Workshop Summary
Artificial intelligence (AI) and Human Computer Interaction (HCI) share common roots and early work on conversational agents has laid the foundation for both fields. However, in subsequent decades the initial tight connection between the fields has become less pronounced. The recent rise of deep learning has revolutionized AI and has led to a raft of practical methods and tools that significantly impact areas outside of core-AI. In particular, modern AI techniques now power new ways for machines and humans to interact. Thus it is timely to investigate how modern AI can propel HCI research in new ways and how HCI research can help direct AI developments.
This workshop offers a forum for researchers to discuss new opportunities that lie in bringing modern AI methods into HCI research, identifying important problems to investigate, showcasing computational and scientific methods that can be applied, and sharing datasets and tools that are already available or proposing those that should be further developed.
The topics we are interested in including deep learning methods for understanding and modeling human behaviors and enabling new interaction modalities, hybrid intelligence that combine human and machine intelligence to solve difficult tasks, and tools and methods for interaction data curation and large-scale data-driven design. At the core of these topics, we want to start the conversation on how data-driven and data-centric approaches of modern AI can impact HCI.
Call For Participation
Please visit the Call for Participation page for detail on contributions solicited. The submission site is at OpenReview.
Important Dates
Paper Submission Open on OpenReview: December 16, 2019
Paper Submission Deadline: February 11, 2020
Paper Notification Date: February 28, 2020
Workshop Date: April 25, 2020
Workshop Venue
The workshop will be organized along with CHI 2020 in Honolulu, Hawaii.
Organizers
Yang Li, Ph.D., is a Staff Research Scientist at Google Research, and an affiliate Associate Professor in UW CSE. He earned a Ph.D. degree in Computer Science from the Chinese Academy of Sciences, and conducted postdoctoral research at UC Berkeley EECS. His current research focuses on using deep learning methods to model human intelligence in interaction tasks. Yang led the development of next app prediction at Google that is in use by tens of millions of users, which pioneered on-device interactive ML on Android. Yang has extensively published in top venues across both the HCI and ML fields, including CHI, UIST, ICML, NeuraIPS, ICLR, CVPR and KDD, and has constantly served on the program committees of top-tier HCI venues including SCs and ACs at CHI and reviewers at ICML and NeuraIPS.
Ranjitha Kumar, Ph.D., is an Assistant Professor in the Department of Computer Science and (by courtesy) the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. She runs the Data Driven Design Group, where she and her students leverage data mining and machine learning to address the central challenge of creating good user experiences: tying design decisions to desired outcomes. Ranjitha received her PhD from the Department of Computer Science at Stanford University. She was formerly the Chief Scientist at Apropose, Inc., a data-driven design company she co-founded.
Walter S. Lasecki, Ph.D., is an Assistant Professor of Computer Science and Engineering at the University of Michigan, Ann Arbor, where he is founding director of the Center for Hybrid Intelligence Systems (HyIntS Center), and leads the Crowds+Machines (CROMA) Lab. Walter's lab creates interactive intelligent systems that are robust enough to be used and trained in real-world settings by combining both human and machine intelligence to exceed the capabilities of either. These systems let people be more productive, and improve access to the world for people with disabilities.
Otmar Hilliges, Ph.D., is an Associate Professor of Computer Science at ETH Zürich. He currently is the head of the Institute for Intelligent Interactive Systems and leads the AIT group. His research interests are in machine perception of human activity, including pose estimation, activity recognition and other forms of input sensing. Furthermore, he is interested in algorithms that can extract high-level concepts such as style and semantic meaning from observations of human activities and algorithms that continuously update individualized user models (based on such data). Prior to joining ETH he was a Researcher at Microsoft Research Cambridge, in the I3D group. He earned a PhD in Computer Science from LMU München, Germany. Otmar broadly publishes in top-tier HCI and ML venues including CHI, UIST, ICLR and CVPR.