Assistant Professor in the Statistics Department (by courtesy) 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.

Here is a note for prospective students that are considering working with me.

CS 37300: Data Mining And Machine Learning: Fall 2018

CS 59000-HLT: Hands-On Learning Theory: Fall 2018, also offered on Fall 2017, Fall 2016 and Fall 2015

CS 57800: Statistical Machine Learning: Spring 2018, also offered on Fall 2017 and Fall 2016

CS 52000: Computational Methods In Optimization: Spring 2016

Ghoshal A.,

(Under submission.)

Exact Inference in Structured Prediction. (Preprint)

Bello K.,

(Under submission.)

Minimax Bounds for Structured Prediction. (Preprint)

Bello K., Ghoshal A.,

(Under submission.)

Learning Bayesian Networks with Low Rank Conditional Probability Tables. (Preprint)

Barik A.,

(Under submission.)

Exact Recovery in the Latent Space Model. (Preprint)

Ke C.,

(Under submission.)

On the Correctness and Sample Complexity of Inverse Reinforcement Learning. (Preprint)

Komanduru A.,

(Under submission.)

Information Theoretic Limits for Standard and One-Bit Compressed Sensing with Graph-Structured Sparsity. (Preprint)

Barik A.,

(Under submission.)

Learning Discrete Bayesian Networks in Polynomial Time and Sample Complexity. (Preprint)

Barik A.,

(Under submission.)

Regularized Loss Minimizers with Local Data Perturbation: Consistency and Data Irrecoverability. (Preprint)

Li Z.,

(Under submission.)

Reconstructing a Bounded-Degree Directed Tree Using Path Queries. (Preprint)

Wang Z.,

(Under submission.)

The Error Probability of Random Fourier Features is Dimensionality Independent. (Preprint)

Li Y.,

(Under submission.)

Optimality Implies Kernel Sum Classifiers are Statistically Efficient.

Meyer R.,

Cost-Aware Learning for Improved Identifiability with Multiple Experiments.

Guo L.,

Bello K.,

Learning Causal Bayes Networks Using Interventional Path Queries in Polynomial Time and Sample Complexity.

Bello K.,

Information-Theoretic Limits for Community Detection in Network Models.

Ke C.,

Statistically and Computationally Efficient Variance Estimator for Kernel Ridge Regression.

Liu M.,

Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time.

Ghoshal A.,

Learning Linear Structural Equation Models in Polynomial Time and Sample Complexity.

Ghoshal A.,

Learning Sparse Polymatrix Games in Polynomial Time and Sample Complexity.

Ghoshal A.,

On the Statistical Efficiency of Compositional Nonparametric Prediction.

Xu Y.,

Ghoshal A.,

On the Sample Complexity of Learning Graphical Games.

Information Theoretic Limits for Linear Prediction with Graph-Structured Sparsity.

Barik A.,

Learning Graphical Games from Behavioral Data: Sufficient and Necessary Conditions.

Ghoshal A.,

Information-Theoretic Limits of Bayesian Network Structure Learning.

Ghoshal A.,

Ghoshal A.,

Structured Prediction: From Gaussian Perturbations to Linear-Time Principled Algorithms.

Information-Theoretic Lower Bounds for Recovery of Diffusion Network Structures.

Park K.,

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, 2016)

Technical report. [code]

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

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.,

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.,