Fivos Kalogiannis

Φοίβος Καλογιάννης
/ˈfi.vos/ /kaloˈʝanis/
UC San Diego

I am broadly interested in optimization theory for machine learning, (multi-agent) reinforcement-learning, machine learning theory, and algorithmic game theory. I enjoy working on both theory and its real-world applications.

I am a CS PhD student at UC San Diego advised by Prof. Mikhail Belkin focusing on optimization for machine learning. Previously, I earned my MS in Computer Science at UC Irvine advised by Prof. Ioannis Panageas where I worked extensively on algorithmic game theory, multi-agent reinforcement learning, and optimization. Even before that, I did my undergrad in Electrical and Computer Engineering at the National Technical University of Athens.

I grew up on Lemnos, a remote island of the North Aegean Sea.

news

Sep 25, 2024 New paper accepted at NeurIPS 2024 with Jingming Yan and Ioannis Panageas! Title: Learning Equilibria in Adversarial Team Markov Games: A Nonconvex-Hidden-Concave Min-Max Optimization Problem (on arxiv, soon).
Sep 22, 2023 Our paper Zero-sum Polymatrix Markov Games: Equilibrium Collapse and Efficient Computation of Nash Equilibria got accepted in NeurIPS 2023! See you in NOLA!
May 1, 2023 Our paper Algorithms and Complexity for Computing Nash Equilibria in Adversarial Team Games got accepted in EC 2023!