The Speakers

Anas Barakat is a research fellow at Singapore University of Technology and Design. He previously held a postdoctoral position at ETH Zurich and earned his PhD in applied mathematics and computer science from Institut Polytechnique de Paris at Télécom Paris. His research lies at the intersection of multi-agent learning, reinforcement learning, and optimization, focusing on the design and analysis of learning algorithms for sequential decision making in strategic, dynamic, and uncertain environments, with applications to machine learning and multi-agent systems. 

Dimitrios Myrisiotis is a Research Fellow at CNRS@CREATE LTD. (Singapore) and a visitor at the School of Computing, National University of Singapore. His research focuses on computational complexity theory and the foundations of machine learning. 

Ioannis is an Assistant Professor of Computer Science at UC Irvine and a researcher at Archimedes AI. He is interested in the theory of computation, machine learning and its interface with non-convex optimization, dynamical systems, learning in games, statistics and multi-agent reinforcement learning. Before joining UCI, he was an Assistant Professor at Singapore University of Technology and Design. Prior to that he was a MIT postdoctoral fellow. He received his PhD in Algorithms, Combinatorics and Optimization from Georgia Tech in 2016, a Diploma in EECS from National Technical University of Athens, and a MS in Mathematics from Georgia Tech. He is the recipient of the 2019 NRF fellowship for AI. 

Tushar Vaidya is a Research Fellow at Nanyang Technological University (NTU), Singapore, in the School of Physical and Mathematical Sciences. He received his Ph.D. from the Singapore University of Technology and Design. His research focuses on quantum algorithms with applications to partial differential equations, statistics, and algebra, alongside work in commutative algebra for machine reasoning and the mathematics of opinion dynamics. In his work on social learning, his study “Averaging Plus Learning Models and Their Asymptotics” introduces a limit theorem within a generalized DeGroot framework, showing how agents' beliefs converge to non-standard limits—offering insight into randomness and the dissipation of information shocks. His interdisciplinary work in quantum computing includes the co-authored paper “Quantum Algorithms for the Pathwise Lasso,” which develops a new quantum algorithm for high-dimensional regression with an ℓ₁-penalty.