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. |

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).

(Under submission.)

Learning the Structure and Parameters of Large-Population Graphical Games from Behavioral Data.

Simultaneous and Group-Sparse Multi-Task Learning of Gaussian Graphical Models. (Preprint)

Variable Selection in Gaussian Markov Random Fields.

Invited book chapter in

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.,

Integration of PCA with a Novel Machine Learning Method for Reparameterization and Assisted History Matching Geologically Complex Reservoirs.

Tight Bounds for the Expected Risk of Linear Classifiers and PAC-Bayes Finite-Sample Guarantees.

Classification on Brain Functional Magnetic Resonance Imaging: Dimensionality, Sample Size, Subject Variability and Noise.

Invited book chapter in

Edited by Chen C. (to be published on December, 2014)

Improving Interpretability of Graphical Models in fMRI Analysis via Variable-Selection.

Predicting Cross-task Behavioral Variables from fMRI Data Using the k-Support Norm.

Misyrlis M., Konova A., Blaschko M.,

Medical Image Computing and Computer-Assisted Intervention.

Methylphenidate Enhances Executive Function and Optimizes Prefrontal Function in Both Health and Cocaine Addiction.

Moeller S.,

Integration of Principal Component Analysis and Streamline Information for the History Matching of Channelized Reservoirs.

Chen C., Gao G.,

Two-Sided Exponential Concentration Bounds for Bayes Error Rate and Shannon Entropy.

fMRI Analysis of Cocaine Addiction Using k-Support Sparsity.

Gkirtzou K.,

fMRI Analysis with Sparse Weisfeiler-Lehman Graph Statistics.

Gkirtzou K.,

Medical Image Computing and Computer-Assisted Intervention,

Variable Selection for Gaussian Graphical Models.

Can a Single Brain Region Predict a Disorder?

Two-person Interaction Detection Using Body-Pose Features and Multiple Instance Learning.

Yun K.,

IEEE Computer Vision and Pattern Recognition,

Dopaminergic Involvement During Mental Fatigue in Health and Cocaine Addiction.

Moeller S., Tomasi D.,

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.,

Digital Analysis and Visualization of Swimming Motion.

Kirmizibayrak C.,

Digital Analysis and Visualization of Swimming Motion.

Kirmizibayrak C.,

Conference on Computer Animation and Social Agents,

Dopaminergic contribution to endogenous motivation during cognitive control breakdown.

Moeller S., Tomasi D.,

Simple Fully Automated Group Classification on Brain fMRI.

Disrupted Functional Connectivity with Dopaminergic Midbrain in Cocaine Abusers.

Tomasi D., Volkow N., Wang R.,

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.,

Learning Brain fMRI Structure Through Sparseness and Local Constancy.

Neural Information Processing Systems,

A Functional Geometry of fMRI BOLD Signal Interactions.

Langs G., Samaras D., Paragios N.,

Neural Information Processing Systems,

Dopaminergic Response to Drug Words in Cocaine Addiction.

Goldstein R., Tomasi D., Alia-Klein N.,

Anterior Cingulate Cortex Hypoactivations to an Emotionally Salient Task in Cocaine Addiction.

Goldstein R., Alia-Klein N., Tomasi D.,

Langs G., Samaras D., Paragios N.,