Thomas Vandal
Thomas Vandal
Research Scientist, NASA Ames Research Center
Verified email at nasa.gov - Homepage
Title
Cited by
Cited by
Year
DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution
T Vandal, E Kodra, S Ganguly, A Michaelis, R Nemani, AR Ganguly
23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2017
532017
Event detection: Ultra large-scale clustering of facial expressions
T Vandal, D McDuff, R El Kaliouby
2015 11th IEEE International Conference and Workshops on Automatic Face and …, 2015
212015
Intercomparison of Machine Learning Methods for Statistical Downscaling: The Case of Daily and Extreme Precipitation
T Vandal, E Kodra, AR Ganguly
Theoretical and Applied Climatology, 1-14, 2018
162018
Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning
T Vandal, E Kodra, J Dy, S Ganguly, R Nemani, AR Ganguly
24rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2018
112018
Mental state event definition generation
E Kodra, R El Kaliouby, TJ Vandal
US Patent App. 14/796,419, 2015
102015
Generating high resolution climate change projections through single image super-resolution: An abridged version
T Vandal, E Kodra, S Ganguly, A Michaelis, R Nemani, AR Ganguly
International Joint Conferences on Artificial Intelligence Organization, 2018
72018
Quantum-assisted associative adversarial network: Applying quantum annealing in deep learning
M Wilson, T Vandal, T Hogg, E Rieffel
arXiv preprint arXiv:1904.10573, 2019
52019
Mental state event signature usage
R El Kaliouby, E Kodra, D McDuff, TJ Vandal
US Patent App. 15/262,197, 2016
42016
First provisional land surface reflectance product from geostationary satellite Himawari-8 AHI
S Li, W Wang, H Hashimoto, J Xiong, T Vandal, J Yao, L Qian, K Ichii, ...
Remote Sensing 11 (24), 2990, 2019
22019
Statistical Downscaling of Global Climate Models with Image Super-resolution and Uncertainty Quantification
TJ Vandal
Northeastern University, 2018
22018
Statistical Downscaling in Climate with State-of-the-Art Scalable Machine Learning
T Vandal, U Bhatia, AR Ganguly
Large-Scale Machine Learning in the Earth Sciences, 55-72, 2017
22017
Uncertainty Quantification for Statistical Downscaling using Bayesian Deep Learning
T Vandal, AR Ganguly
7th International Workshop on Climate Informatics, 2017
12017
High-Dimensional Similarity Search with Quantum-Assisted Variational Autoencoder
N Gao, M Wilson, T Vandal, W Vinci, R Nemani, E Rieffel
arXiv preprint arXiv:2006.07680, 2020
2020
Data Science for Weather Impacts on Crop Yield
VS Konduri, TJ Vandal, S Ganguly, AR Ganguly
Frontiers in Sustainable Food Systems 4, 52, 2020
2020
DeepEmSat: Deep Emulation for Satellite Data Mining
K Duffy, T Vandal, S Li, S Ganguly, R Nemani, AR Ganguly
Front. Big Data 2: 42. doi: 10.3389/fdata, 2019
2019
Transfer Learning to Generate True Color Images from GOES-16
T Vandal, RR Nemani, W Wang, S Li
AGUFM 2019, A34F-07, 2019
2019
Surface Reflectance Product from Geostationary Satellite
S Li, W Wang, H Hashimoto, T Vandal, J Yao, RR Nemani
AGUFM 2019, A41T-2653, 2019
2019
Deep Learning Emulation of Atmospheric Correction for Geostationary Sensors
K Duffy, T Vandal, S Li, RR Nemani, AR Ganguly
AGUFM 2019, A41T-2671, 2019
2019
Deep Learning Emulation of Multi-Angle Implementation of Atmospheric Correction (MAIAC)
K Duffy, T Vandal, W Wang, R Nemani, AR Ganguly
arXiv preprint arXiv:1910.13408, 2019
2019
Optical Flow for Intermediate Frame Interpolation of Multispectral Geostationary Satellite Data
T Vandal, R Nemani
arXiv preprint arXiv:1907.12013, 2019
2019
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