About Pratik Nag Pratik Nag Ph.D. Student, Statistics spatio-temporal modeling machine learning artificial intelligence uncertainty quantification computational statistics Pratik Nag is a Ph.D. candidate at the KAUST Environmental Statistics research group under the supervision of Professor Ying Sun. Before joining KAUST, Pratik obtained a master's degree in statistical quality control and operation research from the Indian Statistical Institute, India. Research Interests Pratik's research interests focus on machine learning, artificial intelligence and statistical techniques to solve problems related to spatio-temporal processes. Education Profile Master's degree in Statistical Quality Control and operation research, Indian Statistical Institute, India Events Presented Events May 26 - Jun 1, 2024 Deep Neural Networks for Large-scale Complex Spatial and Spatio-temporal Processes Pratik Nag , Ph.D. Student, Statistics May 29, 09:00 - 11:30 B4 L5 R5220 Environmental statistics play a critical role in various interconnected domains, encompassing weather and climate forecasting, air quality monitoring, and sustainable urban planning. However, because of their high inherent unpredictability and nonstationarity, modeling complex spatio-temporal dynamics of environmental processes is challenging. This dissertation develops a set of DNN based methods for large-scale spatial and spatio-temporal processes.
Deep Neural Networks for Large-scale Complex Spatial and Spatio-temporal Processes Pratik Nag , Ph.D. Student, Statistics May 29, 09:00 - 11:30 B4 L5 R5220 Environmental statistics play a critical role in various interconnected domains, encompassing weather and climate forecasting, air quality monitoring, and sustainable urban planning. However, because of their high inherent unpredictability and nonstationarity, modeling complex spatio-temporal dynamics of environmental processes is challenging. This dissertation develops a set of DNN based methods for large-scale spatial and spatio-temporal processes.
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