Ambuj Tewari

(734) 615-0928

Applications:
Behavioral Science, Computer Science, Healthcare Research
Methodologies:
Artificial Intelligence, Computing, Data Mining, Graph-Based Methods, Machine Learning, Mathematical and Statistical Modeling, Networks, Optimization, Statistics
Relevant Projects:

NSF, NIH


Connections:

Peers Health

Ambuj Tewari

Professor

Statistics, LSA
EECS, College of Engineering

Professor of Statistics, College of Literature, Science, and the Arts and Professor of Electrical Engineering and Computer Science, 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