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Administrative data

Federico Amato

federico.amato@epfl.ch +41 21 693 12 36

EPFL SDSC
INN 218 (Bâtiment INN)
Station 14
1015 Lausanne

EPFL › ETU › MTE-S › MTE-PG

Expertise

Data Science, Artificial Intelligence, Applied Statistics, ML- and LLM- ops, Spatio-temporal Data Analysis

Mission

My mission is to leverage AI as a catalyst for better health, sustainable progress, and societal impact, transforming data into insight, research into innovation, and innovation into lasting value.
Principal Data Scientist at the Swiss Data Science Center with a MSc in Engineering and Ph.D. in Sustainable Development and Innovation Engineering. I have 10+ years of experience driving research, innovation, and real-world AI solutions. I have delivered and led data science projects across health and biomedical R&D, insurance, manufacturing, energy, climate & environment, generating measurable impact for businesses, NGOs, and public institutions.

I have large experience in multiple AI branches, including Generative AI, LLM-Ops, Computer Vision, applied statistics, and multimodal data science (images, text, speech, structured data). At SDSC, I focus on bridging cutting-edge research with practical applications, from conceptual innovation and model development to the deployment of scalable AI systems.

Education

Ph.D.

| Innovation and Sustainable Development Engineering

2014 – 2018 University of Basilicata, Italy

MSc

| Engineering

2007 – 2014 University of Basilicata, Italy

Professionals experiences

Postdoctoral Research Fellow

2018–2021

University of Lausanne

Selected publications

A novel framework for spatio-temporal prediction of environmental data using deep learning.

Amato, F., Guignard, F., Robert, S., and Kanevski, M.
Published in Scientific reports, 10(1), 1-11. in

Spatio-temporal estimation of wind speed and wind power using machine learning: predictions, uncertainty and technical potential.

Amato, F., Guignard, F., Walch, A., Mohajeri, N., Scartezzini, J. L., & Kanevski, M.
Published in arXiv preprint arXiv:2108.00859 in

Spatio-temporal evolution of global surface temperature distributions.

Amato, F., Guignard, F., Humphrey, V., & Kanevski, M.
Published in Association for Computing Machinery, Proceedings of the 10th International Conference on Climate Informatics, 37-43. in

Uncertainty quantification in extreme learning machine: Analytical developments, variance estimates and confidence intervals.

Guignard, F., Amato, F., & Kanevski, M.
Published in Neurocomputing, 456, 436-449. in

Modelling the impact of urban growth on agriculture and natural land in Italy to 2030.

Martellozzo, F., Amato, F., Murgante, B., & Clarke, K. C.
Published in Applied Geography, 91, 156-167. in