Dr. Edward Albert Feigenbaum
Professor of Computer Science and Co-Scientific Director of the Knowledge Systems Laboratory
Stanford University, USA

Dr. Jennifer Widom
Fletcher Jones Professor, Computer Science and Electrical Engineering
Stanford University, USA

Dr. Anuj Dawar
Professor of Logic and Algorithms, University of Cambridge, UK

Dr. Edward Feigenbaum, Kumagai Professor of Computer Science Emeritus, Stanford University
ACM Turing Award laureate

Title of Talk: Advice to Young Scientists: How to Find Great Research Problems in Artificial Intelligence

Abstract: This talk will give a synthesis of the history and current work of Artificial Intelligence research, viewed in three dimensions that called spectrums. The first spectrum is "Cognitive to Perceptual." The AI field studies, models, and ultimately builds artifacts across a huge spectrum of intelligent cognitive and perceptual behaviors. The second spectrum is Knowledge versus Search (exploring the question of where lies the power of AI techniques). The third spectrum is "What-To-How." exploring AI applications versus other computer applications. The talk concludes with advice to young scientists: three suggestions for choosing research problems along these three "spectrums."

Prof. Edward Feigenbaum was born in Weehawken, New Jersey, in 1936. He holds a B.S. (1956) and Ph.D. (1960), both from Carnegie Mellon University. His dissertation was supervised by legendary computer pioneer Herb Simon and explored a pioneering computer simulation of human learning. Feigenbaum is a pioneer in the field of artificial intelligence and is often known as "the father of expert systems." He founded the Knowledge Systems Laboratory at Stanford University and is currently a professor emeritus of computer science there. In 1994, Feigenbaum received the ACM Turing Award. From 1994 to 1997, he was Chief Scientist of the U.S. Air Force. He is a member of the National Academy of Engineering and the American Academy of Arts and Sciences.

Dr. Jennifer Widom, Fletcher Jones Professor, Computer Science and Electrical Engineering
Senior Associate Dean for Faculty and Academic Affairs, School of Engineering, Stanford University

Title of Talk: Inflection Points in Research and Teaching

Abstract: Several noteworthy "inflection points" have occurred during my approximately 30 years of computer science research and 20 years of teaching. On the research side, I'll recount some fruitful early collaborations (and competitions), and I'll explain a set of principles that have become career-long basic tenets of my approach to research. On the education side, I'll revisit the unforgettable experience of creating and teaching one of Stanford's first free online classes, launching the "MOOC" revolution going strong today.

Dr. Jennifer Widom is the Fletcher Jones Professor of Computer Science and Electrical Engineering at Stanford University, and the Engineering School's Senior Associate Dean for Faculty and Academic Affairs. She received her Bachelor's degree from the Indiana University Jacobs School of Music and her Computer Science Ph.D. from Cornell University. She was a Research Staff Member at the IBM Almaden Research Center before joining the Stanford faculty in 1993. She is an ACM Fellow and a member of the National Academy of Engineering and the American Academy of Arts & Sciences. She received the ACM SIGMOD Edgar F. Codd Innovations Award in 2007 and the ACM-W Athena Lecturer Award in 2015. Her research interests span many aspects of nontraditional data management.

Dr. Anuj Dawar, Professor of Logic and Algorithms, University of Cambridge, UK

Title of Talk: Abstraction, Complexity and Symmetry

Abstract: It has been said that computer science is the science of abstraction. The key to designing effective computational systems is to forget or hide unnecessary detail. An important strand of work in theoretical computer science aims at building and describing suitable abstractions. Yet, another important strand of work, in computational complexity, which has thrown up some of the most challenging problems in the field - such as the P vs, NP problem - is described at a very low level of abstraction. I claim that the lack of abstraction often hides important issues. In this talk, I explore some ways in which abstraction and complexity interact that have prompted an interesting study of symmetries in data.

Dr. Anuj Dawar is Professor of Logic and Algorithms at the University of Cambridge. He serves as President of the European Association for Computer Science Logic (EACSL) and a member of the ACM SigLog Executive. He obtained a first degree at IIT, Delhi and a Masters degree at the University of Delaware before completing his PhD at the University of Pennsylvania in 1993. He has been a member of the faculty at Cambridge since 1999. His research focus is in theoretical computer science, particularly where methods from logic and combinatorics intersect in the study of algorithms.