Keynotes and Panel
Salton Award Keynote - Information Retrieval as Augmentation of Human Intelligence
Speaker: ChengXiang Zhai
Donald Biggar Willett Professor in Engineering
Department of Computer Science
University of Illinois at Urbana-Champaign
Abstract: In this talk, I will present a personal reflection on Information Retrieval (IR) from the perspective of how to optimize the intelligence of an IR system. My view of the intelligence of an IR system has evolved substantially over the years, leading to several different lines of my research work, including using Natural Language Processing to improve IR, probabilistic language models for IR, axiomatic retrieval framework for optimizing relevance modeling, various forms of user feedback, interface card models for interactive IR, and simulation of users. I will provide a brief overview of these lines of work and explain how they reflect a shift of the view of intelligent IR from system-centered to user-centered, resulting in a general view of an intelligent IR system as an intelligent knowledge assistant that can collaborate with a user via natural interactions to augment the user’s intelligence for finishing a task while minimizing the overall effort of the user. Realizing the vision of such an intelligent knowledge assistant requires tackling many challenges such as how to extend a search engine to support interactive analysis, comparison, and synthesis of all relevant information and data so as extract actionable knowledge and insights needed for finishing a task or making complex decisions, how to mathematically model users and their behaviors, how to design intelligent algorithms to enable a system to collaborate and communicate with a user effectively using multi-mode interactions, and how to maximize the combined intelligence of a human and a computer system in general. Addressing these new challenges opens many exciting opportunities for innovative interdisciplinary IR research intersecting with many other areas such as Natural Language Processing, Data Mining, Machine Learning, and Human-Computer Interaction.
Bio: ChengXiang Zhai is a Donald Biggar Willett Professor in Engineering of Department of Computer Science at the University of Illinois at Urbana-Champaign, where he also holds a joint appointment at the Carl R. Woese Institute for Genomic Biology, Department of Statistics, and the School of Information Sciences. He received a Ph.D. in Computer Science from Nanjing University in 1990, and a Ph.D. in Language and Information Technologies from Carnegie Mellon University in 2002. He worked at Clairvoyance Corp. as a Research Scientist and a Senior Research Scientist from 1997 to 2000. His research interests are in the general area of intelligent information systems, including specifically intelligent information retrieval, data mining, natural language processing, machine learning, and their applications in domains such as biomedical informatics, and intelligent education systems. He has published over 300 papers in these areas and holds 6 patents. He offers two Massive Open Online Courses (MOOCs) on Coursera covering Text Retrieval and Search Engines and Text Mining and Analytics, respectively, and is a key contributor of the Lemur text retrieval and mining toolkit. He served as Associate Editors for major journals in multiple areas including information retrieval (ACM TOIS, IPM), data mining (ACM TKDD), and medical informatics (BMC MIDM), Program Co-Chairs of NAACL HLT’07, SIGIR’09, and WWW’15, and Conference Co-Chairs of CIKM’16, WSDM’18, and IEEE BigData’20. He is an ACM Fellow and a member of ACM SIGIR Academy. He received numerous awards, including ACM SIGIR Test of Time Paper Award (three times), the 2004 Presidential Early Career Award for Scientists and Engineers (PECASE), Alfred P. Sloan Research Fellowship, IBM Faculty Award, HP Innovation Research Award, Microsoft Beyond Search Research Award, UIUC Rose Award for Teaching Excellence, and UIUC Campus Award for Excellence in Graduate Student Mentoring. He has graduated 37 PhD students and over 50 MS students.
(Mis)Informed about the Pandemic: Information Sources and Knowledge about COVID-19
Speaker: Eszter Hargittai
Professor & Chair of Internet Use and Society
Institute of Communication and Media Research
University of Zurich
Abstract: Rarely is access to reliable information as important as during a global health crisis. What information sources were people using to learn about COVID-19 during the early days of the pandemic in 2020 and how did people of different backgrounds compare in these experiences? Drawing on national survey data collected in three countries (Italy, Switzerland, the United States) in the first month of lockdowns, this talk looks at people’s use of various media as sources about the virus. It then compares information sources used with people’s level of knowledge and their misperceptions about the virus. The talk considers both traditional media such as television and radio, as well as diverse online resources including different social media platforms in how people informed themselves about COVID-19. Findings suggest considerable variation in how people kept informed about the pandemic and how these related to their understanding of the virus. The talk emphasizes the importance of paying attention to people’s socio-demographic background when looking at the implications of what information sources they consult.
Bio: Eszter Hargittai is Professor and holds the Chair of Internet Use and Society at the Department of Communication and Media Research of the University of Zurich. She is past Fellow of the Center for Advanced Study in the Behavioral Sciences at Stanford and Harvard’s Berkman Klein Center for Internet & Society. Hargittai’s research looks at how people may benefit from their digital media uses with a particular focus on how differences in people’s Web-use skills influence what they do online. Hargittai’s research has been supported by the U.S. National Science Foundation, the John D. and Catherine T. MacArthur Foundation, the Alfred P. Sloan Foundation, Google, Microsoft Research, Facebook, and Merck, among others. Her work has received awards from several professional associations and for her teaching, she received the Galbut Outstanding Faculty Mentor Award of the School of Communication at Northwestern University. She is Fellow of the International Communication Association. She is editor, most recently, of Research Exposed: How Empirical Social Science Gets Done in the Digital Age (Columbia University Press 2021). She has given invited talks in 18 countries on five continents. Hargittai holds a PhD in Sociology from Princeton University and a BA in Sociology from Smith College. She tweets @eszter.
Exploring the Future of Dialogue Technologies
Speaker: Hang Li
Director of AI Lab
Abstract: Nowadays searches and recommendations have become the major means for people to obtain information in their daily lives. Natural language dialogue, including voice dialogue and text dialogue, as a new way of information access, has also begun to emerge amidst other technologies. We believe that dialogue technologies in the future, with the goal of providing useful information and knowledge to people, will have the following trends: domain-specific, multimodal, and neural-symbolic. In this talk, I will share our recent experiences in developing dialogue systems and my views on the challenges and opportunities of the area. First, I will introduce the `smart desk lamp’ product produced by ByteDance. This system, based on AI technologies including voice dialogue, provides primary school students with question answering, homework grading, and homework tutoring features. It serves as a tool to help the students better understand and master the knowledge and skills they have learned at school. After that, I will discuss the role of neural symbol processing in dialogue systems and explain the related technologies we have developed recently. Finally, I will summarize the challenges and open questions in the future development of dialogue technologies.
Bio: Hang Li is a Director of the AI Lab at ByteDance Technology, the company of TikTok, Toutiao and other products. He is also a Fellow of ACL, Fellow of IEEE, and Distinguished Scientist of ACM. He graduated from Kyoto University and earned his Ph.D. from the University of Tokyo. He worked at NEC Research as researcher and at Microsoft Asia Research as senior researcher and research manager. He was a director and chief scientist of Noah’s Ark Lab of Huawei Technologies prior he joined ByteDance. His research interest lies in the intersections of natural language processing, information retrieval, machine learning, and data mining.
REASONABLE RESISTANCE!! Ethics and Politics Where Information Retrieval Meets Personalized Recommendation
Speaker: Helen Nissenbaum
Director, Digital Life Initiative
Department of Information Science
Panel: The Future of SIGIR
- Will there still be SIGIR?
- Post-COVID SIGIR
- Open access
- Review model
- Review criteria and quality
- The roles of industry and academia