Amos Korman

I am a full professor in the Department of Computer Science at the University of Haifa. Previously, I served as a senior researcher (Research Director) at the CNRS, located at IRIF and FILOFOCS research groups.

I am deeply fascinated by the examination of natural phenomena through the algorithmic lens, particularly in uncovering the interconnections between distributed computing and collective animal behavior. My primary focus is on developing methodologies to integrate rigorous algorithmic analysis with biological experimentation. I'm particularly interested in establishing and verifying meaningful predictions based on algorithmic analysis and exploring the potential insights that algorithmic lower bounds can provide in biological or social contexts. 

To achieve these goals, I focus on specific animal groups, including ants, bats, and humans, while also analyzing abstract multi-agent systems in bio-inspired environments. This approach leads me to explore key questions such as: How do noise and competition affect a group's effectiveness in collective tasks? What computational challenges do animals face during foraging, and which strategies help overcome them? How do simple rules followed by individuals create global patterns? In addressing these questions, I apply algorithmic concepts and analytical tools from the fields of distributed computing, game theory, and probability theory. When feasible, I collaborate with biologists to integrate these approaches with experiments on animal behavior.

I consider myself an ambassador for algorithm theory within the biological study of collective animal behavior. My overarching goal is to help establish a new research area called "Algorithmic Biology". This domain emphasizes designing and conducting biological experiments grounded in principles and insights from algorithm theory. It aims to provide biologists with advanced analytical tools to gain a deeper understanding of the computational challenges inherent in biological systems, while also offering computer scientists a new avenue to engage with empirical science.