BEGIN:VCALENDAR VERSION:2.0 PRODID:-//wp-events-plugin.com//7.2.3.1//EN TZID:America/Los_Angeles X-WR-TIMEZONE:America/Los_Angeles BEGIN:VEVENT UID:253@escience.washington.edu DTSTART;TZID=America/Los_Angeles:20250213T163000 DTEND;TZID=America/Los_Angeles:20250213T172000 DTSTAMP:20250128T014733Z URL:https://escience.washington.edu/events/uw-data-science-seminar-john-ch oe-and-kevin-jamieson/ SUMMARY:UW Data Science Seminar: John Choe and Kevin Jamieson DESCRIPTION:Please join us for the third talk in a special series in the UW Data Science Seminar featuring the AI@UW Seed Grant awardees. The AI@UW S eed Grant projects partner domain researchers from an applied disciplinary field with researchers specializing in AI theory and/or methodology. Thes e projects were supported by the eScience Institute in collaboration with the Office of Research\, the Paul G. Allen School of Computer Science & \; Engineering\, the Information School\, and the NSF Institute for Founda tions of Data Science (IFDS).  This seminar will feature John Choe\, an A ssociate Professor at UW Industrial and Systems Engineering\, and Kevin Ja mieson\, an Associate Professor at UW's Allen School of Computer Science a nd Engineering on Thursday\, February 13th from 4:30 to 5:20 p.m. PT.\n\n\ n\nThe seminar will be held in Hitchcock Hall 132 – Campus Map.\n"Data- Driven Optimization of Stochastic Computational Experiments: Applications in Hazard Risk Assessment"\nAbstract This AI@UW seed grant project develo ped a domain-agnostic data-driven algorithm to optimize stochastic computa tional experiments under computational resource constraints. Stochastic co mputational experiments are ubiquitous in science\, engineering\, and medi cine. This project focused on motivating applications in disaster manageme nt\, where the proposed optimization can potentially save billions of doll ars and many lives from disasters. This project’s optimal adaptive exper iment design algorithm enables computationally efficient hazard risk asses sment (e.g.\, earthquake-induced tsunamis\, landslides)\, thereby informin g decision-making for disaster risk reduction.\n\nBiography: John Choe is an Associate Professor of Industrial and Systems Engineering at the Univer sity of Washington\, Seattle. He is the Director of the Disaster Data Scie nce Lab and the Deputy Director of the Center for Disaster Resilient Commu nities. He received his Ph.D. in Industrial and Operations Engineering and M.A. in Statistics from the University of Michigan\, Ann Arbor. His work has been supported by the U.S. National Science Foundation\, National Inst itute of Environmental Health Sciences\, Centers for Disease Control and P revention\, and other public and private sector organizations.\n\n Kevin J amieson is an Associate Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. His research expl ores how to leverage already-collected data to inform what future measurem ents to make next\, in a closed loop. His work has been recognized by an N SF CAREER award and Amazon Faculty Research award. He received his B.S. fr om the University of Washington\, M.S. from Columbia University\, and Ph.D . from the University of Wisconsin - Madison\, all in electrical engineeri ng.\n\nThe UW Data Science Seminar is an annual lecture series at the Un iversity of Washington that hosts scholars working across applied areas of data science\, such as the sciences\, engineering\, humanities and arts a long with methodological areas in data science\, such as computer science\ , applied math and statistics. Our presenters come from all domain fields and include occasional external speakers from regional partners\, governme ntal agencies and industry.\nThe 2024-2025 seminars will be held in person \, and are free and open to the public. ATTACH;FMTTYPE=image/jpeg:https://escience.washington.edu/wp-content/uploa ds/2025/01/Headshot2_Seminar.png END:VEVENT BEGIN:VTIMEZONE TZID:America/Los_Angeles X-LIC-LOCATION:America/Los_Angeles BEGIN:STANDARD DTSTART:20241103T010000 TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST END:STANDARD END:VTIMEZONE END:VCALENDAR