John Martinis


John Martinis pioneered research on superconducting quantum bits as a graduate student at U.C. Berkeley. He has worked at CEA France, NIST Boulder, and UC Santa Barbara on a variety of low-temperature electronic devices, including electron counters, superconducting amplifiers and radiation detectors. In 2014 he was awarded the London Prize for low-temperature physics research on superconducting qubits. In 2014 he joined the Google quantum-AI team, and now heads the hardware effort to build a useful quantum computer.

Keynote Title: Building a Quantum Computer

Abstract: As microelectronics technology nears the end of exponential growth over time, known as Moore’s law, there is a renewed interest in new computing paradigms such as quantum computing. A key step in the roadmap to build a scientifically or commercially useful quantum computer will be to demonstrate its exponentially growing computing power. I will explain how a 7 by 7 array of superconducting qubits can compute over a huge state space of 2^49 = 560 trillion states, which can only be checked using the world’s largest classical supercomputers. I will present progress towards this “quantum supremacy” experiment.


Peter Mueller

IBM Zurich Research Laboratory

Peter Mueller joined IBM Research as a Research Staff Member in 1988. His research expertise covers broad areas of distributed computing systems architecture, microwave technology, device physics, nano science and modeling. His current field of research is in the areas of quantum technology and data center storage security. Peter is a founding member and was the Chair of the IEEE ComSoc Communications and Information Systems Security Technical Committee (CIS-TC).

Keynote Title: Quantum Information Processing

Abstract: Every track in the call for this conference has potential for future applications of quantum technologies. Quantum technology is more than a next-step development; it is about stepping into a new technology domain. We should all be aware of its roadmap, assess its advantages and benefits, and explore its possibilities for use in our research. This talk will start with a brief look at the quantum technology roadmap in areas such as sensors, communications and computing. For computing, we will depict device building blocks and present the architecture of one of IBM’s quantum-processing devices that is publicly accessible today. Moreover, the concept of quantum information processing on such an architecture will be demonstrated. Finally, a number of critical parameters for quantum computing will be explained.

James Hendler

James Hendler

Rensselaer Polytechnic Institute (RPI)

James Hendler is the Director of the Institute for Data Exploration and Applications and the Tetherless World Professor of Computer, Web and Cognitive Sciences at RPI. He also serves as a Director of the UK’s charitable Web Science Trust. Hendler is coauthor of the recently published “Social Machines: The coming collision of Artificial Intelligence, Social Networking and Humanity” (APress, 2016) and the earlier “Semantic Web for the Working Ontologist” (Elsevier, 2009/2011), “Web Science: Understanding the Emergence of Macro-Level features o the World Wide Web” (Now Press, 2013), and “A Framework for Web Science” (Now Press, 2006). He has also authored over 300 technical papers and articles in the areas of Semantic Web, artificial intelligence, agent-based computing and high performance processing.
One of the originators of the “Semantic Web,” Hendler was the recipient of a 1995 Fulbright Foundation Fellowship, is a former member of the US Air Force Science Advisory Board, and is a Fellow of the American Association for Artificial Intelligence, the British Computer Society, the IEEE and the AAAS. He is also the former Chief Scientist of the Information Systems Office at the US Defense Advanced Research Projects Agency (DARPA) and was awarded a US Air Force Exceptional Civilian Service Medal in 2002. He is also the first computer scientist to serve on the Board of Reviewing editors for Science. In 2010, Hendler was named one of the 20 most innovative professors in America by Playboy magazine and was selected as an “Internet Web Expert” by the US government. In 2012, he was one of the inaugural recipients of the Strata Conference “Big Data” awards for his work on large-scale open government data, and he is a columnist and associate editor of the Big Data journal. In 2013, he was appointed as the Open Data Advisor to New York State and in 2015 appointed a member of the US Homeland Security Science and Technology Advisory Committee. In 2016, Hendler became a member of the National Academies Board on Research Data and Information.

Social Machines: The Coming Collision of Artificial Intelligence, Social Networking, and Humanity

As technology has increasingly brought computing off of the laptop and into our social domain, we see society more and more impacted by the interactions allowed by mobile technologies and increasingly ubiquitous communications. These new sources of data, coupled with new breakthroughs in computation, and especially AI, are opening new vistas for ways that information comes into our world, and how what we do increasingly impacts others. Current social networking sites will be, to the coming generation of social machines, what the early "entertainment" web was to the read/write capabilities once called "Web 2.0." In this talk, we explore some of these trends and some of the promises and challenges of these emerging technologies.

Kevin Leyton-Brown

Kevin Leyton-Brown

University of British Columbia

Kevin Leyton-Brown is a professor of Computer Science at the University of British Columbia and an associate member of the Vancouver School of Economics. He holds a PhD and M.Sc. from Stanford University (2003; 2001) and a B.Sc. from McMaster University (1998). He studies the intersection of computer science and microeconomics, addressing computational problems in economic contexts and incentive issues in multiagent systems. He also applies machine learning to the automated design and analysis of algorithms for solving hard computational problems.
He has co-taught two Coursera courses on "Game Theory" to over half a million students, and has received awards for his teaching at UBC—notably, a 2013/14 Killam Teaching Prize. He is chair of the ACM Special Interest Group on Electronic Commerce, which runs the annual Economics & Computation conference. He serves as an associate editor for the Artificial Intelligence Journal (AIJ), ACM Transactions on Economics and Computation (ACM-TEAC), and AI Access; serves as an advisory board member for the Journal of Artificial Intelligence Research (JAIR, after serving as associate editor for eight years); and was program chair for the ACM Conference on Electronic Commerce (ACM-EC) in 2012. In 2016 he was a visiting researcher at Microsoft Research New England and a visiting professor at Harvard's EconCS group.

Keynote Title: Modeling Human Strategic Behavior: From Behavioral Economics to Deep Learning

Abstract: It is common to assume that players in strategic settings will make decisions in ways predicted by game theory, adopting so-called Nash equilibrium strategies. (These are named after Nobel-prize winner John Nash, the protagonist of the move "Beautiful Mind".) However, experimental studies have demonstrated that Nash equilibrium is often a poor description of human players' behavior, even in the very simple case of unrepeated simultaneous-move interactions. Nevertheless, human behavior in such settings is far from random. Drawing on data from real human play, the field of behavioral game theory has developed a variety of models that aim to capture these patterns. The current state of the art in that literature is a model called quantal cognitive hierarchy. It predicts that agents approximately best respond and explicitly model others' beliefs to a finite depth, grounded in a uniform model of nonstrategic play. We have shown that even stronger models can be built by drawing on ideas from cognitive psychology to better describe nonstrategic behavior. However, this whole approach requires extensive expert knowledge and careful choice of functional form. Deep learning presents an alternative, offering the promise of automatic cognitive modeling. We introduce a novel architecture that allows a single network to generalize across different input and output dimensions by using matrix units rather than scalar units, and show that its performance significantly outperforms that of the previous state of the art.

Ann Cavoukian

Ann Cavoukian

Ryerson University

Ann Cavoukian is the former Information and Privacy Commissioner for the Canadian province of Ontario serving from 1997 to 2014. Dr. Cavoukian is recognized as one of the world’s leading privacy experts. Her Privacy by Design framework seeks to proactively embed privacy into the design specifications of information technologies and business practices, thereby achieving the strongest protection possible. In October 2010, regulators at the conference of International Data Protection and Privacy Commissioners in Jerusalem unanimously passed a Resolution recognizing Privacy by Design as an essential component of fundamental privacy protection. This was followed by the U.S. Federal Trade Commission’s inclusion of Privacy by Design as one of three recommended practices for protecting online privacy – a major validation of its significance. In November 2011, Dr. Cavoukian was ranked as one of the top 25 Women of Influence, recognizing her contribution to the Canadian and global economy. In October 2013, she was named one of the top 100 City Innovators Worldwide by UBM Future Cities for her passionate advocacy of Privacy by Design. In December 2013, the Founding Partners of the Respect Network, the world’s first peer-to-peer network for personal and business clouds, named Dr. Cavoukian as its first Honorary Architect. As of July 1, 2014, she began a new position at Ryerson University as the Executive Director of the Privacy and Big Data Institute – Where Big Data meets Big Privacy.

Keynote Title: Embedding Privacy into Design: An Essential Feature in IoT and AI

Abstract: Privacy is presently under siege. With the growth of ubiquitous computing, online connectivity, social media, wireless/wearable devices, and concern over the direction of Artificial Intelligence, people are being led to believe they have no choice but to give up on privacy. This is not the case! A privacy framework called Privacy by Design will enable our privacy and our freedom, to live well into the future. Dr. Cavoukian dispels the notion that privacy acts as a barrier to public safety, security and innovation. She argues that the limiting paradigm of “zero-sum” – that you can either have privacy or innovation, but not both – is an outdated, win/lose model of approaching the question of privacy in the age of massive surveillance of citizens. Instead a “positive-sum” solution is needed in which the interests of both sides may be met, in a doubly- enabling, “win-win” manner through Privacy by Design (PbD). PbD is predicated on the rejection of zero-sum propositions by proactively identifying the risks and embedding the necessary protective measures into the design and data architecture involved. Her new AI Ethics by Design explores the need to proactively embed an ethical framework on AI developments, in order to maximize the gains: win/win! Dr. Cavoukian has also convened a new International Council on Global Privacy and Security, by Design, to respond to the growing pressures of zero- sum models seeking to advance security at the expense of privacy. Say NO to such win/lose models. She outlines how organizations can embed privacy and security into virtually any system or operation, to achieve positive-sum, win/win outcomes, enabling both privacy and security – not one at the expense of the other. We can do this!

Mohammad S. Obaidat

Mohammad S. Obaidat

Fordham University, United States

Professor Mohammad S. Obaidat (Fellow of IEEE and Fellow of SCS) is an internationally well-known academic/researcher/ scientist. He received his Ph.D. and M. S. degrees in Computer Engineering with a minor in Computer Science from The Ohio State University, Columbus, Ohio, USA. Dr. Obaidat is currently a Full Professor of Computer and Information Science at Fordham University, USA. Among his previous positions are Advisor to the President of Philadelphia University for Research, Development and Information Technology, President of the Society for Molding and Simulation International, SCS, Senior Vice President of SCS, Dean of the College of Engineering at Prince Sultan University, Chair of the Department of Computer and Information Science and Director of the MS Graduate Program in Data Analytics at Fordham university, Chair of the Department of Computer Science and Director of the Graduate Program at Monmouth University. He has received extensive research funding and has published to date over Forty (40) books and over Six Hundreds and Fifty (650) refereed technical articles in scholarly international journals and proceedings of international conferences. Professor Obaidat has served as a consultant for several corporations and organizations worldwide. His research interests are: wireless communications and networks, telecommunications and Networking systems, security of network, information and computer systems, security of e-based systems, performance evaluation of computer systems, algorithms and networks, green ICT, high performance and parallel computing/computers, applied neural networks and pattern recognition, adaptive learning and speech processing.

Keynote Title: Trends and Challenges in Key Enabling Technologies for Smart Homes and Cities and Samples of Our Related Works

The increase of worldwide population, especially in populous countries and cities and the increase migration of citizens to cities have also brought with it challenges in transportation systems, health care, utility’s supplies, learning & education, sensing city dynamics, computing with heterogeneous data sources, managing urban big data, and environmental protection including pollution and others. In this keynote, we will shed some light on the key enabling Information and Communications technology to smart cities and homes. We will also investigate the advances, current trends, challenges and future in the research and development in smart homes and cities. Some of our recent research results, especially the ones related to the use of wireless networks and security for smart and digital homes will be presented. Among these, fire detection schemes in forests. An intelligent system for fire prediction based on wireless sensor networks is presented. This system obtains the probability of fire and fire behavior in a particular area. This information allows firefighters to obtain escape paths and determine strategies to fight the fire. A firefighter can access this information with a portable device on every node of the network.