2021 - present: Assistant Professor, University of Graz

2015 - 2021: Postdoc, University of Graz

2012 - 2015: PhD student, University of Graz

2010 - 2012: Lecturer, Hanoi University of Science and Technology

Dr. Binh T. Nguyen is an Associate Professor of Computer Science and Head of the Department of Computer Science at the Faculty of Mathematics and Computer Science, VNU HCM – University of Science. He defended his Ph.D. thesis at Ecole Polytechnique in 2012 and has worked at the University of Science, Vietnam National University Ho Chi Minh City, since 2013. He has over ten years of experience in machine learning, with more than 90 publications and four patents filed in the USA and Canada. He has received some national awards in training and research, such as the Mathematical Research Award from the Vietnam Institute of Advanced Study in Mathematics, Vietnam National Ministry of Education and Training (2020), and the Golden Globe Award in Science and Technology in Vietnam (2021), and best paper awards in several international conferences. 

Dabeen Lee is currently an assistant professor at the Department of Industrial and Systems Engineering (ISE) at the Korea Advanced Institute of Science and Technology (KAIST). Before joining KAIST, Dabeen was a postdoctoral research fellow at the Discrete Mathematics Group at the Institute for Basic Science (IBS) in Korea, and he was a recipient of the IBS Young Scientist Fellowship.

Dabeen obtained his Ph.D. in Algorithms, Combinatorics, and Optimization (ACO) at the Tepper School of Business, Carnegie Mellon University His thesis won the Gerald L. Thompson Doctoral Dissertation Award for the best Ph.D. thesis in the area of Management Science, and I was supported by Henry J. Gailiot Presidential Fellowship. Moreover, he received Second Place in the INFORMS Optimization Society Best Student Paper Prize Competition 2019.

His research interests are in designing algorithms and mathematical programming frameworks for broad areas of optimization spanning discrete, combinatorial, integer, convex, online, stochastic, robust, and distributionally robust optimization. Recently, he has been working on the seamless integration of machine learning and optimization with applications in combinatorial black-box optimization, online reinforcement learning, and optimization under nonstationary environments. He currently works with several industry partners and helps them to develop and apply optimization methods for designing decision-making processes.

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