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MIDAS Seminar Series Presents: Eric Xing – Carnegie Mellon University
December 14, 2020 @ 4:00 pm - 5:00 pm
Professor, Computer Science, Carnegie Mellon University
Founder, CEO, and Chief Scientist, Petuum Inc.
From Performance-oriented AI to Production- and Industrial-AI
Machine Learning systems for complex tasks – such as controlling industrial manufacturing processes in real-time; or writing medical imaging case reports – are becoming increasing sophisticated and consist of a large number of data, model, algorithm, and system elements and modules. Traditional performance-oriented bespoke approaches in the ML community are not suited to meet highly demanding industrial standards beyond performance, such as safety, energy-efficiency, and scalability typically expected in production systems in industries such as healthcare, manufacturing, and transportation. In this talk, I discuss technical challenges toward production- and industrial-AI from the following aspects: theoretical foundation for panoramic learning with all experiences, compositional strategies for building Pan-ML programs from Lego-like blocks, optimization methods for tunning systems, and systems framework for scaling up and scaling out ML productions. I will provide a few examples of our effects to address each of these challenges in the form of first principle formula, new algorithms, software toolkits, and composable systems.
Bio: Eric P. Xing is a Professor of Computer Science at Carnegie Mellon University, and the Founder, CEO, and Chief Scientist of Petuum Inc., a 2018 World Economic Forum Technology Pioneer company that builds standardized artificial intelligence development platform and operating system for broad and general industrial AI applications. He completed his undergraduate study at Tsinghua University, and holds a PhD in Molecular Biology and Biochemistry from the State University of New Jersey, and a PhD in Computer Science from the University of California, Berkeley. His main research interests are the development of machine learning and statistical methodology, and large-scale computational system and architectures, for solving problems involving automated learning, reasoning, and decision-making in high-dimensional, multimodal, and dynamic possible worlds in artificial, biological, and social systems. Prof. Xing currently serves or has served the following roles: associate editor of the Journal of the American Statistical Association (JASA), Annals of Applied Statistics (AOAS), IEEE Journal of Pattern Analysis and Machine Intelligence (PAMI) and the PLoS Journal of Computational Biology; action editor of the Machine Learning Journal (MLJ) and Journal of Machine Learning Research (JMLR); member of the United States Department of Defense Advanced Research Projects Agency (DARPA) Information Science and Technology (ISAT) advisory group. He is a recipient of the National Science Foundation (NSF) Career Award, the Alfred P. Sloan Research Fellowship in Computer Science, the United States Air Force Office of Scientific Research Young Investigator Award, the IBM Open Collaborative Research Faculty Award, as well as several best paper awards. Prof Xing is a board member of the International Machine Learning Society; he has served as the Program Chair (2014) and General Chair (2019) of the International Conference of Machine Learning (ICML); he is also the Associate Department Head of the Machine Learning Department, founding director of the Center for Machine Learning and Health at Carnegie Mellon University; he is a Fellow of the Association of Advancement of Artificial Intelligence (AAAI), and an IEEE Fellow.