BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//UM//UM*Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:America/Detroit
TZURL:http://tzurl.org/zoneinfo/America/Detroit
X-LIC-LOCATION:America/Detroit
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20070311T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20071104T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20250114T110443
DTSTART;TZID=America/Detroit:20250128T160000
DTEND;TZID=America/Detroit:20250128T170000
SUMMARY:Workshop / Seminar:Statistics Department Seminar Series: Yuchen Wu\, Postdoctoral Research Fellow\, Department of Statistics and Data Science\, The Wharton School\, University of Pennsylvania
DESCRIPTION:Abstract:  Sampling from a target distribution is a recurring theme in statistics and generative artificial intelligence (AI). In statistics\, posterior sampling offers a flexible inferential framework\, enabling uncertainty quantification\, probabilistic prediction\, as well as the estimation of intractable quantities. In generative AI\, sampling aims to generate unseen instances that emulate a target population\, such as the natural distributions of texts\, images\, and molecules. \n\nIn this talk\, I will present my works on designing provably efficient sampling algorithms\, addressing challenges in both statistics and generative AI. (1) In the first part\, I will focus on posterior sampling for Bayes sparse regression. In general\, such posteriors are high-dimensional and contain many modes\, making them challenging to sample from. To address this\, we develop a novel sampling algorithm based on decomposing the target posterior into a log-concave mixture of simple distributions\, reducing sampling from a complex distribution to sampling from a tractable log-concave one. We establish provable guarantees for our method in a challenging regime that was previously intractable. (2) In the second part\, I will describe a training-free acceleration method for diffusion models\, which are deep generative models that underpin cutting-edge applications such as AlphaFold\, DALL-E and Sora. Our approach is simple to implement\, wraps around any pre-trained diffusion model\, and comes with a provable convergence rate that strengthens prior theoretical results. We demonstrate the effectiveness of our method on several real-world image generation tasks. \n\nLastly\, I will outline my vision for bridging the fields of statistics and generative AI\, exploring how insights from one domain can drive progress in the other.\n\nhttps://wuyc0114.github.io/
UID:129242-21862370@events.umich.edu
URL:https://events.umich.edu/event/129242
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:seminar
LOCATION:West Hall - 411
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20250212T123203
DTSTART;TZID=America/Detroit:20250128T160000
DTEND;TZID=America/Detroit:20250128T164500
SUMMARY:Careers / Jobs:Virtual Veteran Career Exploration and Recruitment Event (Students Welcome)
DESCRIPTION:Great opportunity for accounting and business students to learn along side transitioning Veterans about career opportunities related to auditing &amp\; accounting for government entities\, for profit entities\, and not-for-profit entities\, advisory and consulting roles\, information technology to include IT auditing\, tax\, and valuation services. Although the presentation is directed to Veterans\, anyone is welcome to attend to learn more about these career fields.Virtual Veteran Career Exploration and Recruitment EventJanuary 28th\, 4pm - 445pm ESTPanel of Sikich Service Line leadersRegistration link: https://events.teams.microsoft.com/event/73c65f6c-55e3-4895-900a-1b7e30abe586@9b9c6b7e-397f-419e-82dd-db42bc3cad88  
UID:131813-21869280@events.umich.edu
URL:https://events.umich.edu/event/131813
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:
LOCATION:
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20250103T100910
DTSTART;TZID=America/Detroit:20250128T163000
DTEND;TZID=America/Detroit:20250128T180000
SUMMARY:Workshop / Seminar:Robotics Pathways and Career Speaker Series
DESCRIPTION:The Robotics Pathways and Careers Speaker Series (RPCSS) invites professionals working in robotics to come talk with current undergraduates about their career path\, how a background in robotics has impacted their professional growth\, and what they hope to see in students looking to enter the profession.\n\nThe 90-minute format of the event will consist of a 30-minute presentation from the invited speaker and up to 40 minutes of moderated Q&A and discussion. Students will be able to participate in person or remotely.\n\nAll undergrads are welcome! Please RSVP HERE -\n\nZoom Link: https://umich.zoom.us/j/92286702864\n\nDanny is a two-time graduate of the University of Michigan with his BSE and MSE in Aerospace Engineering. He cofounded SkySpecs in 2012 and has grown the team to over 250 people worldwide. Danny is a Techstars alum\, Forbes' \"30 under 30\" award winner\, Endeavor Entrepreneur\, Detroit Crain's \"40 under 40\" winner\, 2023 EY Entrepreneur of the Year Regional winner\, and 2023 Michigan Entrepreneur of the Year.
UID:130388-21865940@events.umich.edu
URL:https://events.umich.edu/event/130388
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Career,Michigan Robotics,Robotics,Undergraduate Students
LOCATION:Ford Robotics Building - 2300
CONTACT:
END:VEVENT
END:VCALENDAR