About

Martin Takáč

Associate Professor and Deputy Department Chair of Machine Learning at MBZUAI, working on optimization, scientific AI, energy AI, and reliable LLM reasoning.

Optimization AI for Science AI for Energy LLM Reasoning
Short bio

Martin Takáč is an Associate Professor and the Deputy Department Chair of Machine Learning at the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) in the UAE. Prior to joining MBZUAI, he was an Associate Professor in the Department of Industrial and Systems Engineering at Lehigh University, where he had been on faculty since 2014.

He earned his B.S. (2008) and M.S. (2010) degrees in Mathematics from Comenius University, Slovakia, and his Ph.D. (2014) in Mathematics from the University of Edinburgh, United Kingdom.

His research focuses on the development and analysis of algorithms for machine learning, AI-driven scientific discovery, protein-DNA interaction studies, and the application of machine learning to energy systems.

He has received funding from multiple U.S. National Science Foundation programs, including a TRIPODS Institute grant in collaboration with Lehigh, Northwestern, and Boston University. Recently, he was also awarded three grants in partnership with the Weizmann Institute of Science.

For invited talks

Plain short bio

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Martin Takáč is an Associate Professor and Deputy Department Chair of Machine Learning at the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI). His research develops optimization algorithms and AI systems for machine learning, scientific discovery, energy applications, and reliable LLM reasoning. Before joining MBZUAI, he was a faculty member at Lehigh University. He received his Ph.D. in Mathematics from the University of Edinburgh and his B.S. and M.S. degrees in Mathematics from Comenius University, Slovakia. His awards include the Best Ph.D. Dissertation Award by the OR Society, the Leslie Fox Prize second prize, the INFORMS Computing Society Best Student Paper runner-up, and the Charles Broyden Prize. His research has been supported by multiple U.S. National Science Foundation programs, including a TRIPODS Institute grant, and by grants in partnership with the Weizmann Institute of Science. He has served as an Associate Editor for Mathematical Programming Computation, Journal of Optimization Theory and Applications, and Optimization Methods and Software, as Area Chair for AISTATS, NeurIPS, and ICLR, and as Senior Area Chair for ICML and NeurIPS.

Awards

Best Ph.D. Dissertation Award by the OR Society, Leslie Fox Prize second prize, INFORMS Computing Society Best Student Paper runner-up, and the Charles Broyden Prize.

Editorial Service

Associate Editor for Mathematical Programming Computation, Journal of Optimization Theory and Applications, and Optimization Methods and Software.

Conference Service

Area Chair for AISTATS, NeurIPS, and ICLR, and Senior Area Chair for ICML and NeurIPS.

Research focus

Main directions

Research overview

Optimization

Stochastic, distributed, adaptive, and second-order methods for large-scale machine learning and modern AI systems.

Explore optimization

AI for Science

Machine learning for scientific discovery, including chemistry, biology, protein-DNA interaction studies, and structured scientific data.

Explore AI for Science

AI for Energy

Energy-domain LLMs, VLMs, causal reasoning, and physics-grounded decision support for exploration, production, and subsurface workflows.

Explore AI for Energy

LLM Work

Reasoning, prompting, multi-agent systems, evaluation, and domain adaptation for language models in specialized workflows.

Explore LLM work