Ambuj Tewari

(734) 615-0928

Behavioral Science, Computer Science, Human Subjects Trials and Intervention Studies, Mental Health, Mobile Devices, Networks, Precision Health
Artificial Intelligence, Classification, Computational Tools for Data Science, Dynamical Models, Graph Theory and Graph-based Methods, High-Dimensional Data Analysis, Longitudinal Data Analysis, Machine Learning, Network Analysis, Optimization, Pattern Analysis and Classification, Predictive Modeling, Real-time Data Processing, Statistical Inference, Statistical Modeling, Statistics, Time Series Analysis
Relevant Projects:



Peers Health

Ambuj Tewari

Assistant Professor

Statistics, LSA
EECS, College of Engineering

My research group is engaged in fundamental research in the following areas: Statistical learning theory: We are developing theory and algorithms for predictions problems (e.g., learning to rank and multilabel learning) with complex label spaces and where the available human supervision is often weak. Sequential prediction in a game theoretic framework: We are trying to understand the power and limitations of sequential predictions algorithms when no probabilistic assumptions are placed on the data generating mechanism. High dimensional and network data analysis: We are developing scalable algorithms with provable performance guarantees for learning from high dimensional and network data. Optimization algorithms: We are creating incremental, distributed and parallel algorithms for machine learning problems arising in today’s data rich world. Reinforcement learning: We are synthesizing concepts and techniques from artificial intelligence, control theory and operations research for pushing the frontier in sequential decision making with a focus on delivering personalized health interventions via mobile devices. My research group is pursuing and continues to actively search for challenging machine learning problems that arise across disciplines including behavioral sciences, computational biology, computational chemistry, learning sciences, and network science.

Research to deliver personalized interventions in real-time via people's mobile devices

Research to deliver personalized interventions in real-time via people’s mobile devices