DATA ANALYTICS · ARTIFICIAL INTELLIGENCE · COMPUTATIONAL EARTH SCIENCE
Antonios E. Marsellos, Ph.D.
Developing advanced data analytics, artificial intelligence, and computational methods for extracting knowledge from complex spatial and temporal data, applied to earthquakes, volcanoes, floods, climate, biomedical signals, and business.
Associate Professor in Geo-analytics, Department of Geology, Environment & Sustainability, Hofstra University · Adjunct Associate Professor of Business, Norm Brodsky College of Business, Rider University.
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Monitoring Earthquakes in New York (MENY)
A live seismicity dashboard built with students, the kind of community-data-to-app project CEEDS (Community-Engaged Earth Data and Sustainability) and Intro to R build toward.
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Open the live app →Research with students
I keep part of my research close to the students’ level of study and bring students into team projects, from first exposure to coding all the way to peer-reviewed publication. Student names appear as co-authors across conference proceedings, posters, and journal articles.
High School Research
I mentor high school researchers through the Hofstra University Summer Science Research Program (HUSSRP), where they run real research projects in R (from EEG seizure detection to sunspot cycles and GPS ground motion) and present at conferences. I mentored 18 students in 2024, 27 in 2025 and 7 in 2026, with a 2026 IEEE conference paper among the outcomes.
Meet the projects →University Research
Undergraduate and graduate students join independent research studies (GEOL-151), honors options, and theses (earlier cohorts joined the NSF-funded GEOTeams program, 2021–2023), co-authoring over a hundred conference papers and journal articles on floods, earthquakes, volcanoes, climate, and AI. New for Fall 2026: a 2-credit Intro to R for Geoscience and Sustainability course, with seats still open.
See student research →Books
R for Earth and Sustainability Data Analytics (2026)
A practical introduction to GIS, time series, modeling, and environmental applications. Learn to analyze real Earth and environmental data from scratch in R, no prior coding or statistics experience required. Available in paperback, hardcover, and Kindle.
Earlier books, touristic guides, and book chapters are listed in the publications database.
Latest peer-reviewed work
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