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 Moffett Field, CA. 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.
(December 2019) Presented at the AGU Fall Meeting.
(August 2019) Pre-print posted, “Optical Flow for Intermediate Frame Interpolation of Multispectral Geostationary Satellite Data”, on Arxiv.
(July 2019) Presented at the Space Lidar Winds Working Group Meeting at the National Institute of Aerospace.
(April 2019) Presented at the 1st Workshop on Leveraging AI in the Exploitation of Satellite Earth Observations & Numerical Weather Prediction, Slides.
(July 2018) Our paper was invited for submission to the Sister’s Best Paper Track at IJCAI 2018.
(August 2018) Paper accepted to KDD 2018 research track on “Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning”.
(December 2017) Research is featured in the Nature comment article The case for technology investments in the environment .
(August 2017) Paper accepted at KDD 2017 on DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution.
Vandal, T. & Nemani, R. (2019). “Optical Flow for Intermediate Frame Interpolation of Multispectral Geostationary Satellite Data”. Arxiv Pre-print
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. Paper.
Vandal, T., “Statistical Downscaling of Global Climate Models with Image Super-resolution and Uncertainty Quantification”, 2018. Northeastern University. Dissertation.