*Please join us for our monthly Chicago Data Night, cohosted by [ChiData](https://www.google.com/url?q=https://uchi-db.github.io/chidatasite/&sa=D&source=calendar&ust=1778521514486810&usg=AOvVaw0l0Cm8AgMzkQPfDjSCVAU2) and the [DSI](https://www.google.com/url?q=https://datascience.uchicago.edu/&sa=D&source=calendar&ust=1778521514486810&usg=AOvVaw0dS0C5LLYzkj_UdL83aXxl)*, *in partnership with* *[TeamWorking by TechNexus.](https://teamworking.vc/)* *Practitioners, academics, and aficionados within the Chicago area are all invited to be part of a community at the intersection of industry and academia, brought together by a mutual interest in data. Each month, guest speakers will cover a specific data-related topic. Chicago Data Night is generously sponsored by Booth's Center for Applied AI and Every.*
*Hors d’oeuvres and drinks will be provided. Admission is free, and we strongly encourage an RSVP to attend.*
NEW MEETING LOCATION!
TechNexus
[20 N Wacker Drive](google.com/maps/place/TeamWorking+by+TechNexus/data=!4m2!3m1!1s0x0:0xe4423cbdbedf304d?sa=X&ved=1t:2428&ictx=111), 12th Floor
Chicago, IL 60606
AGENDA
5:30pm: Networking Reception
6:00pm: Welcome Remarks
6:05pm: Guest Speaker
6:50pm: Q&A
7:00pm: Networking Reception
7:30pm: Event Concludes
Title: Multiagent Challenges in Team Sports Analytics
Abstract: This talk will present several challenges and opportunities within the area of team sports analytics, specifically related to active areas of research within reinforcement learning and multiagent systems. These connections are most prevalent in “invasion games” such as ice hockey, basketball, and soccer, defined as where teams must invade opponent territory to be successful. The talk will detail relevant problems along two axes, short-term strategy (coaching) and long-term planning (management), where problems of player/policy evaluation, strategy, and coalition formation are most present. I will discuss various examples of data with which these problems are typically addressed and show how insights from reinforcement learning and multiagent systems are particularly well suited for these tasks.
Bio: David Radke is a Senior Research Scientist with the Chicago Blackhawks of the NHL. His group focuses on research at the frontier of artificial intelligence and ice hockey to better understand and improve player evaluation, roster construction, and in-game strategy. Dr. Radke holds a PhD in artificial intelligence from the University of Waterloo where he worked with Dr. Kate Larson and Dr. Tim Brecht understanding the impact of agent teams in multiagent reinforcement learning. While at the University of Waterloo, he served as an assistant captain of the USports men’s ice hockey team for two seasons. During his undergraduate education at Colorado College, Dr. Radke studied computer science and math while also playing NCAA Division 1 ice hockey.
