Bayes Error Rate and Shannon Entropy.
Honorio J., Jaakkola T.
International Conference on Machine Learning. Atlanta, 2013.

2. Game Theory

Learning the Structure and Parameters of Large-Population Graphical Games from Behavioral Data.
Honorio J., Ortiz L.
Journal of Machine Learning Research, 16(Jun): pp. 1157-1210, 2015. [code]

3. Optimization

Convergence Rates of Biased Stochastic Optimization for Learning Sparse Ising Models.
Honorio J.
International Conference on Machine Learning. Edinburgh/Scotland, 2012. [code]

4. Graphical Models

Simultaneous and Group-Sparse Multi-Task Learning of Gaussian Graphical Models. (Preprint)
Honorio J., Samaras D.
Journal of Machine Learning Research. (submitted on April 23, 2012, resubmission in preparation) [code]

Inverse Covariance Estimation for High-Dimensional Data in Linear Time and Space: Spectral Methods for Riccati and Sparse Models.
Honorio J., Jaakkola T.
Uncertainty in Artificial Intelligence. Washington, 2013. [code]

Variable Selection for Gaussian Graphical Models.
Honorio J., Samaras D., Rish I., Cecchi G.
Artificial Intelligence and Statistics. Canary Islands/Spain, 2012. [code]

Lipschitz Parametrization of Probabilistic Graphical Models.
Honorio J.
Uncertainty in Artificial Intelligence. Barcelona/Spain, 2011.

Multi-Task Learning of Gaussian Graphical Models.
Honorio J., Samaras D.
International Conference on Machine Learning. Haifa/Israel, 2010. [code]

Sparse and Locally Constant Gaussian Graphical Models.
Honorio J., Ortiz L., Samaras D., Paragios N., Goldstein R.
Neural Information Processing Systems. Vancouver/Canada, 2009. [code]

Learning Brain fMRI Structure Through Sparseness and Local Constancy.
Honorio J., Ortiz L., Samaras D., Goldstein R.
Neural Information Processing Systems, Workshop on Connectivity Inference in NeuroImaging. Vancouver/Canada, 2009.

5. Machine Learning for Neuroscience

Variable Selection in Gaussian Markov Random Fields.
Honorio J., Samaras D., Rish I., Cecchi G.
Invited book chapter in Log-Linear Models, Extensions and Applications.
Edited by Aravkin A., Deng L., Heigold G., Jebara T., Kanevski D., Wright S. (to be published on December, 2015)

Predictive Sparse Modeling of fMRI Data for Improved Classification, Regression, and Visualization Using the k-Support Norm.
Belilovsky E., Gkirtzou K., Misyrlis M., Konova A., Honorio J., Alia-Klein N., Goldstein R., Samaras D., Blaschko M.
Computerized Medical Imaging and Graphics. 2015. (accepted, pending publication.)

Classification on Brain Functional Magnetic Resonance Imaging: Dimensionality, Sample Size, Subject Variability and Noise.
Honorio J.
Invited book chapter in Frontiers of Medical Imaging.
Edited by Chen C. (to be published on December, 2014)

Improving Interpretability of Graphical Models in fMRI Analysis via Variable-Selection.
Honorio J., Samaras D., Rish I., Cecchi G.
Organization for Human Brain Mapping, Anual Meeting. Hamburg/Germany, 2014.

Predicting Cross-task Behavioral Variables from fMRI Data Using the k-Support Norm. (Best paper award)
Misyrlis M., Konova A., Blaschko M., Honorio J., Alia-Klein N., Goldstein R., Samaras D.
Medical Image Computing and Computer-Assisted Intervention. Workshop on Sparsity Techniques in Medical Imaging. Boston, 2014.

fMRI Analysis of Cocaine Addiction Using k-Support Sparsity.
Gkirtzou K., Honorio J., Samaras D., Goldstein R., Blaschko M.
IEEE International Symposium on Biomedical Imaging. California, 2013. [code]

fMRI Analysis with Sparse Weisfeiler-Lehman Graph Statistics.
Gkirtzou K., Honorio J., Samaras D., Goldstein R., Blaschko M.
Medical Image Computing and Computer-Assisted Intervention, Workshop on Machine Learning in Medical Imaging. Nagoya/Japan, 2013. [code]

Can a Single Brain Region Predict a Disorder?
Honorio J., Tomasi D., Goldstein R., Leung H.C., Samaras D.
IEEE Transactions on Medical Imaging, 31(11): pp. 2062-2072, 2012. [code]

Simple Fully Automated Group Classification on Brain fMRI.
Honorio J., Samaras D., Tomasi D., Goldstein R.
IEEE International Symposium on Biomedical Imaging. Rotterdam/The Netherlands, 2010. [code]

A Functional Geometry of fMRI BOLD Signal Interactions.
Langs G., Samaras D., Paragios N., Honorio J., Golland P., Alia-Klein N., Tomasi D., Volkow N., Goldstein R.
Neural Information Processing Systems, Workshop on Connectivity Inference in NeuroImaging. Vancouver/Canada, 2009.

Task-Specific Functional Brain Geometry from Model Maps.
Langs G., Samaras D., Paragios N., Honorio J., Alia-Klein N., Tomasi D., Volkow N., Goldstein R.
Medical Image Computing and Computer-Assisted Intervention. New York, 2008.

6. Neuroscience

Methylphenidate Enhances Executive Function and Optimizes Prefrontal Function in Both Health and Cocaine Addiction.
Moeller S., Honorio J., Tomasi D., Parvaz M., Woicik P., Volkow N., Goldstein R.
Cerebral Cortex, 24(3): pp. 643-653, 2014.

Dopaminergic Involvement During Mental Fatigue in Health and Cocaine Addiction.
Moeller S., Tomasi D., Honorio J., Volkow N., Goldstein R.
Translational Psychiatry, 2: e176, 2012.

Enhanced Midbrain Response at 6-month Follow-up in Cocaine Addiction, Association with Reduced Drug-related Choice.
Moeller S., Tomasi D., Woicik P., Maloney T., Alia-Klein N., Honorio J., Telang F., Wang G., Wang R., Sinha R., Carise D., Astone-Twerell J., Bolger J., Volkow N., Goldstein R.
Addiction Biology, 17(6): pp. 1013-25, 2012.

Dopaminergic contribution to endogenous motivation during cognitive control breakdown.
Moeller S., Tomasi D., Honorio J., Volkow N., Goldstein R.
Society for Neuroscience. Washington DC, 2011.

Disrupted Functional Connectivity with Dopaminergic Midbrain in Cocaine Abusers.
Tomasi D., Volkow N., Wang R., Honorio J., Maloney T., Alia-Klein N., Woicik P., Telang F., Goldstein R.
Public Library of Science, PLoS ONE, 5(5): e10815, 2010.

Oral Methylphenidate Normalizes Cingulate Activity in Cocaine Addiction During a Salient Cognitive Task.
Goldstein R., Woicik P., Maloney T., Tomasi D., Alia-Klein N., Shan J., Honorio J., Samaras D., Ruiliang W., Telang F., Wang G., Volkow N.
Proceedings of the National Academy of Sciences, 107(38): pp. 16667-72, 2010.

Dopaminergic Response to Drug Words in Cocaine Addiction.
Goldstein R., Tomasi D., Alia-Klein N., Honorio J., Maloney T., Woicik P., Wang R., Telang F., Volkow N.
Journal of Neuroscience, 29(18): pp. 6001-6, 2009.

Anterior Cingulate Cortex Hypoactivations to an Emotionally Salient Task in Cocaine Addiction.
Goldstein R., Alia-Klein N., Tomasi D., Honorio J., Maloney T., Woicik P., Wang R., Telang F., Volkow N.
Proceedings of the National Academy of Sciences, 106(23): pp. 9453-8, 2009.

7. Petroleum Engineering

Integration of PCA with a Novel Machine Learning Method for Reparameterization and Assisted History Matching Geologically Complex Reservoirs.
Honorio J., Chen C., Gao G., Du K., Jaakkola T.
Society of Petroleum Engineers: 91th Annual Technical Conference and Exhibition. Houston, 2015.

Integration of Principal Component Analysis and Streamline Information for the History Matching of Channelized Reservoirs.
Chen C., Gao G., Honorio J., Gelderblom P., Jimenez E., Jaakkola T.
Society of Petroleum Engineers: 90th Annual Technical Conference and Exhibition. Amsterdam/The Netherlands, 2014.

8. Computer Vision and Graphics

Two-person Interaction Detection Using Body-Pose Features and Multiple Instance Learning.
Yun K., Honorio J., Chattopadhyay D., Berg T., Samaras D.
IEEE Computer Vision and Pattern Recognition, Workshop on Human Activity Understanding from 3D Data. Rhode Island, 2012. [data]

Digital Analysis and Visualization of Swimming Motion.
Kirmizibayrak C., Honorio J., Jiang X., Mark R., Hahn J.
The International Journal of Virtual Reality, 10(3): pp. 9-16, 2011.

Digital Analysis and Visualization of Swimming Motion.
Kirmizibayrak C., Honorio J., Jiang X., Mark R., Hahn J.
Conference on Computer Animation and Social Agents, Simulation of Sports Motion Workshop. Chengdu/China, 2011.