Antonios Mamalakis

Antonios Mamalakis, Ph.D., is an environmental data scientist interested in exploring data science tools like statistical and Bayesian analysis, machine/deep learning, and explainable AI to solve challenges in environmental applications. Among others, these challenges include improving predictive skill of hydroclimate and extreme events, understanding climate teleconnections and predictability, advancing climate attribution and causal discovery, etc.

Prior to joining UVA, he worked as a research scientist at Colorado State University, where he pioneered the investigation of the fidelity of explainable AI tools for applications in the geosciences. Some of his papers have garnered international attention and have been highlighted by publishers. Examples include "A new interhemispheric teleconnection increases predictability of winter precipitation in southwestern US," published in Nature Communications; "Zonally contrasting shifts of the tropical rain belt in response to climate change," published in Nature Climate Change; and "Underestimated MJO variability in CMIP6 models,"published in Geophysical Research Letters. Antonios serves as an Associate Editor for the AMS journal "Artificial Intelligence for the Earth Systems."

Mamalakis holds a Ph.D. in Civil and Environmental Engineering from University of California, Irvine, and a M.Sc. in the same major from University of Patras, Greece.