KDD Panels

Location: Room 101A

Environment Day Panel: August 8th (Tuesday) 10am – 12pm

Moderator: Bistra Dilkina

Opportunities and Challenges in Leveraging Data Mining and AI for the Environment

Invited Panelists: Dr. Caleb Robinson (Microsoft AI for Good Research Lab), Dr. Anthony Schultz (ESRI), Dr. Dave Thau (WWF), Dr. Rose Yu (UCSD)

Dr. Caleb Robinson (Microsoft AI for Good Research Lab)

Dr. Anthony Schultz (ESRI)

Dr. Dave Thau (WWF)

Dr. Rose Yu (UCSD)

AI for Science Panel: August 8th (Tuesday) 1:30 – 3:30pm

Moderator: Wei Ding (UMass Boston) 

An open conversation with a reflection on the past, present, and future of AI-enabled scientific discoveries. 

Discussion on a new frontier in AI, where novel AI frameworks will drive scientific inquiry, suggest novel experiments, elucidate new theories, and thus revolutionize the traditional discovery process across multiple scientific disciplines. 

Invited Panelists: Aidong Zhang (University of Virginia), Vipin Kumar (university of Minnesota), Jiawei Han ( University of Illinois Urbana-Champaign) , Nitesh Chawla ( University of Notre Dame ), and Raj Acharya (NSF) 


  • 1:30 – 1:45 (15 minutes) Opening remarks by Aidong and Vipin to summarize the NSF Sponsored Workshop on AI-Enabled Scientific Revolution, March 8-9, 2023
  • 1:45 – 2:00 (15 minutes) Self-introductions and research statements by Jiawei, Nitesh, and Raj (5 minutes / panelist)
  • 2:00 – 3:30 (90 minutes) Q&A session guided by the 12 questions below between the panel and the audience.

AI-centric discussion:

  1. What impact would you like to see AI have on other areas of science?
  2. What are the key limitations of state-of-the-art AI in the context of scientific problems?
  3. What AI insights from one scientific discipline would you expect to generalize to others?
  4. What can AI do to advance other scientific areas?

Science-centric discussion:

  1. What areas of science have been transformed by AI?
  2. What are the key gaps in current scientific methods that can be filled by AI?
  3. From AlphaFold to ChatGPT, what is next on the horizon?
  4. What are the fundamental barriers to getting there?

Vision Questions:

  1. What are the priority research directions for AI to enable scientific revolution?
  2. What AI insights from one scientific discipline would you expect to generalize to others?
  3. What can the federal government/industry/researchers do to catalyze AI-enabled scientific revolution?
  4. How can AI education and community outreach accelerate this process?

Health Panel From Data to Discoveries: Harnessing AI for Biology, Health and Medicine: August 9th (Wednesday) 10am – 12pm

Moderator: Wei Wang

  • 10:00 – 10:05 Opening
  • 10:05 – 10:35 Keynote by Dr. Belinda Seto (Deputy Director, NIH Office of Data Science Strategy (ODSS))
  • 10:35 – 10:50 Invited Talk by Dr. Chris Kinsinger (Assistant Director for Catalytic Data Resources, NIH Common Fund)
  • 10:50 – 11:05 Invited Talk by Dr. Chris Yang (Program Director, NSF CISE/IIS)
  • 11:05 – 11:20 Invited Talk by Dr. Sorin Draghici (Program Director, NSF CISE/IIS)
  • 11:20 – 12:00 Panel Discussion

The Impact of LLMs on Education: August 9th (Wednesday) 1:30 – 3:30pm

Moderator: Johannes Gehrke

Invited Panelists: Madeleine Udell (Stanford University), Shawn Jansepar (Khan Academy & Khanmigo), Eric Horvitz (Microsoft), Ed H. Chi (Google DeepMind)

Madeleine Udell

Madeleine Udell is Assistant Professor of Management Science and Engineering at Stanford University, with an affiliation with the Institute for Computational and Mathematical Engineering (ICME) and courtesy appointment in Electrical Engineering, and Associate Professor with tenure (on leave) of Operations Research and Information Engineering and Richard and Sybil Smith Sesquicentennial Fellow at Cornell University. Her research aims to accelerate and simplify large-scale data analysis and optimization, with impact on challenges in healthcare, finance, marketing, operations, and engineering systems design, among others. Her work in optimization seeks to detect and exploit novel structures, leading to faster and more memory-efficient algorithms. Her work in machine learning centers on challenges of data preprocessing, interpretability, and causality, which are critical to practical application of machine learning methods. She is a Kavli Fellow (2023) and Alfred P. Sloan Research Fellow (2021). Other awards include a National Science Foundation CAREER award (2020) and an Office of Naval Research (ONR) Young Investigator Award (2020).

Shawn Jansepar

Shawn Jansepar is Director of Engineering at Khan Academy and product leader of the Khanmigo Platform. He is Vice-Chair of the Board at Modo Carshare, and Advisory Council Member at Ethos Lab, which inspires youth to transform community and shift culture through STEAM. Prior to joining Khan Academy, Shawn led Engineering at Mobify in Vancouver, growing it from a small startup to a large team which was eventually acquired by Salesforce. He holds a Bachelor of Science degree in Computer Science from Simon Fraser University.

Eric Horvitz

Eric Horvitz serves as Microsoft’s Chief Scientific Officer. He spearheads company-wide initiatives, navigating opportunities and challenges at the confluence of scientific frontiers, technology, and society, including strategic efforts in AI, medicine, and the biosciences. He has pursued in his research principles and applications of AI amidst the complexities of the open world, with efforts on harnessing probability and utility in machine learning and reasoning, models of bounded rationality, and principles and mechanisms for supporting human-AI interaction and complementarity. His efforts and collaborations have led to fielded systems in healthcare, transportation, ecommerce, operating systems, and aerospace. He received the Feigenbaum Prize and Allen Newell Prize for contributions in AI. He received the CHI Academy honor for innovations at the intersection of AI and human-computer interaction. He has been elected to the National Academy of Engineering (NAE) and is a fellow of the Association of Computing Machinery (ACM) and Association for the Advancement of AI (AAAI). He serves on the President’s Council of Advisors on Science and Technology (PCAST). He founded and chairs Microsoft’s Aether committee, established the One Hundred Year Study on AI, and co-founded and serves as board chair of the Partnership on AI.

Ed H. Chi

Ed H. Chi is a Distinguished Scientist at Google DeepMind, leading machine learning research teams working on large language models (LaMDA/Bard), neural recommendations, and reliable machine learning. With 39 patents and ~200 research articles, he is also known for research on user behavior in web and social media.  As the Research Platform Lead, at Google he helped launched Bard, a conversational AI experiment, and delivered significant improvements for YouTube, News, Ads, Google Play Store with >660 product improvements since 2013. 

Prior to Google, he was Area Manager and Principal Scientist at Xerox Palo Alto Research Center‘s Augmented Social Cognition Group in researching how social computing systems help groups of people to remember, think and reason. Ed earned his 3 degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota. Inducted as an ACM Fellow and into the CHI Academy, he also received a 20-year Test of Time award for research in information visualization. He has been featured and quoted in the press, including the EconomistTime Magazine, LA Times, and the Associated Press.  An avid golfer, swimmer, photographer and snowboarder in his spare time, he also has a blackbelt in Taekwondo.