AAAI-18 Invited Speakers

AAAI-18 / IAAI-18 will feature the following series of distinguished speakers (partial list):

Cynthia Dwork

Cynthia Dwork

Harvard / Radcliffe Institute for Advanced Study

AAAI-18 Invited Speaker

Cynthia Dwork, Gordon McKay Professor of Computer Science at Harvard and Radcliffe Alumnae Professor at the Radcliffe Institute for Advanced Study, is renowned for placing privacy-preserving data analysis on a mathematically rigorous foundation. A cornerstone of this work is differential privacy, a strong privacy guarantee frequently permitting highly accurate data analysis, recognized by the 2016 Theory of Cryptography Conference Test-of-Time award and the Goedel Prize. Dwork has also made seminal contributions in cryptography and distributed computing, and is a recipient of the Edsger W. Dijkstra Prize, recognizing some of her earliest work establishing the pillars on which every fault-tolerant system has been built for decades. Her most recent foci include stability in adaptive data analysis (especially via differential privacy) and fairness in classification. Dwork is a member of the US National Academy of Sciences and the US National Academy of Engineering, and the American Philosophical Society, and is a Fellow of the American Academy of Arts and Sciences.

Zoubin Ghahramani

Zoubin Ghahramani

University of Cambridge / Uber

AAAI-18 Invited Speaker

Zoubin Ghahramani FRS is Professor of Information Engineering at the University of Cambridge and Chief Scientist at Uber. He is also Deputy Director of the Leverhulme Centre for the Future of Intelligence, and a Fellow of St John’s College. He was a founding Cambridge Director of the Alan Turing Institute, the UK’s national institute for data science. He has worked and studied at the University of Pennsylvania, MIT, the University of Toronto, the Gatsby Unit at University College London, and Carnegie Mellon University. His research focuses on probabilistic approaches to machine learning and artificial intelligence, and he has published over 250 research papers on these topics. He was co-founder of Geometric Intelligence (now Uber AI Labs) and advises a number of AI and machine learning companies. In 2015, he was elected a Fellow of the Royal Society for his contributions to machine learning.

Joseph Halpern

Joseph Halpern

Cornell University

AAAI-18 Invited Speaker
Talk:
Actual Causality: A Survey

What does it mean that an event C “actually caused” event E?  The problem of defining actual causation goes beyond mere philosophical speculation.  For example, in many legal arguments, it is precisely what  needs to be established in order to determine responsibility. (What exactly was the actual cause of the car accident or the medical problem?) The philosophy literature has been struggling with the problem of defining causality since the days of Hume, in the 1700s. Many of the definitions have been couched in terms of counterfactuals. (C is a cause of E if, had C not happened, then E would not have happened.) In 2001, Judea Pearl and I introduced a new definition of actual cause, using Pearl’s notion of structural equations to model counterfactuals. The definition has been revised twice since then, extended to deal with notions like “responsibility” and “blame”, and applied in databases and program verification. I survey the last 15 years of work here, including joint work with Judea Pearl, Hana Chockler, and Chris Hitchcock. The talk will be completely self-contained.

Joseph Halpern received a Ph.D. in mathematics from Harvard after spending two years as the head of the Mathematics Department at Bawku Secondary School, in Ghana.  After a postdoc at MIT and 14 years at the IBM Almaden Research Center (and serving as a consulting professor at Stanford), he joined the CS Department at Cornell in 1996, and was department chair 2010-14.  Halpern is a Fellow of AAAI, AAAS (American Association for the Advancement of Science), the American Academy of Arts and Sciences, ACM, IEEE, the Game Theory Society, and the Society for the Advancement of Economic Theory. He has received the ACM SIGART Autonomous Agents Research Award, the Dijkstra Prize, the Newell Award, the Kampe de Feriet Award, and the Godel Prize. He was editor-in-chief of the Journal of the ACM (1997-2003) and started and continues to be the administrator of CoRR, the computer science section of arxiv.

Percy Liang

Percy Liang

Stanford University

AAAI-18 Invited Speaker
Talk:
How Should We Evaluate Machine Learning for AI?

Machine learning has undoubtedly been hugely successful in driving progress in AI, but it implicitly brings with it the train-test evaluation paradigm. This standard evaluation only encourages behavior that is good on average; it does not ensure robustness as demonstrated by adversarial examples, and it breaks down for tasks such as dialogue that are interactive or do not have a correct answer. In this talk, I will describe alternative evaluation paradigms with a focus on natural language understanding tasks, and discuss ramifications for guiding progress in AI in meaningful directions.

Percy Liang is an Assistant Professor of Computer Science at Stanford University (B.S. from MIT, 2004; Ph.D. from UC Berkeley, 2011).  His research spans machine learning and natural language processing, with the goal of developing trustworthy agents that can communicate effectively with people and improve over time through interaction.  Specific topics include question answering, dialogue, program induction, interactive learning, and reliable machine learning.  His awards include the IJCAI Computers and Thought Award (2016), an NSF CAREER Award (2016), a Sloan Research Fellowship (2015), and a Microsoft Research Faculty Fellowship (2014).

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