BEGIN:VCALENDAR METHOD:REQUEST PRODID:Microsoft Exchange Server 2010 VERSION:2.0 BEGIN:VTIMEZONE TZID:US Eastern Standard Time BEGIN:STANDARD DTSTART:16010101T020000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;INTERVAL=1;BYDAY=1SU;BYMONTH=11 END:STANDARD BEGIN:DAYLIGHT DTSTART:16010101T020000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;INTERVAL=1;BYDAY=2SU;BYMONTH=3 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT ORGANIZER;CN="Li, Dongyang";SENT-BY="mailto:jo@purdue.edu":mailto:lidongyang@pu rdue.edu ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=FALSE;CN=bmegradst udents-list@ecn.purdue.edu:mailto:bmegradstudents-list@ecn.purdue.edu ATTENDEE;ROLE=OPT-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=FALSE;CN="Gelfand, Johanna K":mailto:jo@purdue.edu DESCRIPTION;LANGUAGE=en-US:Purdue System Thinkers proudly presents our next Research Colloquium with Dr. Nan Kong. We will meet in WALC room 3138 at 6 PM on Thursday\, Nov 16th\; free food will be provided!\n\nBio: Dr. Nan Kong is Professor and Interim Head of Weldon School of Biomedical Engineer ing at Purdue. He was the former Associate Director for Health Systems at Purdue’s Regenstrief Center for Healthcare Engineering. He graduated wi th B.S. in Automation from Tsinghua University in 1999 and Ph.D. in Indust rial Engineering from University of Pittsburgh in 2006. He joined Purdue B ME in 2007. His primary research is innovating data-driven optimization an d analytics to address challenges in health care delivery.\n\nAbstract: Ag ency for Healthcare Research and Quality (AHRQ) defines a learning health system as a health system in which internal data and experience (e.g.\, se mi-mechanistic model) are systematically integrated with external evidence (e.g.\, observational data)\, and the leant knowledge is put into practic e. Becoming a learning system is increasingly an imperative in healthcare delivery and public health. Among the many challenges faced by learning sy stems is the one challenge on effective use of data\, i.e.\, integrating n ewly acquired data with existing mechanistic understanding of the system t o update intelligence for better controlling the system. This becomes more prevalent as we come out from the COVID-19 pandemic. How can data-driven optimization help? In this talk\, we consider the context of multi-period location-specific resource allocation in infectious disease management. We propose two algorithmic approach to solve multi-period decision problems requiring online training and re-optimization.\n\nThanks\nPurdue System Th inkers\n\n UID:040000008200E00074C5B7101A82E00800000000C43C50FE6A16DA01000000000000000 0100000008D6335379DB02B47B74752170B159D49 SUMMARY;LANGUAGE=en-US:Dr. Nan Kong - Learning public health systems in inf ectious disease management – How can data-driven optimization help? DTSTART;TZID=US Eastern Standard Time:20231116T180000 DTEND;TZID=US Eastern Standard Time:20231116T190000 CLASS:PUBLIC PRIORITY:5 DTSTAMP:20231113T195255Z TRANSP:OPAQUE STATUS:CONFIRMED SEQUENCE:0 LOCATION;LANGUAGE=en-US:WALC Room 3138\; https://purdue-edu.zoom.us/j/57125 09615 X-MICROSOFT-CDO-APPT-SEQUENCE:0 X-MICROSOFT-CDO-OWNERAPPTID:2122017220 X-MICROSOFT-CDO-BUSYSTATUS:TENTATIVE X-MICROSOFT-CDO-INTENDEDSTATUS:BUSY X-MICROSOFT-CDO-ALLDAYEVENT:FALSE X-MICROSOFT-CDO-IMPORTANCE:1 X-MICROSOFT-CDO-INSTTYPE:0 X-MICROSOFT-DONOTFORWARDMEETING:FALSE X-MICROSOFT-DISALLOW-COUNTER:FALSE X-MICROSOFT-REQUESTEDATTENDANCEMODE:DEFAULT X-MICROSOFT-LOCATIONS:[{"DisplayName":"WALC Room 3138"\,"LocationAnnotation ":""\,"LocationUri":""\,"LocationStreet":""\,"LocationCity":""\,"LocationS tate":""\,"LocationCountry":""\,"LocationPostalCode":""\,"LocationFullAddr ess":""}\,{"DisplayName":"https://purdue-edu.zoom.us/j/5712509615"\,"Locat ionAnnotation":""\,"LocationUri":""\,"LocationStreet":""\,"LocationCity":" "\,"LocationState":""\,"LocationCountry":""\,"LocationPostalCode":""\,"Loc ationFullAddress":""}] BEGIN:VALARM DESCRIPTION:REMINDER TRIGGER;RELATED=START:-PT15M ACTION:DISPLAY END:VALARM END:VEVENT END:VCALENDAR