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Thomas Vandal

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Thomas Vandal
Research Scientist

NASA Ames Research Center
Bay Area Environmental Research Institute
Moffett Field, CA

[Research Statement]

My research is at the intersection of machine learning, earth sciences, image processing, and high-performance computing. I am interested in building machine learning techniques for extracting information from remotely sensed satellite imagery and atmospheric models to better understand the effects of climate change. These include applications such as spatial and temporal downscaling, emulation of physical models, generating virtual sensors, and near-term forecasting.


Thomas is a research scientist at the NASA Ames Research Center and Bay Area Environmental Research Institute within the NASA Earth Exchange (NEX). In August 2018 Thomas earned a Ph.D. in Interdisciplinary Engineering from Northeastern University working in the Sustainability and Data Sciences Lab advised by Auroop Ganguly and Jennifer Dy. His research has won runner-up best paper and runner-up best student paper awards at SIGKDD 2017 and outstanding graduate researcher at Northeastern University in 2018. He is an elected student member of the committee on Artificial Intelligence Applications to Environmental Science for the American Meterological Society (AMS). Prior to graduate school he worked at multiple startups in the Boston area, including the MIT Media Lab spin-out Affectiva. He completed his bachelors in mathematics at the University of Maryland College Park in 2012.


Selected Publications

Vandal, T. & Nemani, R. (2019). “Optical Flow for Intermediate Frame Interpolation of Multispectral Geostationary Satellite Data”. [arxiv]

Vandal, T., Kodra, E., Ganguly, S., Dy, J., Nemani, R., & Ganguly, A (2018): “Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning,” Proceedings of the 24rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 1663-1672. (Research Track, 18% acceptance rate)[paper] [code]

Vandal, T., Kodra, E., Ganguly, S., Michaelis, A., Nemani, R., & Ganguly, A. (2017). “DeepSD: Generating high resolution climate change projections through single image super-Resolution,” KDD 2017, Proceedings of the 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 1663-1672. (Runner-Up BEST PAPER Award and Runner-up BEST STUDENT PAPER Award in Applied Data Science Track, 9% oral acceptance rate) [paper] [code]

Vandal, T., E. Kodra, and A. Ganguly, “Intercomparison of Machine Learning Methods for Statistical Downscaling: The Case of Daily and Extreme Precipitation.” Theoretical and Applied Climatology. September 2018.

Vandal, T., “Statistical Downscaling of Global Climate Models with Image Super-resolution and Uncertainty Quantification”, 2018. Northeastern University. [Dissertation]