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DTSTART;TZID=America/Detroit:20241125T150000
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SUMMARY:Workshop / Seminar:[DEICP] Demonstrating a Commitment to Diversity
DESCRIPTION:Diversity\, equity\, and inclusion (DEI) aptitude is now highly valued by many employers\, both within and beyond academe. This interactive workshop will:Show how employers are evaluating DEI in job interviewsProvide opportunities for reflection on how you demonstrate your commitment to DEIProvide time for students to practice answering common interview questions related to DEIThis workshop is designed primarily for graduate students seeking non-academic jobs beyond the professoriate.\n\n\nLearning objectives:\nReflect on ways you are committed to DEI in your research\, teaching\, engagement\, leadership\, or other areas\nArticulate your commitment to DEILearn about the different methods employers are using to assess job candidates’ commitment to diversity\nThis workshop is designed for University of Michigan master's students\, doctoral students\, and postdoctoral fellows. For faculty and staff\, please contact rackhampdeworkshops@umich.edu to see if we can accommodate your attendance.
UID:123615-21851168@events.umich.edu
URL:https://events.umich.edu/event/123615
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Sessions
LOCATION:Virtual via Zoom
CONTACT:
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DTSTAMP:20241114T154219
DTSTART;TZID=America/Detroit:20241125T150000
DTEND;TZID=America/Detroit:20241125T160000
SUMMARY:Workshop / Seminar:Data Analytics for Decision Support in Public Health Policy
DESCRIPTION:About the speaker: Kevin B. Smith is a PhD candidate in the department of Industrial and Operations Engineering at the University of Michigan. His research is focused on applying data analytics tools to improve outcomes of problems in healthcare and public health policy. The mission of his research is to inform policymakers and the public of the critical factors that impact decisions with applications in organ allocation\, pandemic risk planning\, clinical kidney stone care\, and AI/ML monitoring thus far. Additionally\, he is motivated to develop models which improve decision making outcomes in these settings. His work aims to address systemic shortcomings of current healthcare and public health policies and create models that improve outcomes using interpretable and accessible results.\n\nAbstract: At the intersection of the acceleration of the availability of data and efficient data analytic techniques is a unique moment to reconsider the way the US policy dictates community-level care delivery. Despite increased spending on novel treatments among other key innovations\, shortcomings of the US’ policy delivery system have included poor outcomes of the country’s most vulnerable and the continuation of systemic barriers to better health. In this talk\, I will demonstrate the use of data analytics to support these challenging policy contexts. In particular\, I will present the results of a study wherein we developed a decision support tool designed to enable public health decision makers to rank US counties most at-need of scarce medical resources. I will also describe some opportunities and challenges we discovered when using publicly-available data and data analytics to guide decision making in this public health policy setting. I will conclude with a discussion of some other work supporting policy making decisions by using analytics on observational healthcare data and monitoring AI/ML models that are used in medical decision making settings.
UID:129128-21862171@events.umich.edu
URL:https://events.umich.edu/event/129128
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Graduate Students,Industrial And Operations Engineering,Ioephdstudents,Ioephdtalks,Michigan Engineering,seminar
LOCATION:Industrial and Operations Engineering Building - 2717
CONTACT:
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