Bio: Alexander Rodriguez is a Ph.D. student in Computer Science at Georgia Tech advised by Prof. B. Aditya Prakash. His research interests include data science and AI, with emphasis on time-series and real-world networks problems motivated from epidemiology and community resilience. In response to COVID-19, he has been the student lead at his research group in forecasting the progression of the pandemic, and these predictions have been featured in the CDC’s website and FiveThirtyEight.com. His work has been published in AAAI, KDD, NeurIPS, and BigData, and awarded the 1st place in the Facebook/CMU COVID-19 Challenge and the 2nd place in the C3.ai COVID-19 Grand Challenge. He also has served as workshop organizer in BPDM @ KDD 2017 and epiDAMIK @ KDD 2021.
Talk Title: Deep Learning Frameworks for Epidemic Forecasting
Talk Abstract: Our vulnerability to emerging infectious diseases has been illustrated with the devastating impact of the COVID-19 pandemic. Forecasting epidemic trajectories (such as future incidence over the next four weeks) gives policymakers a valuable input for designing effective healthcare policies and optimizing supply chain decisions. However, this is a non-trivial task with multiple open questions. In this talk, I will present our neural frameworks for epidemic forecasting, using seasonal influenza and the COVID-19 as examples. I will introduce our efforts in three research directions: (1) awareness of multiple facets of the epidemic dynamics, (2) coping with challenges from using public health data, and (3) readiness to provide actionable forecasts and insights. I will first discuss our deployed model for predicting COVID-associated indicators, which has been recognized as a top short-term forecasting model among all models submitting predictions to the CDC. I will also introduce how to use deep learning to adapt a historical flu model to an emerging scenario where COVID and flu coexist by leveraging auxiliary data sources. Next, I will introduce deep learning frameworks for incorporating expert-guidance, principled uncertainty quantification for well-calibrated forecasts, and handling data revisions for refining forecasts. Finally, I will share some future research directions.