Emily Therese Cloyd, Director, Center for Public Engagement with Science and Technology, AAAS
Emily Therese Cloyd is the director for the American Association for the Advancement of Science Center for Public Engagement with Science and Technology. Emily oversees all Center programming, including the AAAS Leshner Leadership Institute and the Communicating Science program. Prior to joining AAAS in 2016, Emily led engagement and outreach for the U.S. Global Change Research Program, served as a Knauss Marine Policy Fellow at the National Oceanic and Atmospheric Administration, and studied the use of ecological models in Great Lakes management. She holds a Master’s in Conservation Biology (SUNY College of Environmental Science and Forestry) and a Bachelor’s in Plant Biology (University of Michigan). Emily is always up for a paddle on the Potomac or Anacostia Rivers, especially if it is in a dragon boat, and recently hiked the Tour du Mont Blanc. Follow her on Twitter @EngageClimate.
Heather Lynch, IACS Endowed Chair for Ecology & Evolution, Stony Brook University
John Zimmerman, Tang Family Professor of Artificial Intelligence and Human-Computer Interaction, Carnegie Mellon University
Brian Scassellati, A. Bartlett Giamatti Professor of Computer Science, Cognitive Science, and Mechanical Engineering, Yale University
Omalabake Adenle, Director, AJA.LA Studios
Benjamin Grewe, Professor of Systems Circuits and Neuroinformatics, ETH Zurich
Carolyn Rose, Professor of Computer Science, Carnegie Mellon University
William Smart, Professor in the Robotics Program, Oregon State University
Biplav Srivastava, Professor, Artificial Intelligence Institute, University of South Carolina
Nicholas Mattei, Assistant Professor of Computer Science, Tulane University
Michael Littman, Professor of Computer Science and Co-Director of Humanity-Centered Robotics Initiative, Brown University
Anita Nikolich, University of Illinois – Urbana Champaign
Lyle Ungar, Professor of Computer and Information Science, University of Pennsylvania
Venkataraman Sundareswaran, MCHC Fellow, AI & Machine Learning, World Economic Forum
Sundar is an Artificial Intelligence Fellow at the World Economic Forum, where he is co-creating a governance framework with a multi-stakeholder community for the use of Chatbots in healthcare. He represents Mitsubishi Chemical Holdings Corporation in this role at the Forum’s Centre for the Fourth Industrial Revolution. Sundar is a seasoned technologist with research, development, P&L and executive leadership experience. With a Master’s degree in Natural Language Understanding and a PhD in Computer Vision, Sundar made numerous research contributions in robotics, neural networks, human computer interaction, virtual/augmented reality and autonomous vehicles, prior to taking leadership roles in advanced technology production facilities. He is passionate about responsible deployment of novel technologies in societally important areas such as healthcare.
Advances in deep learning have enabled the development of intelligent agents that have exhibited a remarkable tendency to localize and recognize concepts and events in visual data for enhanced human-machine interaction. However, they tend to experience errors when faced with scenes or examples beyond their initial training environment. Hence, they fail to adapt to new domains without significant retraining with large amounts of annotated data. Current algorithms are trained in an inductive learning environment where they use data-driven models to learn associations between input observations with a fixed set of known classes. In this talk, we look at ways to overcome these limitations by moving to an open world setting by decoupling the ideas of recognition and reasoning. Building upon the compositional representation offered by Grenander's Pattern Theory formalism, we show that attention and commonsense knowledge can be used to enable the self-supervised discovery of novel actions in unconstrained environments. We also show that these compositional representations can be used for training question answering models without the need for any human annotation and hence build collaborative agents that can function in complex real-world environments.
Sathyanarayanan Aakur is an Assistant Professor in the Department of Computer Science at Oklahoma State University. He works primarily on commonsense reasoning in computer vision tasks for open domain understanding to offer deeper, meaningful interaction beyond training semantics. Sathya received his Ph.D. in Computer Science and Masters in Management Information Systems from the University of South Florida in 2019 and 2015, respectively. Sathya is exploring new approaches for visually grounded commonsense reasoning for understanding multimodal data across various domains to improve human-machine interaction via natural language and visual cues. His work has been presented at various leading venues such as CVPR, ECCV and WACV to name a few. More details about his research interests and publications can be obtained at https://saakur.github.io/.
Kartik Talamadupula, IBM Research - AI
Kartik Talamadupula is a Research Scientist at IBM Research. He received a Ph.D. in Computer Science from Arizona State University in 2014. His research focuses on applying sequential decision making techniques to real-world problems, and has encompassed the fields of automated planning, human-robot interaction, crowdsourcing, dialog systems, reinforcement learning, natural language processing, knowledge graphs, and automation of software processes. Kartik has organized several workshops and technical events at international AI venues; served as an SPC for AAAI and an Area Chair for IJCAI; and has co-chaired the ICAPS 2019 Tutorials program and the AAAI 2020 and AAAI 2021 demonstrations programs. He is the Program Co-Chair for Applications at ICAPS 2021. Kartik is currently an appointed officer of the ACM Special Interest Group on AI (SIGAI).
Shreyansh Bhatt, Amazon
Nearly 80% of the Americans agree that there is a skill gap and it could leave a damaging $2.5 trillion impact on the US economy in the next decade. Learners are starting to adapt online education for skill development during the current pandemic. However, one of the biggest challenges is the ability of a learner to practice a skill. In this talk, we will demonstrate how Alexa can assist a learner to practice a conversational skill. We can provide aggregated expertise of a number of experts and a learner can practically have access to multiple experts to practice the skill based on a learner's background and a learner's current knowledge state. A knowledge tracing algorithm uses a learner's past and current interaction with Alexa and updates the learner's current knowledge state and to provide personalized skill practice.
Shreyansh is a Machine Learning Scientist at Amazon. After finishing PhD, Shreyansh joined Amazon where he is currently working on using machine learning to provide enhanced skill development.
Candace Thille, Amazon
Candace is currently a director of Learning Science and Engineer team at Amazon where she focuses on providing scalable adult workforce skill development. Candace Thille is also a senior research fellow in the Office of the Vice Provost for Online Learning and an assistant professor in the Graduate School of Education at Stanford University. She is the founding director of the Open Learning Initiative at Carnegie Mellon University and at Stanford University. The focus of her work is in applying the results from research in the science of learning to the design and evaluation of open web-based learning environments.
Dr. Thille serves as a redesign scholar for the National Center for Academic Transformation; as a fellow of the International Society for Design and Development in Education; on the Assessment 2020 Task Force of the American Board of Internal Medicine; on the advisory committee for the Association of American Universities STEM initiative; on the advisory committee for the NSF Directorate for Education and Human Resources; and on the board of directors of the Association of American Colleges and Universities. She served on the U.S. Department of Education working group, co-authoring the National Education Technology Plan, and on the working group of the President’s Council of Advisors on Science and Technology that produced the Engage to Excel report.
Jinjin Zhao, Amazon
Jinjin is a Machine Learning Scientist at Amazon. Jinjin works on the intersection of computer science, machine learning, and education domain to enhanced skill development.
Dr. Imed Zitouni, Google
Imed Zitouni is a director of engineering at Google leading efforts on NLU to enhance and enable capabilities for Semantic Search and Assistant leveraging the power of the Knowledge Graph. Before joining Google in 2019, Imed was at Microsoft leading the NLU and the Conversation Engine effort for the digital assistant Cortana. Prior to joining Microsoft in 2012, Imed was a Senior Researcher at IBM Watson for almost a decade, working on several NLP initiatives including the Watson initiative around informatics extraction, language modeling and automatic machine translation. Prior to IBM, Imed was a research member at Bell Labs, Lucent Technologies, for half a dozen years working on speech recognition, language modeling and spoken dialog systems. Imed received his M.Sc. and Ph.D. from the University-of-Nancy1 and INRIA in France. He also obtained a MEng degree in computer science from ENSI in Tunisia.
Imed is the Editor-in-Chief of ACM Transactions on Asian and Low-Resources Language Processing. He is a senior member of IEEE, served as a board member of the IEEE Speech and Language Processing Technical Committee, and is the associate editor of IEEE Trans. on Audio, Speech and Language Processing. He is also the Information Officer of the ACL SIG on Semitic-Languages and served as chair as well as reviewing-committee-member of several conferences and journals in the area of machine learning, information retrieval and natural language technologies. Imed is the author/co-author of two books, half-dozen book-chapters as well as more than 100 patents and scientific papers.
Dr. Ullas Nambiar, Accenture
Dr Ullas Nambiar is Principal Director Artificial Intelligence at Accenture. He and his team are focused on building pre-emptive solutions for Accenture and their customers by leveraging algorithms and tools in AI & ML.
Ullas has over 20 years of experience in leading R&D teams and delivering technology led innovations. He is an accomplished researcher in AI and Knowledge Management with experience in building sustainable innovations for all markets.
Prior to Accenture, he served was Chief Technology Officer at Zensar Technologies (2016-19), AVP - Data Science at Myntra (2015-16), Scientist & Head – Analytics at EMC (2012-15), Research Scientist at IBM (2006-12) and Software Analyst at L&T Infotech (1997-99).
Ullas received his PhD in Computer Science from Arizona State University in 2005, a BE in Computer Science from M.S. University of Baroda in 1997, and recently a Diploma in Innovation and Business Excellence from UC Berkeley Haas School of Business in 2015.
Ullas has delivered several keynotes at industry bodies & academic conferences, published 50+ research papers, chaired several workshops in AI & Knowledge Management, and has 10 patents granted by USPTO. Ullas is an ACM Senior Member and Distinguished Speaker.
More details about his research interests and publications can be obtained at https://sites.google.com/site/ubnambiar/.
Luis is a Distinguished Research Staff Member at the IBM T.J. Watson Research Center, where additionally he is a member of the IBM Academy of Technology, and Senior Manager of the Conversational Systems department, which conducts research ranging from speech transcription and synthesis, knowledge graph induction to interactive dialog systems. For most of his career, Luis has been with IBM Research, however he spent 3 years at IBM Watson, the business unit that IBM created to commercialize its interests in artificial intelligence, where he led two teams that launched two new IBM offerings - Watson Concept Insights and Watson Virtual Agent. At IBM Research, Luis led a multiyear program on the design of error correcting codes for computer memory, which resulted in technology that for more than ten years has been used by IBM's mainframe and POWER computers, including RAIM, the world's most reliable memory system, implemented all IBM zSeries systems. Luis core scientific training is on information and coding theory and has published in subjects ranging from multiterminal Shannon theory to data compression algorithms, the Shannon theory of memories and coding theory for new memory technologies. At the present moment he is particularly interested in the use of extensions of information theory as the foundation for representation learning, a line of research that he believes will fundamentally alter the trajectory of research in deep learning by bringing to bear a key results from information theory that will speed up our fundamental understanding of it. Luis obtained his Bachelor in Electrical Engineering form the Universidad Autonoma de San Luis Potosi, Mexico, and his MSc and PhD degrees from Cornell University in 1998 and 2000, respectively.
Joey Yip, Artificial Intelligence Institute, University of South Carolina
Joey Yip is a third year PhD student at the Artificial Intelligence Institute (AII), University of South Carolina.
Joey (and his research) is primarily driven by the notion of intertwining machine with deep learning and knowledge graph (knowledge creation and semantic web) with natural language understanding in Conversational AI and Big Data analytics for health and social good.
Prof. Amit Sheth , AI Institute, University of South Carolina
Professor Sheth's current interests include Artificial Intelligence (esp. knowledge graphs, NLP, deep learning, knowledge-enhanced learning, conversational AI- especially chatbots for health and education), Semantic Web, Physical/IoT-Cyber-Social-Clinical Big Data, Augmented Personalized Health, AI and Big Data applications (in health and life sciences, social good, disaster management, etc.).
Prof. Dezhi Wu, Department of Integrated Information Technology, University of South Carolina
Dr. Dezhi Wu is an associate professor at the Department of Integrated Information Technology, University of South Carolina. Her primary research interest is human-computer interaction that applies to artificial intelligence, health IT/health informatics, cybersecurity, and cyberlearning domains. Her research focuses on designing, implementing and evaluating novel user interfaces and applications for transformative user experiences to bridge the gaps between users and today’s evolving smart technologies. She was the recipient of the global technology award “AIS Technology Challenge Award,” and she is the former Chair for AIS Special Interest Group on Human-Computer Interaction (SIGHCI).
Prof. Barnett Berry , College of Education, University of South Carolina
Barnett Berry, a research professor at the University of South Carolina, is the founding director of the recently launched ALL4SC (Accelerator for Learning and Leadership for South Carolina). Barnett returns to his home state of South Carolina to lead ALL4SC – a systems approach to marshalling resources and expertise from within the entire university to serve the needs of the state’s highest-needs children, schools and communities.
In the 1980s Barnett served as a high school teacher and think tank analyst at the RAND Corporation and in the 1990s as a senior policy leader for the South Carolina Department of Education, a professor of education at UofSC and a consultant to the National Commission on Teaching and America’s Future. In 1999, he founded the Center for Teaching Quality to ignite change inside of public education driven by the ideas and practices of teachers. In 2003, the Center for Teaching Quality launched the nation’s first virtual network of teacher leaders, which grew to 10,000 members leading to more opportunities for those who teach students everyday to lead without leaving their classrooms.
Barnett has authored more than 120 peer-reviewed journal articles and book chapters related to understanding the teaching profession, improving teachers’ working conditions and spurring their leadership, and developing more effective education policies grounded in realities of our public schools. His two books, TEACHING 2030 and Teacherpreneurs: Innovative Teachers Who Lead But Don’t Leave, frame a bold vision for the profession’s future and a system of learning for young people and those who teach them. His most recent research has focused on the role a school’s teaching and learning conditions plays in teacher retention and school performance and how teacher-led learning and leadership leads to better and more equitable outcomes for students.
Prof. Ronda Hughes, College of Nursing, University of South Carolina
Dr. Hughes is the Director of the Center for Nursing Leadership, the Director for the Executive Doctorate of Nursing Practice program, and an Associate Professor. Her career has included a rich blend of roles in academe, administration, and research. Prior to coming to UofSC, she was an Associate Professor at Marquette University in Milwaukee (2010 – 2016), after almost two decades of service in the U.S. Department of Health and Human Services, primarily at the Agency for Healthcare Research and Quality and the Health Resources and Services Administration. She is a Fellow in the American Academy of Nursing.
Dr. Hughes received her PhD in Health Policy and Health Services Research from the Johns Hopkins School of Hygiene and Public Health (2001), a Masters in Health Science in Health Policy and Management with a minor in biomedical ethics from the Johns Hopkins School of Hygiene and Public Health (1993), and a Bachelor’s of Science in Nursing from Boston University (1988).
Prof. Bryant Walker Smith, School of Law, University of South Carolina
Bryant Walker Smith is an associate professor in the School of Law and (by courtesy) the School of Engineering at the University of South Carolina. He is also an affiliate scholar at the Center for Internet and Society at Stanford Law School and co-director of the University of Michigan Project on Law and Mobility. He previously led the Emerging Technology Law Committee of the Transportation Research Board of the National Academies and served on the US Department of Transportation's Advisory Committee on Automation in Transportation.
Trained as a lawyer and an engineer, Bryant advises cities, states, countries, and the United Nations on emerging transport technologies. He coauthored the globally influential levels of driving automation, drafted the leading model law for automated driving in the United States, and taught the first legal courses dedicated to automated driving (in 2012), hyperloops, and flying taxis. His students have developed best practices for regulating scooters, and he is writing on what it means to be a trustworthy company. His publications are available at newlypossible.org.
Before joining the University of South Carolina, Bryant led the legal aspects of automated driving program at Stanford University, clerked for the Hon. Evan J. Wallach at the United States Court of International Trade, and worked as a fellow at the European Bank for Reconstruction and Development. He holds both an LL.M. in International Legal Studies and a J.D. (cum laude) from New York University School of Law and a B.S. in civil engineering from the University of Wisconsin. Prior to his legal career, Bryant worked as a transportation engineer.