Patrick McDaniel is the William L. Weiss Professor of Information and Communications Technology and Director of the Institute for Networking and Security Research in the School of Electrical Engineering and Computer Science at the Pennsylvania State University. Professor McDaniel is also a Fellow of the IEEE and ACM and serves as the program manager and lead scientist for the Army Research Laboratory’s Cyber-Security Collaborative Research Alliance. Dr. McDaniel’s research focuses on a wide range of topics in computer and network security and technical public policy, with particular interests in mobile device security, adversarial machine learning, systems security, program analysis for security, and the integrity and security of election systems.
Professor Boneh heads the applied cryptography group and co-direct the computer security lab. Professor Boneh’s research focuses on applications of cryptography to computer security. His work includes cryptosystems with novel properties, web security, security for mobile devices, and cryptanalysis. He is the author of over a hundred publications in the field and is a Packard and Alfred P. Sloan fellow. He is a recipient of the 2014 ACM prize and the 2013 Godel prize. In 2011 Dr. Boneh received the Ishii award for industry education innovation. Professor Boneh received his Ph.D from Princeton University and joined Stanford in 1997.
Dr. Chaudhuri received a Bachelor’s of Technology degree in Computer Science and Engineering in 2002 from the Indian Institute of Technology, Kanpur, and a PhD in Computer Science from UC Berkeley in 2007. After a stint as a postdoctoral researcher at the Information Theory and Applications Center at UC San Diego, she joined the CSE department at UCSD as an assistant professor in 2010. She is a recipient of the NSF CAREER Award in 2013, and a Hellman Faculty Fellowship in 2012. Chaudhuri’s research is on the design and analysis of machine-learning algorithms and their applications. In particular, her interests lie in clustering, online learning, and privacy-preserving machine-learning, and applications of machine-learning and algorithms to practical problems in other areas.
David Evans is a Professor of Computer Science at the University of Virginia and leader of the Security Research Group. He is the author of an open computer science textbook (http://www.computingbook.org) and a children’s book on combinatorics and computability (http://www.dori-mic.org). He won the Outstanding Faculty Award from the State Council of Higher Education for Virginia, and was Program Co-Chair for the 24th ACM Conference on Computer and Communications Security (CCS 2017) and the 30th (2009) and 31st (2010) IEEE Symposia on Security and Privacy. He has SB, SM and PhD degrees in Computer Science from MIT and has been a faculty member at the University of Virginia since 1999.
Dr. 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 focuses on methods for learning richly-structured statistical models from limited supervision, most recently in the context of semantic parsing in natural language processing. He won a best student paper at the International Conference on Machine Learning in 2008, received the NSF, GAANN, and NDSEG fellowships, and is also a 2010 Siebel Scholar.
Dawn Song is a Professor in the Department of Electrical Engineering and Computer Science at UC Berkeley. Her research interest lies in deep learning, security, and blockchain. She has studied diverse security and privacy issues in computer systems and networks, including areas ranging from software security, networking security, distributed systems security, applied cryptography, blockchain and smart contracts, to the intersection of machine learning and security. She is the recipient of various awards including the MacArthur Fellowship, the Guggenheim Fellowship, the NSF CAREER Award, the Alfred P. Sloan Research Fellowship, the MIT Technology Review TR-35 Award, the George Tallman Ladd Research Award, the Okawa Foundation Research Award, the Li Ka Shing Foundation Women in Science Distinguished Lecture Series Award, the Faculty Research Award from IBM, Google and other major tech companies, and Best Paper Awards from top conferences in Computer Security and Deep Learning. She obtained her Ph.D. degree from UC Berkeley. Prior to joining UC Berkeley as a faculty, she was a faculty at Carnegie Mellon University from 2002 to 2007.