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Association for the Advancement of Artificial Intelligence

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AAAI-13 Invited Speakers

AAAI-13 Invited Speakers

AAAI-13 will feature the following series of distinguished speakers:

Keynote Address
Grounded Language Learning

Raymond J. Mooney (University of Texas at Austin)

Most approaches to semantics in computational linguistics represent meaning in terms of words or abstract symbols. Grounded-language research bases the meaning of natural language on perception and/or action in the (real or virtual) world. Machine learning has become the most effective approach to constructing natural-language systems; however, current methods require a great deal of laboriously annotated training data. Ideally, a computer would be able to acquire language like a child, by being exposed to language in the context of a relevant but ambiguous environment, thereby grounding its learning in perception and action. We will review recent research in grounded language learning and discuss future directions.

Raymond J. MooneyRaymond J. Mooney is a professor in the Department of Computer Science at the University of Texas at Austin. He received his Ph.D. in 1988 from the University of Illinois at Urbana/Champaign. He is an author of over 150 published research papers, primarily in the areas of machine learning and natural language processing. He was the president of the International Machine Learning Society from 2008-2011, program cochair for AAAI 2006, and is a AAAI and ACM Fellow. His recent research has focused on learning for natural-language processing, statistical relational learning, active transfer learning, and connecting language, perception and action.

AAAI 2013 Robert S. Engelmore Lecture Award Lecture
Giving Data a Voice

Deborah L. McGuinness (Rensselaer Polytechnic Institute)

As data explodes on the web and as interest increases in using data to inform decisions, it becomes increasingly important to be able to “converse” with data. This talk will look at some techniques for interacting with data to create smart and actionable applications. I will highlight usage of semantic approaches to improve discovery, integration, visualization, and explanation. I will attempt to highlight promising directions, needs, and potential for future data exploration settings where users and data may enter into collaborative investigations.

Deborah L. McGuinnessDeborah L. McGuinness is the Tetherless World Senior Constellation Chair, professor of computer and cognitive science, and founding director of Rensselaer Polytechnic Institute’s Web Science Research Center. McGuinness is a leading authority on the semantic web and has been working in knowledge representation and reasoning environments for over 25 years. Her primary research focuses on making smart systems understandable and usable by a broad range of people. She leads active research efforts in explanation, trust, ontology environments, and provenance. McGuinness is also known for semantic application environments, particularly for eScience frameworks such as the semantic escience framework and demonstration portals including many in natural science and health informatics settings. McGuinness also founded McGuinness Associates — a small woman owned business — that consults on semantic applications in a wide range or areas with recent focus on health and environmental informatics, context-aware mobile computing, and next generation journalism.

AAAI-13 Invited Talk
Fighting the Tuberculosis Pandemic Using Machine Learning

Kristin P. Bennett and the TB-Insight Team (Rensselaer Polytechnic Institute)

Kristin P. BennettTuberculosis (TB) infects one third of the world’s population and is the second leading cause of death from a single infectious agent worldwide. The emergence of drug resistant TB remains a constant threat. We examine how machine learning methods can help control tuberculosis. DNA fingerprints of Mycobacterium tuberculosis complex bacteria (Mtb) are routinely gathered from TB patient isolates for every tuberculosis patient in the United States to support TB tracking and control efforts. We develop learning models to predict the genetic lineages of Mtb based on DNA fingerprints. Mining of tuberculosis patient surveillance data with respect to these genetic lineages helps discover outbreaks, improve TB control, and reveal Mtb phenotype differences. We discuss learning- and visualization-based tools to support public health efforts towards TB control in development for the New York City Health Department.

Kristin P. Bennett is a professor in the Mathematical Sciences and Computer Science Departments at Rensselaer Polytechnic Institute. As an active member of the machine learning, data mining, and operations research communities, she has served as present or past associate or guest editor for ACM Transactions on Knowledge Discovery from Data, SIAM Journal on Optimization, Naval Research Logistics, Machine Learning Journal, IEEE Transactions on Neural Networks, and Journal on Machine Learning Research. She served as program chair of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. She has a Ph.D. in computer sciences from the University of Wisconsin-Madison. She has been researching mathematical-programming approaches to machine learning and their applications to problems in chemistry, biology, epidemiology, engineering, and business since 1989. She founded and directs the “TB-Insight” project which uses molecular epidemiology to help track and control tuberculosis.

AAAI-13 Invited Talk
Aerial Robot Swarms

Vijay Kumar (University of Pennsylvania)

Autonomous microaerial robots can operate in three-dimensional unstructured environments, and offer many opportunities for environmental monitoring, search and rescue, and first response. I will describe the challenges in developing small, agile robots and our recent work in the areas of (1) control and planning, (2) state estimation and mapping, and (3) coordinating large teams of robots, with applications to cooperative manipulation and transport, construction, and exploration and mapping.

Vijay KumarVijay Kumar is the UPS Foundation professor in the School of Engineering and Applied Science at the University of Pennsylvania, and on sabbatical leave at White House Office of Science and Technology Policy where he serves as the assistant director for robotics and cyber physical systems. He received his bachelors of technology from the Indian Institute of Technology, Kanpur and his Ph.D. from The Ohio State University in 1987. He has been on the faculty in the Department of Mechanical Engineering and Applied Mechanics with a secondary appointment in the Department of Computer and Information Science at the University of Pennsylvania since 1987.

Kumar served as the deputy dean for Research in the School of Engineering and Applied Science from 2000-2004. He directed the GRASP Laboratory, a multidisciplinary robotics and perception laboratory, from 1998-2004. He was the chairman of the Department of Mechanical Engineering and Applied Mechanics from 2005–2008. He then served as the deputy dean for education in the School of Engineering and Applied Science from 2008-2012.

Kumar is a Fellow of the American Society of Mechanical Engineers (ASME) and the Institution of Electrical and Electronic Engineers (IEEE). He has served on the editorial boards of the IEEE Transactions on Robotics and Automation, IEEE Transactions on Automation Science and Engineering, ASME Journal of Mechanical Design, the ASME Journal of Mechanisms and Robotics and the Springer Tract in Advanced Robotics (STAR). He is the recipient of the 1991 National Science Foundation Presidential Young Investigator award, the 1996 Lindback Award for Distinguished Teaching (University of Pennsylvania), the 1997 Freudenstein Award for significant accomplishments in mechanisms and robotics, the 2012 ASME Mechanisms and Robotics award, the 2012 IEEE Robotics and Automation Society distinguished service award and a 2012 World Technology Network award. He has won best paper awards at DARS 2002, ICRA 2004, ICRA 2011, and RSS 2011 and has advised doctoral students who have won Best Student Paper awards at ICRA 2008, RSS 2009, and DARS 2010. He is also a distinguished lecturer in the IEEE Robotics and Automation Society and an elected member of the Robotics and Automation Society Administrative Committee (2007-2012). His research interests are in robotics, specifically multirobot systems, and microaerial vehicles.

AAAI-13 Invited Talk
Socially Assistive Robotics: Human-Robot Interaction Methods for Creating Robots that Care

Maja J. Mataric’ (University of Southern California)

Socially assistive robotics (SAR) is a new field of intelligent robotics that focuses on developing machines capable of assisting users through social rather than physical interaction. The robot’s physical embodiment is at the heart of SAR’s effectiveness, as it leverages the inherently human tendency to engage with lifelike (but not necessarily humanlike or otherwise biomimetic) social behavior. People readily ascribe intention, personality, and emotion to robots; SAR leverages this engagement stemming from non-contact social interaction involving speech, gesture, movement demonstration and imitation, and encouragement, to develop robots capable of monitoring, motivating, and sustaining user activities and improving human learning, training, performance and health outcomes. Human-robot interaction (HRI) for SAR is a growing multifaceted research area at the intersection of engineering, health sciences, neuroscience, social, and cognitive sciences. This talk will describe our research into embodiment, modeling and steering social dynamics, and long-term user adaptation for SAR. The research will be grounded in projects involving analysis of multi-modal activity data, modeling personality and engagement, formalizing social use of space and non-verbal communication, and personalizing the interaction with the user over a period of months, among others. The presented methods and algorithms will be validated on implemented SAR systems evaluated by human subject cohorts from a variety of user populations, including stroke patients, children with autism spectrum disorder, and elderly with Alzheimers and other forms of dementia.

Maja MataricMaja Mataric is a professor and Chan Soon-Shiong chair of computer science, neuroscience, and pediatrics at the University of Southern California, founding director of the USC Center for Robotics and Embedded Systems, codirector of the USC Robotics Research Lab and vice dean for Research in the USC Viterbi School of Engineering. She received her MS and PhD in computer science and AI from the Massachusetts Institute of Technolgy and her BS in computer science from the University of Kansas. She is a Fellow of AAAS and IEEE, and recipient of the Presidential Awards for Excellence in Science, Mathematics and Engineering Mentoring, the Anita Borg Institute Women of Vision Award for Innovation, Okawa Foundation Award, NSF Career Award, the MIT TR35 Innovation Award, and the IEEE Robotics and Automation Society Early Career Award. Mataric is an associate editor of three major journals, has published extensively, and served on the NSF CISE Advisory Committee, among other advisory boards. She is actively involved in K-12 educational outreach to engage student interest in science, technology, engineering, and math (STEM) topics. Her research into socially assistive robotics aims to endow robots with the ability to help people through individual noncontact assistance in convalescence, rehabilitation, training, and education, with applications for children with autism spectrum disorders, survivors of stroke and traumatic brain injury, and individuals with Alzheimer’s Disease and other forms of dementia.

AAAI-13 Invited Talk
Poker AI: Algorithms for Creating Game-Theoretic Strategies for Large Incomplete-Information Games

Tuomas Sandholm (Carnegie Mellon University)

Incomplete-information games — such as most auctions, negotiations, and future (cyber)security settings — cannot be solved using minimax search even in principle. Completely different algorithms were needed. A dramatic scalability leap has occurred in our ability to solve such games over the last seven years, fueled largely by the Annual Computer Poker Competition. I will discuss the key domain-independent techniques that enabled this leap, including automated abstraction techniques and approaches for mitigating the issues that they raise, new equilibrium-finding algorithms, safe opponent exploitation methods, techniques that use qualitative knowledge as an extra input, and endgame solving techniques. I will finish by benchmarking poker programs against the best human poker professionals and by discussing what humans can learn from the programs.

Tuomas SandholmTuomas Sandholm is a professor at Carnegie Mellon University in the Computer Science Department, Machine Learning Department, and the CMU/UPitt joint Ph.D. program in computational biology. He has published over 450 papers on market design, game theory, search and integer programming, auctions and exchanges, automated negotiation and contracting, coalition formation, voting, safe exchange, normative models of bounded rationality, resource-bounded reasoning, and machine learning. In parallel with his academic career, he was founder, chairman, and CTO/chief scientist of CombineNet, Inc. from 1997 until its acquisition in 2010; during this period the company commercialized 800 of the world’s largest-scale generalized combinatorial auctions, totaling over $60 billion. Sandholm’s algorithms also run the US-wide UNOS kidney exchange. He is the founder and CEO of Optimized Markets, Inc., a startup that is bringing a new paradigm to advertising sales and scheduling. Among his many honors are the NSF Career Award, inaugural ACM Autonomous Agents Research Award, Sloan Fellowship, Carnegie Science Center Award for Excellence, and Computers and Thought Award. He is a Fellow of the ACM and AAAI.

Joint AAAI/IAAI Invited Talk
Telling Stories at Internet Scale

Larry Birnbaum (Northwestern University)

Taking full advantage of the massive scale of “big data” will require technologies for analyzing and communicating these data to people, in terms they can understand and act on, at an equally massive scale. The automatic generation of narratives from data offers the promise of meeting this critical need. Our technology, which leverages human editorial judgment at scale, is today generating millions of stories from data, including highly personalized stories, in domains varying from business operations, to sports, education, medicine, and finance. The resulting narratives are often indistinguishable from those written by human analysts and writers.

Larry BirnbaumLarry Birnbaum is an associate professor of electrical engineering and computer science, and of journalism, at Northwestern University, as well as a founder and chief scientific advisor of Narrative Science Inc. At Northwestern, Birnbaum is cocirector of the Intelligent Information Lab, and a founder of the Knight Lab, an interdisciplinary center for innovation in technology, media, and journalism. He formerly served as chair of the Computer Science Department. At Narrative Science, Birnbaum focuses on technology and IP. He received his BS and PhD degrees from Yale, and was on the faculty there before joining Northwestern.

IAAI-13 Invited Talk
Building a Mind for Life

Lawrence Hunter (University of Colorado)

Sometimes two hard problems are easier than one. Making sense of the explosion to data about life and human health that arises from exponentially improving DNA sequencing technology is one of the primary scientific challenges of our time. Artificial intelligence is based in the equally profound question of what constitutes a mind and how one could be constructed artificially. These two challenges mutually inform and constrain each other. Computational biology needs tools that relate enormous experimental results to vast amounts of relevant prior knowledge in ways that illuminate the mechanisms underlying the phenomena under study. This is a fundamentally semantic process that requires programs represent complex, incomplete and partially incorrect knowledge, reason about it and its relation to experimental results, and engage in effective, ongoing scientific communication about hypotheses. This is an “AI-complete” problem. However, the domain is special in several ways: (1) All the relevant knowledge is explicit, and can be found in the roughly 10,000 textbooks and 20 million journal articles that make up the biomedical literature. (2) The biomedical research community has a long-standing and effective project in ontology development, provide an increasingly comprehensive conceptual foundation of entities, processes, functions, locations, relations and more that are explicitly defined, rigorously organized and maintained by domain experts. These biomedical ontologies underpin several large-scale annotation projects, the results of which are available in standardized semantic formats like RDF and OWL. (3) There is a large community of biomedical research scientists desperate for effective means of explaining and contextualizing their data. Although they are demanding and less interested in the technological means than the scientific ends, a thoughtful human community eager to interact in an extended intellectual partnership with programs is a rare and valuable resource for AI research. This combination of factors suggests that the first wildly acknowledged genuine AI may think about biology. I will talk about progress to date and prospects in building a mind for life.

Lawrence HunterLawrence Hunter is the director of the University of Colorado’s Computational Bioscience Program and a professor of pharmacology (School of Medicine) and computer science (Boulder). He received a Ph.D. in computer science from Yale University in 1989, and then joined the National Institutes of Health as a staff scientist, first at the National Library of Medicine and then at the National Cancer Institute, before coming to Colorado in 2000. Hunter is widely recognized as one of the founders of bioinformatics; he served as the first president of the International Society for Computational Biology (ISCB), and created several of the most important conferences in the field, including ISMB, PSB and VizBi. Hunter’s research interests span a wide range of areas, from cognitive science to rational drug design. He has published more than 100 scientific papers, holds two patents and has been elected a fellow of both the ISCB and the American College of Medical Informatics. His primary focus recently has been the integration of natural language processing, knowledge representation, machine learning and advanced visualization techniques to address challenges in interpreting data generated by high throughput molecular biology.

IAAI-13 Invited Speakers
Applying AI to Vehicle Failure Monitoring for Autonomous Vehicle Durability Testing

Nestor Rychtyckyj and Howie (Hao) Yang (Ford Motor Company)

Recently Ford Motor Company has deployed autonomous testing vehicles for automated driving at the Michigan Proving Grounds. These nonproduction (prototype) vehicles are used for durability testing over a variety of extreme terrain and conditions that are very difficult on human drivers. A vehicle with autonomous driving capability can be driven over the same difficult terrain numerous times at a consistent rate of speed. However, the ability of the human driver to detect issues with the car during these tests is still a major requirement. In this talk we will discuss the development and deployment of an AI-based system that is used to monitor the vehicle for any mechanical failures. This system processes signals from a variety of different in-vehicle sensors that provide data about the vehicle performance during each test run. The AI system analyzes the data and sends messages to the vehicle tracking center if any issues occur with the vehicle and the operator at the tracking center can stop the vehicle to prevent any potential damage. Various factors such a noise, vibration, speed, torque are all used by the system and the necessary knowledge to reason with data on a real-time basis that decides what information needs to be sent to the control center is all incorporated into the system. This application shows how AI technology can be integrated into a real-world system that provides tangible benefits for both Ford and its customers.

Nestor RychtyckyjNestor Rychtyckyj is a technical expert in artificial intelligence at Ford Motor Company in Dearborn, Michigan. He received his Ph.D. in computer science from Wayne State University in Detroit, Michigan. His research focuses on the application of AI-based systems for vehicle assembly process planning, scheduling, vehicle diagnostics, ergonomics and plant floor applications. Rychtyckyj is also an adjunct professor at Lawrence Technological University and a senior member of AAAI and IEEE. He chaired the Innovative Applications of Artificial Intelligence (IAAI) conference in 2010 and was cochair for Cybernetics at the IEEE Systems, Man and Cybernetics conference in 2011.

Howie YangHowie (Hao) Yang is an IT supervisor at Ford Motor Company in Dearborn, Michigan. He leads a group to develop software solutions to support Ford powertrain engineers. His responsibilities also cover software solutions for the Global Design Studio organization and AI applications for vehicle verification within product development. He has been leading the development of AI for autonomous vehicle testing. His other work related to AI at Ford includes case-based Reasoning for a stamping feasibility study and knowledge-based engineering for powertrain design and manufacturing. He received his Ph.D. in mechanical engineering from University of Missouri-Rolla with minor in computer science. His Ph.D. research was focused on case-based reasoning and computer-aided manufacturing.

EAAI-13 Invited Speaker
Learning in the Lab at Midnight: Experiences from Teaching AI at Berkeley and Online

Dan Klein (University of California, Berkeley)

Where does learning really happen? Only a little happens in lecture; most students learn much more working with friends in the lab at midnight. The modern student experience increasingly revolves around coursework, peer assistance, and asynchronous interactions — not lectures, textbooks, and office hours. With these trends only increasing as enrollments rise and online channels emerge, how should we design our AI courses?

I’ll talk about the best answers we’ve found so far for the Berkeley AI course. One key component of our approach is a set of thematically coherent, autograded projects that engage students and integrate with lectures in an ongoing way. More generally, I’ll focus on several questions that have shaped our course, including: What should the role of a modern lecture be? What’s the balance between cooperative learning and competition? When is an autograder more useful than a human TA? Why are students even taking AI in the first place? Finally, I’ll talk about how technology that we originally developed for pedagogical purposes, such as rich autograding, has helped the course scale from tens to hundreds of students on campus and now to tens of thousands online.

Our experiences have resulted in a large number of re-usable materials, which we’re always excited to share. I’ll conclude with a discussion of how other instructors can take advantage of our lectures, interactive assignments, and autograded projects, which have already been used by over a hundred AI courses.

Dan KleinDan Klein (PhD Stanford) is an associate professor of computer science at the University of California, Berkeley. He works on natural language processing, machine learning, and other areas of AI. There may come a day when his research systems rise up and give his talks for him. Until then, he teaches a lot, from an undergraduate AI class with over 300 people per semester to an online version with over 30,000. Klein’s research honors include a Microsoft Faculty Fellowship, a Sloan Fellowship, an NSF CAREER award, the ACM Grace Murray Hopper award for his work on grammar induction, and best paper awards at the ACL, NAACL, and EMNLP conferences. He is particularly proud to have been recognized with multiple teaching honors, including the University of California, Berkeley’s Distinguished Teaching Award.

EAAI-13 Invited Speaker
Broader and Earlier Access to Machine Learning

Paula Matuszek (Villanova University)

In a world of big data, everyone needs machine learning. How do we teach it to everyone? At Villanova we have embarked on an NSF-funded project to develop a set of machine-learning modules for students from a wide variety of domains. Our goal is to have students who can make intelligent and effective use of appropriate machine learning algorithms and techniques as tools. In this talk I will describe our grant and share the state of the modules we have so far, in order to stimulate discussion about how we can teach machine learning more broadly.

Paula Matuszek is currently an adjunct professor at Villanova University, where she uses more than twenty years of experience developing intelligent systems for industry as a basis for courses in a variety of artificial intelligence topics. She is particularly interested in ways to apply powerful AI tools to problems in a variety of domains.