BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Penn Engineering Events - ECPv6.15.18//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://seasevents.nmsdev7.com
X-WR-CALDESC:Events for Penn Engineering Events
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20210314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20211107T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20220313T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20221106T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20230312T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20231105T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220627T100000
DTEND;TZID=America/New_York:20220627T120000
DTSTAMP:20260405T195003
CREATED:20220518T184309Z
LAST-MODIFIED:20220518T184309Z
UID:10007182-1656324000-1656331200@seasevents.nmsdev7.com
SUMMARY:ESE Ph.D. Thesis Defense: "Machine Learning on Large-Scale Graphs"
DESCRIPTION:Graph neural networks (GNNs) are successful at learning representations from most types of network data but suffer from limitations in large graphs\, which do not have the Euclidean structure that time and image signals have in the limit. Yet\, large graphs can often be identified as being similar to each other in the sense that they share structural properties. Indeed\, graphs can be grouped in families converging to a common graph limit — the graphon. A graphon is a bounded symmetric kernel which can be interpreted as both a random graph model and a limit object of a convergent sequence of graphs. Graphs sampled from a graphon almost surely share structural properties in the limit\, which implies that graphons describe families of similar graphs. We can thus expect that processing data supported on graphs associated with the same graphon should yield similar results. In my research\, I formalize this intuition by showing that the error made when transferring a GNN across two graphs in a graphon family is small when the graphs are sufficiently large. This enables large-scale graph machine learning by transference: training GNNs on moderate-scale graphs and executing them on large-scale graphs.
URL:https://seasevents.nmsdev7.com/event/ese-ph-d-thesis-defense-machine-learning-on-large-scale-graphs/
LOCATION:Room 452 C\, 3401 Walnut\, 3401 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar,Dissertation or Thesis Defense
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220627T120000
DTEND;TZID=America/New_York:20220627T130000
DTSTAMP:20260405T195003
CREATED:20220601T145938Z
LAST-MODIFIED:20220601T145938Z
UID:10007186-1656331200-1656334800@seasevents.nmsdev7.com
SUMMARY:PSOC@Penn Talk: "Microtechnology-based Single Cell and EV Profiling” (Jina Ko)
DESCRIPTION:Contact manu@seas.upenn.edu for the Zoom link.
URL:https://seasevents.nmsdev7.com/event/psocpenn-talk-microtechnology-based-single-cell-and-ev-profiling-jina-ko/
LOCATION:Room 337\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar,Faculty
ORGANIZER;CN="PSOC":MAILTO:manu@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220628T100000
DTEND;TZID=America/New_York:20220628T110000
DTSTAMP:20260405T195003
CREATED:20220614T175156Z
LAST-MODIFIED:20220614T175156Z
UID:10007195-1656410400-1656414000@seasevents.nmsdev7.com
SUMMARY:MEAM Ph.D. Thesis Defense: "Enhancing Strength and Toughness Via Reinforcement with Nanocellulose Fibers"
DESCRIPTION:Cellulose nanofibrils (CNFs) are a nanomaterial obtained from plant sources and have excellent mechanical properties\, high aspect ratios\, and are biodegradable. As a reinforcing phase\, CNFs have the potential to improve the mechanical properties of polymer materials. The overarching objective of this thesis is to investigate the use of CNFs to enhance the strength and toughness of polymers and traditional papers. \nIn the first part of this thesis\, poly methyl methacrylate (PMMA) fibers are reinforced using CNFs to increase the strength and toughness. Fourier transform infrared (FTIR) spectroscopy is used to measure PMMA molecular orientation in the composite fibers. Tensile tests and fiber-based fracture tests using edge-cracked fibers are used to quantify the enhancement of modulus\, strength\, and fracture toughness through the addition of CNFs to PMMA. Specifically\, a 2 improvement in fracture toughness is observed at 1% wt. CNF content. \nIn the second part of the thesis\, filter paper\, which is a network of microscale cellulose fibers is infiltrated with CNFs to create all-cellulose sheets with heterogenous mechanical properties. This is realized by printing and subsequent drying of an aqueous CNF solution and patterning of the infiltrated regions is used to engineer the strength and toughness of the materials. Single edge notch tension (SENT) tests are performed on the specimens to evaluate their fracture behavior. It is shown that geometric and elastic heterogeneity can be utilized to tune the toughness over a large range while maintaining or improving the strength. \nFinally\, to overcome limitations of the SENT\, a new experimental fracture specimen\, denoted the hinged rigid beam (HRB)\, was developed. The HRB eliminates the compressive stresses developed in conventional beam-bending fracture tests like the double cantilever beam method\, thus making it suitable for testing thin materials such as paper. A mechanics model of the HRB was developed to allow critical strain energy release rate to be calculated from the measured force-displacement response and the specimen was validated via finite element analysis and experiments on thin PMMA sheets. This technique was used to characterize the toughness of several materials\, including filter paper\, copy paper and 2D lattice materials. Finally\, the HRB was used to characterize and further understand the fracture behavior of patterned nanocellulose infiltrated sheets.
URL:https://seasevents.nmsdev7.com/event/meam-ph-d-thesis-defense-enhancing-strength-and-toughness-via-reinforcement-with-nanocellulose-fibers/
LOCATION:Room 337\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Doctoral,Dissertation or Thesis Defense
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
END:VCALENDAR