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Publications

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Dissertation

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

Preprints

[P1] Duffy, K., Vandal, T., Wang, W., Nemani, R., & Ganguly, A. R. (2019). Deep Learning Emulation of Multi-Angle Implementation of Atmospheric Correction (MAIAC). arXiv preprint arXiv:1910.13408. arXiv.

Conference Papers

[C8] Vandal, T., Duffy, K., McCarty, W., Sewnath, A., & Nemani, R. (2022). “Dense feature tracking of atmospheric winds with deep optical flow”, Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.

[C7] Gao, N., Wilson, M., Vandal, T., Vinci, W., Nemani, R., & Rieffel, E. “High-Dimensional Similarity Search with Quantum-Assisted Variational Autoencoder,” Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Research Track, 16% acceptance rate). Paper

[C6] Nemani, R., Wang, W., Hshimoto, H., Michaelis, A., Vandal, T., .. (2020). “GeoNEX: A geostationary earth observatory at NASA Earth eXchange: Earth monitoring from operational geostationary satellite systems.” IEEE International Geoscience and Remote Sensing Symposium. Paper

[C5] 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.

[C4] 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.

[C3] 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.

[C2] 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.

[C1] 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

[J8] Duffy. K., Vandal, T., Wang, W., Nemani, R., & Ganguly, A. R. (2022). A framework for deep learning emulation of numerical models with a case study in satellite remote sensing. IEEE Transactions on Neural Networks and Learning Systems (TNNLS). [[Paper]] (https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9769745) [Code]

[J7] Vandal, T. & Nemani, R. (2021). “Optical Flow for Intermediate Frame Interpolation of Multispectral Geostationary Satellite Data”. IEE Transactions on Neural Network and Learning Systems (TNNLS). Special issue on Special Issue on Deep Learning for Earth and Planetary Geosciences. [Paper] [Code] [Video].

[J6] Vandal, T., McDuff, D., Wang, W., Duffy, K., Michaelis, A., & Nemani, R. (2021) “Spectral Synthesis for Geostationary Satellite-to-Satellite Translation”. IEEE Transactions on Geoscience and Remote Sensing. Paper

[J5] Wilson, M., Vandal, T., Hogg, T., & Rieffel, E. (2021). “Quantum-assisted associative network: Applying quantum annealing in deep learning”. Quantum Machine Intelligence Paper.

[J4] Konduri, V., Vandal, T., Ganguly, S., & Ganguly, A. R. (2020). Data Science for Weather Impacts on Crop Yield. Frontiers in Sustainable Food Systems, 4, 52. Paper

[J3] Li, S., Wang, W., Hashimoto, H., Xiong, J., Vandal, T., Yao, J., Qian, L., Ichii, K., Lyapustin, A., Wang, Y., & Nemani, R., “First Provisional Land Surface Reflectance Product from Geostationary Satellite Himawari-8 AHI”. Remote Sensing. December 12 2019. DOI.

[J2] Duffy, K., Vandal, T., Li, S., Ganguly, S., Nemani, R., & Ganguly, A., “DeepEmSat: Deep Emulation for Satellite Data Mining.” Frontiers in Big Data. December 10 2019. Paper.

[J1] 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.

Workshop Papers

[W5] Vandal, T. & Nemani, R. (2020). “Optical Flow for Intermediate Frame Interpolation of Multispectral Geostationary Satellite Data”. 1st ACM SIGKDD Workshop on Deep Learning for Spatiotemporal Data, Applications, and Systems (Best Spotlight Presentation).

[W4] 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.

[W3] 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.

[W2] Vandal, T. & Ganguly, A. (2017), “Uncertainty Quantification of Statistical Downscaling using Bayesian Deep Learning.” 7th International Workshop on Climate Informatics, Boulder, CO.

[W1] 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.

Book Chapters

[B1] 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. Chapter

Conference Poster Presentations

Vandal. T, Duffy, K, McCarty, W., Sewnath, A., Das, P., Michaelis, A., and Nemani, R. (2022) Deep Learning System for Efficient Processing of Geostationary Satellite Imagery. 21th Conference on Artificial Intelligence for Environmental Science, AMS Winter Meeting, Houston, TX.

Duffy, K., Vandal, T., and Nemani, R. (2022). “LEO sensor to GEO sensor algorithm transfer models for land surface temperature”. 21th Conference on Artificial Intelligence for Environmental Science, AMS Winter Meeting, Houston, TX.

Vandal, T. (2021). “GeoNEX-ML: A Machine Learning System for Earth Observations.” (Invited) Indian Symposium on Machine Learning, Virtual.

Vandal, T. (2021) “Physics Guided Optical Flow for Tracking Atmospheric Motion” (Invited). American Geophysical Union Fall Meeting. New Orleans, LA.

Duffy, K., Vandal, T., and Nemani, R. (2021) “Communicating metrics of land surface temperature variability using multi-sensor machine learning”. American Geophysical Union Fall Meeting. New Orleans, LA.

Yadav, N., Vandal, T., Duffy, K., and Nemani, R. (2021). Physics-Guided Deep Learning for Quantitative Precipitation Nowcasting. American Geophysical Union Fall Meeting. New Orleans, LA.

Das, P., Vandal, T., Duffy, K., and Ganguly, R. (2021). “Uncertainty Aware Machine Learning based Quantitative Precipitation Estimation from Geostationary Satellites.” American Geophysical Union Fall Meeting. New Orleans, LA.

Vandal, T. (2021) “Virtual Sensing with Unsupervised Image-to-Image Translation”. 20th Conference on Artificial Intelligence for Environmental Science, AMS Winter Meeting, Virtual.

Vandal, T. (2021) “Towards Physics Guided Optical Flow for Tracking Atmospheric Motion”. Asia Oceania Geosciences Society, Virtual.

Vandal, T. (2021) “GeoNEX-ML: A Machine Learning System for Geostationary Satellite Imagery” at 3rd NOAA Workshop on Leveraging AI in Environmental Sciences, Virtual.

Vandal, T. & Nemani, R. (2020). “Optical Flow for Intermediate Frame Interpolation of Multispectral Geostationary Satellite Data”. 19th Conference on Artificial Intelligence for Environmental Science, AMS Winter Meeting. Boston, MA.

Park, T., Wang, W., Hashimoto, H., Vandal, T., Dungan, J. L., Wang, Y., … & Nemani, R. R. (2020, December). Generation of land surface reflectance with combined Geo-KOMPSAT-2A AMI and Himawari 8 AHI observations. In AGU Fall Meeting Abstracts (Vol. 2020, pp. A008-0016).

Vandal, T., Nemani, R. R., Wang, W., & Li, S. (2019, December). Transfer Learning to Generate True Color Images from GOES-16. In AGU Fall Meeting 2019. AGU.

Duffy, K., Vandal, T., Li, S., Nemani, R. R., & Ganguly, A. R. (2019, December). Deep Learning Emulation of Atmospheric Correction for Geostationary Sensors. In AGU Fall Meeting 2019. AGU.

Li, S., Wang, W., Hashimoto, H., Vandal, T., Yao, J., & Nemani, R. R. (2019, December). Surface Reflectance Product from Geostationary Satellite. In AGU Fall Meeting 2019. AGU.

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.