Matthias Poloczek

Assistant Professor

Educational Background

Doctor of Philosophy, Computer Science, Goethe-University Frankfurt am Main, 2013

Research & Teaching Interests

Design and analysis of algorithms at the interface of machine learning and optimization. Specifically, algorithms that are fast in practice and provide provable performance guarantees. Optimal learning research to optimally trade-off exploration vs. exploitation for sequential decision-making under uncertainty. This research in particular deals with development of Bayesian optimization algorithms for simulation optimization and the design of experiments with applications in aerospace engineering, biochemistry, chemical engineering, materials science and medicine.

Other research includes investigation of the approximation algorithms for combinatorial problems that allow very fast implementations and thus scale to large data, and inherent limitations of the greedy paradigm, i.e., the price of making decisions myopically.

Awards and Honors

Alexander von Humboldt-Stiftung, Feodor Lynen Research Fellowship for Postdoctoral Researchers

Deutscher Akademischer Austauschdienst, Research Grant for Postdoctoral Researchers, Declined in favor of the Feodor Lynen fellowship

Award for outstanding academic performance and for the best PhD in computer science, Goethe-Universität Frankfurt am Main, 2013

Award for outstanding academic performance and the best diploma in computer science, Goethe-Universität Frankfurt am Main, 2008

Professional Societies and Activities

Program Committee, the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), February 2–7 2018, New Orleans, Louisiana

Co-organizer, SIAM minisymposium on “Multi-fidelity and Multi-information Source Methods,” 2017 SIAM Conference on Computational Science and Engineering, February 27 - March 3 2017, Atlanta, Georgia

University of Arizona College of Engineering