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DTSTART;TZID=America/New_York:20210823T120000
DTEND;TZID=America/New_York:20210823T123000
DTSTAMP:20260406T165059
CREATED:20210813T135129Z
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SUMMARY:PSOC Webinar: Mai Wang
DESCRIPTION:Zoom Meeting Waiting Room (email discher@seas for entry) \n\nhttps://upenn.zoom.us/j/95100409935?pwd=aGZjdkpYd3V4UU1qMkU5S1RiY3QvUT09\n\nMeeting ID: 951 0040 9935\nPasscode: 330227\n\nPSOC@Penn Summer Webinars 2021 \nContact manu@seas.upenn.edu with any questions
URL:https://seasevents.nmsdev7.com/event/psoc-webinar-mai-wang/
LOCATION:https://upenn.zoom.us/j/96715197752
CATEGORIES:Seminar,Doctoral,Graduate,Student
ORGANIZER;CN="PSOC":MAILTO:manu@seas.upenn.edu
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DTSTART;TZID=America/New_York:20210824T103000
DTEND;TZID=America/New_York:20210824T120000
DTSTAMP:20260406T165059
CREATED:20210817T161230Z
LAST-MODIFIED:20210817T161230Z
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SUMMARY:MEAM Seminar: "Deep Learning and Uncertainty Quantification: Methodologies and Applications"
DESCRIPTION:Uncertainty is ubiquitous in physical and engineering science because of the lack of knowledge\, inaccuracy in measurements\, objective stochastic nature. “You cannot be certain about uncertainty”. Uncertainty quantification plays an essential role in decision making\, solutions of partial differential equations\, optimal design\, etc. However\, quantifying the uncertainty is not trivial in computational science. The difficulty of uncertainty quantifications increases dramatically with for instance\, the dimensionality of the problems\, the type of noise in the data\, the complexity of black box functions where the data is obtained (large scale simulations\, expensive experiments)\, etc. Moreover\, with the knowledge of physics\, how to quantify uncertainty with only a few numbers of data should be considered properly. \nUncertainty quantification and its interactions with deep learning is a recently emerging interdisciplinary area that leverages the power of statistical methods\, machine learning models\, numerical methods and data-driven approach to provide reliable inference for quantities of interest in natural science and engineering problems. Given the fast growth in deep learning\, probabilistic methods and the large volume of data available across different research areas (computer visions\, natural language processing\, decision making)\, we aim in taking advantage of these recent advances to propose novel methodologies to solve problems where uncertainty quantification plays important roles. \nThis talk will highlight our recent process on the interaction between deep learning and uncertainty quantifications in the aspects of methodologies and applications. On the methodology side\, solutions regarding partial differential equations (forward problems) and system identifications (inverse problems) will be discussed with robust uncertainty estimations. Active learning and data acquisition will also be covered in the presence of uncertainty. Equipped with the methodologies we have developed\, we will briefly discuss some applications of such uncertainty quantification techniques in real-world problems such as cardiac flow activation mapping\, optimal design as well as analysis for high dimensional data.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-deep-learning-and-uncertainty-quantification-methodologies-and-applications/
LOCATION:Zoom – Email MEAM for Link\, peterlit@seas.upenn.edu
CATEGORIES:Seminar
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210824T150000
DTEND;TZID=America/New_York:20210824T170000
DTSTAMP:20260406T165059
CREATED:20210730T183016Z
LAST-MODIFIED:20210730T183016Z
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SUMMARY:BE Doctoral Dissertation Defense: "Engineering Novel High-Resolution Bioelectronic Interfaces from MXene Nanomaterials" (Brendan Murphy)
DESCRIPTION:The Department if Bioengineering at the University of Pennsylvania and Drs. Brian Litt and Flavia VItale are pleased to announce the Doctoral Dissertation Defense of Brendan Murphy.\n\nTitle: Engineering Novel High-Resolution Bioelectronic Interfaces from MXene Nanomaterials\nDate: August 24\, 2021\nTime: 3:00pm\n \nThe public is welcome to attend via Zoom:\nhttps://us06web.zoom.us/j/9948619859
URL:https://seasevents.nmsdev7.com/event/be-doctoral-dissertation-defense-engineering-novel-high-resolution-bioelectronic-interfaces-from-mxene-nanomaterials-brendan-murphy/
LOCATION:https://upenn.zoom.us/j/96715197752
CATEGORIES:Doctoral,Graduate,Dissertation or Thesis Defense,Staff
ORGANIZER;CN="Bioengineering":MAILTO:be@seas.upenn.edu
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210828T130000
DTEND;TZID=America/New_York:20210828T140000
DTSTAMP:20260406T165059
CREATED:20210824T191532Z
LAST-MODIFIED:20210824T191532Z
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SUMMARY:Launching into SEAS: Underrepresented @ Penn Engineering
DESCRIPTION:Launching into SEAS: Underrepresented @ Penn Engineering \nDate: Saturday\, August 28th \nTime: 1 p.m. – 2 p.m. \nLocation: Skirkanich Hall – Berger Auditorium \nAre you eager to get involved in the SEAS community? Interested in learning more about minority student organizations? Want to meet upperclassmen and fellow engineering students? \nJoin the Underrepresented Student Advisory Board in Engineering (USABE) and the Office of Diversity\, Equity\, and Inclusion (ODEI) in an NSO preceptorial panel featuring upperclassmen from identity organizations in SEAS. Participants will gain insightful advice on navigating their first semester in SEAS and learn about resources to support their academic\, professional\, and social development. Grab-and-go lunch will be provided at the end of the event! \nRegister for the event here. Please reach out to Shriya Karam and/or Fahmida Lubna for more information.
URL:https://seasevents.nmsdev7.com/event/launching-into-seas-underrepresented-penn-engineering/
LOCATION:PA
CATEGORIES:Diversity, Equity and Inclusion
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