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The National Institute of Statistical Sciences (NISS) and Merck are sponsoring a Virtual Meet-Up on Interpretable/Explainable Machine Learning!
Interpretable/explainable Machine Learning (ML) is an emerging field to address the black-box nature of complex models obtained by many popular ML methods. Its success can remove one of the major obstacles that prevent ML from having more impact on areas such as healthcare, where human understanding of how a data-driven model works is crucial for many reasons. As many research fields have experienced in their infancy, few widely accepted notions and boundaries have given researchers the freedom to formulate this field with their visions, and to define the key concepts, such as interpretability and explainability, in their own ways. In this meetup, two leading researchers will share their efforts, contributions, and visions about this rapidly developing field.
Professor Cynthia Rudin, Duke University
"Do Simpler Machine Learning Models Exist, and How Can We Find Them?"
Professor Bin Yu, University of California, Berkeley
"Interpreting deep neural networks towards trustworthiness"
Junshui Ma, Merck
About the Speakers
Cynthia Rudin is the Earl D. McLean, Jr. Professor of Computer Science, Electrical and Computer Engineering, Statistical Science, Mathematics, and Biostatistics & Bioinformatics; and is PI, Interpretable Machine Learning Lab at Duke University. She directs the Interpretable Machine Learning Lab, and her goal is to design predictive models that people can understand. Her lab applies machine learning in many areas, such as healthcare, criminal justice, and energy reliability. She holds degrees from the University at Buffalo and Princeton. She is the recipient of the 2022 Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity from the Association for the Advancement of Artificial Intelligence (the “Nobel Prize of AI”). She received a 2022 Guggenheim fellowship, and is a fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the Association for the Advancement of Artificial Intelligence. Her work has been featured in many news outlets including the NY Times, Washington Post, Wall Street Journal, and Boston Globe. Full Bio
Bin Yu is the Chancellor's Distinguished Professor and Class of 1936 Second Chair in the Departments of statistics and EECS, and Center for Computational Biology at UC Berkeley. She obtained her BS Degree in Mathematics from Peking University, and MS and PhD Degrees in Statistics from UC Berkeley. She was Assistant Professor at UW-Madison, Visiting Assistant Professor at Yale University, Member of Technical Staff at Lucent Bell-Labs, and Miller Research Professor at Berkeley. She was a Visiting Faculty at MIT, ETH, Poincare Institute, Peking University, INRIA-Paris, Fields Institute at University of Toronto, Newton Institute at Cambridge University, and the Flatiron Institute in NYC. She was Chair of the Department of Statistics at UC Berkeley. She has published more than 170 publications in premier venues and these papers not only investigate a wide range of research topics from practice to algorithms and to theory, but also seek deep insights. The breadth and depth of her research experience enabled unique and novel solutions to interdisciplinary data problems in audio and image compression, network tomography, remote sensing, neuroscience, genomics, and precision medicine. Full Bio