Antonio Celani

The Abdus Salam International Centre for Theoretical Physics (ICTP)

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About

Antonio Celani is a computational and theoretical researcher whose work bridges biophysics, fluid dynamics, and artificial intelligence, with a particular focus on **olfactory search** and **multi-agent systems** in complex environments. His most recognized contribution examines how organisms locate odor sources dispersed by turbulent flows — a fundamental challenge in both biology and robotics. His 2020 paper on collective olfactory search, now with 12 citations, investigates whether information-sharing among individuals performing simultaneous search tasks can meaningfully enhance performance, offering insights relevant to both animal behavior and swarm robotics. His subsequent systematic review of olfactory search algorithms (2024) consolidates the growing field's computational approaches, serving as a valuable resource for researchers and engineers alike. More recently, Celani has extended his expertise to applied multi-agent reinforcement learning, with his 2025 work on underwater monitoring demonstrating how AI-driven collaboration can overcome the severe constraints of subaquatic environments — limited communication, poor visibility, and absent global positioning. Across his portfolio, Celani demonstrates a distinctive ability to translate natural biological strategies into robust algorithmic frameworks, making his research valuable to ecologists, roboticists, and AI scientists alike.