Join us for our an exploration of AI with two local experts, Professors Wei Ding and John Quackenbush. They will be discussing the revolutionary role of AI in the field of Medicine and Biomedical Research with the use of unsupervised machine learning. This technique uses algorithms to analyze and cluster data in order to discover hidden patterns without the need for human intervention. Dr. Quackenbush and Dr. Ding have brought their exceedingly diverse backgrounds and experience to bear on this topic in a successful collaboration for years. During the event, they will not only share their insights into AI's transformative power in the field of medicine and biomedical research but also offer valuable career path advice – come and learn about where this may take us in the future.

John Quackenbush is Henry Pickering Walcott Professor of Computational Biology and Bioinformatics and Chair of the Department of Biostatistics at the Harvard T.H. Chan School of Public Health, Professor in the Channing Division of Network Medicine, and Professor at the Dana-Farber Cancer Institute. John’s PhD was in Theoretical Physics, but in 1992 he received a fellowship to work on the Human Genome Project. This led him through the Salk Institute, Stanford, The Institute for Genomic Research (TIGR), and to Harvard in 2005. John uses massive data to probe how many small effects combine to influence human health and disease. He has more than 300 scientific papers and over 73,000 citations. Among his honors is recognition in 2013 as a White House Open Science Champion of Change. Dr. Quackenbush has been elected to the National Academy of Medicine (NAM) in 2023.

Wei Ding is a Professor of Computer Science at the University of Massachusetts Boston. She served as a Program Director at the National Science Foundation's Division of Information Intelligent Systems from 2019 to 2023. Her responsibilities included overseeing Information Integration and Informatics, Smart Health and Biomedical Research, Formal Methods in the Field, Mathematical and Scientific Foundations of Deep Learning, and Principles and Practice of Scalable Systems. Wei's research interests encompass data mining, machine learning, artificial intelligence, computational semantics, with applications in health sciences, astronomy, geosciences, and environmental sciences. Her contributions consist of over 140 referenced research papers, a published book, and three patents. She received the NSF Director’s Award in 2022, the Best Paper Award at the 2011 IEEE International Conference on Tools with Artificial Intelligence (ICTAI), the Best Paper Award at the 2010 IEEE International Conference on Cognitive Informatics (ICCI), and the Best Poster Presentation award at the 2008 ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPAITAL GIS). Wei was honored with the 2019 WISAY Distinguished Woman in Science Award from Yale University, the 2018 Outstanding Alumni Award, and the Best PhD Work Award between 2007 and 2010 from the University of Houston. Her research projects have been funded by NSF, NIH, NASA, and DOE. She is an IEEE Fellow, AAIA Fellow, and an ACM Senior Member.

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Presented in collaboration with the Cary Memorial Library in Lexington and other area libraries.