Martin Takac 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.

Martin’s 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. His work has earned him several prestigious awards, including the Best Ph.D. Dissertation Award by the OR Society (2014), the Leslie Fox Prize (2nd Prize; 2013) by the Institute for Mathematics and its Applications, the INFORMS Computing Society Best Student Paper Award (runner-up; 2012), and the Charles Broyden Prize for the best paper published in the 2022 volume of Optimization Methods and Software.

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.

Martin has served as an Associate Editor for journals such as Mathematical Programming Computation, Journal of Optimization Theory and Applications, and Optimization Methods and Software. Additionally, he has taken on the role of Area Chair for top conferences such as AISTATS, NeurIPS, and ICLR, and served as a Senior Area Chair for NeurIPS 2024.

Google Scholar
Semantic Scholar
LinkedIn
arXiv-e preprints

Workshops/Conferences

I have co-organized following workshops

1. HTiny Titans: The next wave of On-Device Learning for Foundational Models (TTODLer-FM) at ICML'25
2. HOO-2022 - Order up! The Benefits of Higher-Order Optimization in Machine Learning at NeurIPS'22
3. OPT2021: Optimization for Machine Learning at NeurIPS'21
4. OPT2020: Optimization for Machine Learning at NeurIPS'20

I have co-organized following conferences

1. MOPTA'16 - Modeling and Optimization: Theory and Applications
2. MOPTA'15 - Modeling and Optimization: Theory and Applications

Featured Coverage

• Accelerating neural network optimization: The power of second-order methods
• Solving complex problems with LLMs: A new prompting strategy presented at NeurIPS
• A new method for solving variational inequalities in conditions where derivatives cannot be calculated exactly is proposed
• Scientists have proposed a new method for solving variational inequalities in conditions where derivatives cannot be calculated exactly

Codes

My codes are available on following GitHub pages https://github.com/OptMLGroup and https://github.com/optml