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DTSTART;TZID=America/New_York:20211207T100000
DTEND;TZID=America/New_York:20211207T113000
DTSTAMP:20260406T105654
CREATED:20211129T142523Z
LAST-MODIFIED:20211129T142523Z
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SUMMARY:MEAM Seminar: "Continuum Mechanics of Non-equilibrium Phenomena: A Journey Through Space and Time Scales"
DESCRIPTION:The fascinating diversity of material behavior at the macroscopic scale\, including plasticity\, phase transformations\, viscoelasticity or diffusion\, can only emerge from the underlying atomistic or particle behavior. Yet\, the direct connection between these two scales for non-equilibrium phenomena remains an extremely challenging quest from both a theoretical and computational perspective. This knowledge gap currently hinders predictive simulations and material discovery\, and leads to large economic losses and barriers to innovation in many industrial sectors. In this talk\, we will discuss several advances in the predictive modeling of non-equilibrium mechanics\, by combining tools from continuum mechanics\, statistical physics\, applied mathematics\, as well as machine learning and data-driven strategies.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-continuum-mechanics-of-non-equilibrium-phenomena-a-journey-through-space-and-time-scales/
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:20211207T110000
DTEND;TZID=America/New_York:20211207T120000
DTSTAMP:20260406T105654
CREATED:20211202T193540Z
LAST-MODIFIED:20211202T193540Z
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SUMMARY:ESE Fall Colloquium Seminar - "Alpha-loss: A Tunable Class of Loss Functions for Robust Learning"
DESCRIPTION:Machine learning has dramatically enhanced the role of automated decision making across a variety of domains. There are three ingredients that are at the heart of designing of sound ML algorithms: data\, learning architectures\, and loss functions. In this talk\, we focus on loss functions and the role of information theory in understanding the choice of loss functions in learning. We introduce alpha-loss as a parameterized class of loss functions that resulted from operationally motivating information-theoretic measures. Tuning the parameter alpha from 0 to infinity allows continuous interpolation between known and oft-used losses: log-loss (alpha=1)\, exponential loss (alpha=1/2)\, and 0-1 loss (alpha=infinity). \nBeginning with the classification properties of alpha-loss and its information-theoretic interpretations\, we will focus on a specific model\, namely the logistic model\, and quantify the optimization landscape of the average loss as viewed through the lens of Strict-Local-Quasi-Convexity. We discuss how different regimes of the parameter alpha enables the practitioner to tune the sensitivity of their algorithm towards two emerging challenges in learning: robustness and fairness. Finally\, we comment on ongoing and future work on different applications of alpha-loss including GANs and boosting
URL:https://seasevents.nmsdev7.com/event/ese-fall-colloquium-seminar-alpha-loss-a-tunable-class-of-loss-functions-for-robust-learning/
LOCATION:Zoom – Meeting ID 916 0331 6605
CATEGORIES:Seminar,Colloquium
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211207T120000
DTEND;TZID=America/New_York:20211207T130000
DTSTAMP:20260406T105654
CREATED:20211202T131922Z
LAST-MODIFIED:20211202T131922Z
UID:10006984-1638878400-1638882000@seasevents.nmsdev7.com
SUMMARY:CIS Seminar: “Synthetic Data: Anonymisation Groundhog Day”
DESCRIPTION:Synthetic data has been advertised as a silver-bullet solution to privacy-preserving data publishing that addresses the shortcomings of traditional anonymisation techniques. The promise is that synthetic data drawn from generative models preserves the statistical properties of the original dataset but\, at the same time\, provides perfect protection against privacy attacks. In this work\, we present the first quantitative evaluation of the privacy gain of synthetic data publishing and compare it to that of previous anonymisation techniques. \nOur evaluation of a wide range of state-of-the-art generative models demonstrates that synthetic data either does not prevent inference attacks or does not retain data utility. In other words\, we empirically show that synthetic data does not provide a better tradeoff between privacy and utility than traditional anonymisation techniques. \nFurthermore\, in contrast to traditional anonymisation\, the privacy-utility tradeoff of synthetic data publishing is hard to predict. Because it is impossible to predict what signals a synthetic dataset will preserve and what information will be lost\, synthetic data leads to a highly variable privacy gain and unpredictable utility loss. In summary\, we find that synthetic data is far from the holy grail of privacy-preserving data publishing.
URL:https://seasevents.nmsdev7.com/event/cis-seminar-synthetic-data-anonymisation-groundhog-day/
LOCATION:Zoom – Email CIS for link\, cherylh@cis.upenn.edu
ORGANIZER;CN="Computer and Information Science":MAILTO:cherylh@cis.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211209T110000
DTEND;TZID=America/New_York:20211209T120000
DTSTAMP:20260406T105654
CREATED:20211201T213025Z
LAST-MODIFIED:20211201T213025Z
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SUMMARY:ESE Fall Colloquium Seminar - "Data Compression: From Classical to Modern"
DESCRIPTION:Lossy data compression is a vital\, if hidden\, enabling technology. This virtual seminar would be impossible without data compression!  Existing compression standards for images and audio rely on a “classical” theory of compression that models sources as stationary Gaussian processes. This theory is quite mature\, and it provides remarkable insights into how to compress Gaussian sources in a variety of settings\, including those in which the data to be compressed is distributed among multiple users\, as occurs in federated learning. At the same time\, compressors based on artificial neural networks (ANNs) have recently begun to outperform those based on the classical theory for images and other practical sources. Given that the classical methods are provably near-optimal for Gaussian sources\, it is evident that ANNs are able to exploit non-Gaussianity in the data and that a new theory of data compression is required to explain their performance. I will present recent results on both fronts\, specifically the latest advances on distribution compression of Gaussian sources and some first steps toward the development of a “modern” theory. No background in data compression or information theory will be assumed. \nThe talk contains results obtained in collaboration with Omer Bilgen and Johannes Ballé.
URL:https://seasevents.nmsdev7.com/event/ese-fall-colloquium-seminar-data-compression-from-classical-to-modern/
LOCATION:Zoom – Meeting ID 968 2448 5695
CATEGORIES:Seminar,Colloquium
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211209T123000
DTEND;TZID=America/New_York:20211209T133000
DTSTAMP:20260406T105654
CREATED:20211118T210521Z
LAST-MODIFIED:20211118T210521Z
UID:10006973-1639053000-1639056600@seasevents.nmsdev7.com
SUMMARY:MSE Seminar: "Topological Quantum Phases\, Novel Superconductors\, and Ultra-Thin Films Beyond Graphene"
DESCRIPTION:Dr. Bansil will discuss some of his recent work aimed at understanding the electronic structure and spectroscopy of novel superconductors\, topological materials\, and atomically thin 2D films and battery materials. [1-5] Illustrative examples will include: (i) How by exploiting electronic structure techniques we have been able to successfully predict and understand the characteristics of many new classes of topologically interesting materials\, including magnetic topological materials; (ii) How atomically thin beyond graphene 2D and layered materials offer exciting new possibilities for manipulating electronic structures and provide novel platforms for fundamental science studies and applications; And\, (iii) with regard to the high-Tc’s\, he will discuss recent breakthroughs in modeling the insulating pristine compounds and their transition from the insulating to the metallic state with doping without invoking free parameters such as the Hubbard U parameter. A first-principles description of the competing stripe and magnetic phases in the cuprates also then becomes possible\, providing a new pathway for modeling correlated materials more generally. \n[1] A. Gao\, et al.\, Nature 595\, 521 (2021).\n[2] H. Hafiz\, et al.\, Nature 594\, 213 (2021).\n[3] Y. Zhang et al.\, Proceedings of the National Academy of Sciences 117\, 68 (2020).\n[4] D. J. Trainer et al.\, ACS Nano 14\, 2718 (2020).\n[5] A. Bansil\, H. Lin and T. Das\, Reviews of Modern Physics 88\, 021004 (2016).
URL:https://seasevents.nmsdev7.com/event/mse-seminar-topological-quantum-phases-novel-superconductors-and-ultra-thin-films-beyond-graphene/
LOCATION:Auditorium\, LRSM Building\, 3231 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="Materials Science and Engineering":MAILTO:johnruss@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211209T133000
DTEND;TZID=America/New_York:20211209T163000
DTSTAMP:20260406T105654
CREATED:20210707T142831Z
LAST-MODIFIED:20210707T142831Z
UID:10006822-1639056600-1639067400@seasevents.nmsdev7.com
SUMMARY:BE Seminar: "Tissue-Inspired Synthetic Biomaterials" (Shelly Peyton)
DESCRIPTION:This seminar will be held live and broadcast on zoom – check your email for the zoom link or contact ksas@seas.upenn.edu. \nImproved experimental model systems are critically needed to better understand cancer progression and bridge the gap between lab bench proof-of-concept studies\, validation in animal models\, and eventual clinical application. Many methods exist to create biomaterials\, including hydrogels\, which we use to study cells in contexts more akin to what they experience in the human body. Our lab has multiple approaches to create such biomaterials\, based on combinations of poly(ethylene glycol) (PEG) with peptides and zwitterions. In this presentation\, I will discuss our synthetic approaches to building life-like materials\, how we use these systems to grow cells and understand how a cell’s environment\, particularly the extracellular matrix regulates cancer cell growth\, dormancy\, and drug sensitivity.
URL:https://seasevents.nmsdev7.com/event/be-seminar-shelly-peyton/
LOCATION:Moore 216\, 200 S. 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Bioengineering":MAILTO:be@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211210T100000
DTEND;TZID=America/New_York:20211210T120000
DTSTAMP:20260406T105654
CREATED:20211129T135232Z
LAST-MODIFIED:20211129T135232Z
UID:10006980-1639130400-1639137600@seasevents.nmsdev7.com
SUMMARY:BE Doctoral Dissertation Defense: "Single-component optogenetic tools for cytoskeletal rearrangements" (Erin Berlew)
DESCRIPTION:The Department of Bioengineering at the University of Pennsylvania and Dr. Brian Chow are pleased to announce the Doctoral Dissertation Defense of Erin Berlew.\n\nTitle: “Single-component optogenetic tools for cytoskeletal rearrangements”\nDate: Friday\, December 10\, 2021\nTime: 10:00 AM\nLocation: Towne 337 and Zoom\n\n\nZoom information:\nERIN BERLEW is inviting you to a scheduled Zoom meeting. \nTopic: Erin Berlew’s Dissertation Defense\nTime: Dec 10\, 2021 10:00 AM Eastern Time (US and Canada) \nJoin Zoom Meeting\nhttps://upenn.zoom.us/j/91533277922 \nMeeting ID: 915 3327 7922\nOne tap mobile\n+13126266799\,\,91533277922# US (Chicago)\n+16465588656\,\,91533277922# US (New York) \nDial by your location\n+1 312 626 6799 US (Chicago)\n+1 646 558 8656 US (New York)\n+1 301 715 8592 US (Washington DC)\n+1 346 248 7799 US (Houston)\n+1 669 900 9128 US (San Jose)\n+1 253 215 8782 US (Tacoma)\nMeeting ID: 915 3327 7922\nFind your local number: https://upenn.zoom.us/u/acKm9XEi2V
URL:https://seasevents.nmsdev7.com/event/be-doctoral-dissertation-defense-single-component-optogenetic-tools-for-cytoskeletal-rearrangements-erin-berlew/
LOCATION:Room 337\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Doctoral,Graduate,Student,Dissertation or Thesis Defense
ORGANIZER;CN="Bioengineering":MAILTO:be@seas.upenn.edu
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