Coping with Heterogenity and Uncertainty of COVID-19 Datasets

Yu-Xiang Wang
Professor in Computer Science, UCSB

Ambuj Singh
Professor in Computer Science, UCSB

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.

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.

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.

Please join us for this exciting seminar series. Sign up here to receive updates by email about this series.

Sponsored by UCSB's Interdisciplinary Research Centers, NOVIM, and Cottage Health.