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Publications

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Preprints

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

Wilson, M., Vandal, T., Hogg, T., & Rieffel, E. (2019). “Quantum-assisted associative network: Applying quantum annealing in deep learning”. [arxiv]

Conference Papers

Duffy, K., Vandal, T., Li, S., Ganguly, S., Nemani, R. & Ganguly, A. (2019). “DeepEmSat: Deep Emulation of Satellite Data Mining”, SIGKDD workshop on Fragile Earth: Theory Guided Data Science to Enhance Scientific Discovery.

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]

Koduri, V., Vandal, T., Ganguly, S., & Ganguly, A. (2018). “Data Mining for Weather Impacts on Crop Yield”, SIGKDD workshop on Fragile Earth: Theory Guided Data Science to Enhance Scientific Discovery.

Vandal, T., Livingston, M., Piho, C., Zimmerman, S. (2018). “Prediction and Uncertainty Quantification of Daily Airport Flight Delays.” Proceedings of The 4th International Conference on Predictive Applications and APIs, in PMLR. [paper]

Vandal, T., Kodra, E., Ganguly, S., Michaelis, A., Nemani, R., & Ganguly, A. (2018). “Generating High Resolution Climate Change Projections through Single Image Super-Resolution: An Abridged Version.” Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence and Twenty-Third European Conference on Artificial Intelligence Sister Best Paper Track (IJCAI Invited Submission). [paper]

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. & Ganguly, A. (2017), “Uncertainty Quantification of Statistical Downscaling using Bayesian Deep Learning.” 7th International Workshop on Climate Informatics, Boulder, CO.

Li, Y., Chang, Y., Vandal, T., Das, D., Ding, A., Ganguly, A., & Dy, J. (2016), “Copula based covariate selection in climate for statistical downscaling”, 5th International Workshop on Climate Informatics, Boulder, CO.

Vandal, T., McDuff, D., & Kaliouby, R. (2015), “Event Detection : Ultra Large-scale Clustering of Facial Expressions.” 11th IEEE International Conference on Automatic Face and Gesture Recognition, Ljubljana, Slovenia. [paper]

Journal Publications

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.

Dissertation

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

Book Chapters

Vandal, T., Bhatia, U., and Ganguly, A. (2017), “Statistical Downscaling in Climate with State of the Art Scalable Machine Learning.” Large-Scale Machine Learning in the Earth Sciences. Taylor & Francis.

Conference Poster Presentations

Vandal, T., Ganguly, S., Kodra, E., Dy, J., Michaelis, A., Nemani, R., & Ganguly, A. (2018). Image Super-Resolution and Uncertainty Quantification for Earth Science Data on NASA Earth Exchange AI Platform (Invited). In AGU Fall Meeting Abstracts.

Ganguly, S., Kalia, S., Duffy, K., Collier, E., Shreekant, G., Li, S., Mukhopadhyay, S., Prabhat, Vandal, T., Albert, A., Hashimoto, H., Wang, W., Lee, T., Choudhury, D., Michaelis, A., Saatchi, S., Tucker, C., & Nemani, R (2018). NEX-AI: A Cloud and HPC Agnostic Framework for Scaling Deep Learning and Machine Learning Applications for Earth Science. In AGU Fall Meeting Abstracts.

Duffy, K., Vandal, T., Li, S., Ganguly, S., Nemani, R., & Ganguly, A.,(2018). GOENEX: A Deep Learning Approach to Prediction of Surface Spectral Reflectance. In AGU Fall Meeting Abstracts.

Wilson, A., Vandal, T., Rieffel, E., & Nemani, R. (2018).Compressing Earth Science Datasets with Quantum-Assisted Machine Learning Algorithms. In AGU Fall Meeting Abstracts.

Mage, M., Ganguly, S., Vandal, T., Nemani, R. R., Li, S., Kalia, S., & Ganguly, A. R. (2017). Estimation of MODIS-like Surface-Spectral Reflectance from Geostationary Satellites using Deep Neural Networks. In AGU Fall Meeting Abstracts.- Duffy, K., Bhatia, U., Vandal, T., & Ganguly, A. R. (2017). The sensitivity of climate driven hydrologic models to statistical downscaling methods. In AGU Fall Meeting Abstracts.