Houman Owhadi
IBM Professor of Applied and Computational Mathematics and Control and Dynamical Systems
B.S., Ecole Polytechnique (France), 1994; M.S., Ecole Nationale des Ponts et Chaussees, 1997; Ph.D., Ecole Polytechnique Federale de Lausanne (Switzerland), 2001. Assistant Professor, Caltech, 2004-11; Professor, 2011-25; IBM Professor, 2025-.
Statistical Numerical Approximation. Numerical homogenization. Operator adapted wavelets. Fast solvers. Gaussian process regression. Machine learning. Multi-scale and stochastic analysis. Stochastic mechanics and geometric integration. Uncertainty Quantification.
Overview
Professor Owhadi's research concerns the exploration of interplays between numerical approximation, statistical inference and learning from a game theoretic perspective. Whereas the process of discovery is usually based on a combination of trial and error, insight and plain guesswork, his research is motivated by the facilitation/automation possibilities emerging from these interplays.
Related Courses
2022-23
ACM 118 – Stochastic Processes and Regression
ACM/IDS 216 – Markov Chains, Discrete Stochastic Processes and Applications
2021-22
ACM 118 – Stochastic Processes and Regression
ACM/IDS 216 – Markov Chains, Discrete Stochastic Processes and Applications
2020-21
ACM 118 – Stochastic Processes and Regression
ACM/IDS 216 – Markov Chains, Discrete Stochastic Processes and Applications