Coping with Heterogeneity and Uncertainty of COVID-19 Datasets

Event Date: 

Tuesday, April 28, 2020 - 1:00pm to 2:00pm

Event Date Details: 

Yu-Xiang Wang, PhD & Ambuj K Singh, PhD discuss Coping with Heterogenity and Uncertainty of COVID-19 Datasets


This webinar overviews existing datasets that are used for modeling the dynamics of COVID-19.The characteristics, bias, and accuracy of these datasets and how they affect the eventual decision making are discussed.


About the Speakers



Yu-Xiang Wang, PhD

Assistant Professor, Computer Science, UCSB

Dr. Wang (Ph.D. Statistics and Machine Learning, Carnegie Mellon University) studies machine learning with a special focus on statistical theory and methodology, differential privacy, large-scale machine learning, reinforcement learning and deep learning. He is co-directing the UCSB Center for Responsible Machine Learning, and is leading a recently NSF-funded project (with Prof. Xifeng Yan) on modeling COVID-19 with Artificial Intelligence and Machine Learning methods.


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Ambuj K Singh, PhD

Professor, Computer Science, UCSB


Dr. Singh (Ph.D. Computer Science, University of Texas at Austin) studies network science, machine learning, social networks, and bioinformatics. He has led a number of multidisciplinary projects including an Interdisciplinary Graduate Education Research and Training (IGERT) program on Network Science funded by the NSF. He is currently leading UCSB’s Data Science Initiative, which is planning and implementing training and research activities around Data Science.