Sanjeev Arora, Visiting Professor in the School of Mathematics, and Richard Zemel, Visitor in the School of Mathematics, will a give a public lecture, “Machines: How Do They Learn and Where Are They Headed?,” on Friday, October 27, which will take place at 5:30 p.m. in Wolfensohn Hall on the Institute campus.
Artificial intelligence and machine learning have taken the world by storm and are expected to be significant forces in industrial innovation going forward. Like any new technology, artificial intelligence and machine learning will have positive, as well as negative effects. Media and television shows speculate about a future with machines taking over, but is that a reality? Arora and Zemel will give brief talks about the field of machine learning and its major technical challenges followed by a panel discussion moderated by Robbert Dijkgraaf, Director and Leon Levy Professor.
Arora’s research focuses on achieving a better theoretical understanding of methods in machine learning that are empirically successful. Currently, his topics of interest include unsupervised learning, generative models, deep learning, natural language processing, and reinforcement learning. Arora’s work in the field has earned him numerous awards including the D.R. Fulkerson Prize in Discrete Mathematics (2012), the ACM-Infosys Foundation Award in the Computing Sciences (2012), the best paper award from IEEE Foundations of Computer Science (2010), and the EATCS-SIGACT Gödel Prize (2010).
In addition to his current role at the Institute, Arora holds a joint appointment at Princeton University as the Charles C. Fitzmorris Professor of Computer Science. Shortly after receiving his Ph.D. from the University of California, Berkeley, Arora joined the faculty at Princeton University, a position he has remained in since 1994. During this time he was also a Visiting Associate Professor at the University of California, Berkeley (2001–02), a Visiting Researcher at Microsoft (2006–07), and a Visiting Professor at the Weizmann Institute (2007). Arora was the founding Director and lead Principal Investigator at the Center for Computational Intractability in 2008, a project funded partly by a National Science Foundation Expeditions in Computing grant. He was elected as an Association for Computing Fellow in 2009 and also appointed as a Simons Foundation Investigator in 2012.
Zemel has published a number of influential papers, including work on unsupervised learning, information representation in neural populations, and machine learning from images and text. His current research focuses on learning with little data (how to adapt learning systems to accommodate new classes not seen in training); algorithmic fairness (how automated learning systems can make fair decisions); and synergies between our understanding of neural information processing and computation in deep neural networks. Zemel’s contributions to the field have led to awards including a NVIDIA Pioneers of AI Award (2016), two NSERC Discovery Accelerators grants (2009–12), a Young Investigator Award from the Office of Naval Research (2002–04), a Presidential Scholar Award (1980), and seven Dean’s Excellence Awards at the University of Toronto.
Zemel earned his Ph.D. from the University of Toronto in 1993 and is a Professor of Computer Science at the University of Toronto, where he has been a faculty member since 2000. He is currently the Research Director of the new Vector Institute for Artificial Intelligence, an independent not-for-profit institute sponsored by government and industry, focusing on machine learning and deep learning. Zemel received his B.Sc. from Harvard University, was a postdoctoral fellow at CMU and the Salk Institute, and was a faculty member at the University of Arizona. He is a Fellow of the Canadian Institute for Advanced Research and is on the Executive Board of the Neural Information Processing Society. Zemel is also the cofounder of SmartFinance, a financial technology startup specializing in data enrichment and natural language processing.
This event is free and open to the public, but registration is required. To register for this event, click here. For more information on other public lectures and events at the Institute, visit http://www.ias.edu/events.