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DTSTART;TZID=America/New_York:20210405T120000
DTEND;TZID=America/New_York:20210405T130000
DTSTAMP:20260407T021310
CREATED:20210120T165936Z
LAST-MODIFIED:20210120T165936Z
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SUMMARY:PSOC Webinar: "Cancer Cell Unjamming as Predictive Tumor Marker" (Josef A. Käs)
DESCRIPTION:Physical Sciences in Oncology Center PSOC@Penn \nSpring 2021 Webinar Series Mondays at 12:00 noon (EST) \nFor webinar links\, please contact manu@seas.upenn.edu.
URL:https://seasevents.nmsdev7.com/event/psoc-webinar-cancer-cell-unjamming-as-predictive-tumor-marker-josef-a-kas/
LOCATION:PA
CATEGORIES:Seminar
ORGANIZER;CN="PSOC":MAILTO:manu@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210406T103000
DTEND;TZID=America/New_York:20210406T120000
DTSTAMP:20260407T021310
CREATED:20210209T172446Z
LAST-MODIFIED:20210209T172446Z
UID:10006643-1617705000-1617710400@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Nonintrusive Reduced Order Models Using Physics Informed Neural Networks"
DESCRIPTION:The development of reduced order models for complex applications\, offering the promise for rapid and accurate evaluation of the output of complex models under parameterized variation\, remains a very active research area. Applications are found in problems which require many evaluations\, sampled over a potentially large parameter space\, such as in optimization\, control\, uncertainty quantification\, and in applications where a near real-time response is needed. \nHowever\, many challenges remain unresolved to secure the flexibility\, robustness\, and efficiency needed for general large-scale applications\, in particular for nonlinear and/or time-dependent problems.\nAfter giving a brief general introduction to projection based reduced order models\, we discuss the use of artificial feedforward neural networks to enable the development of fast and accurate nonintrusive models for complex problems. We demonstrate that this approach offers substantial flexibility and robustness for general nonlinear problems and enables the development of fast reduced order models for complex applications. \nIn the second part of the talk\, we discuss how to use residual based neural networks in which knowledge of the governing equations is built into the network and show that this has advantages both for training and for the overall accuracy of the model. \nTime permitting\, we finally discuss the use of reduced order models in the context of prediction\, i.e. to estimate solutions in regions of the parameter beyond that of the initial training. With an emphasis on the Mori-Zwansig formulation for time-dependent problems\, we discuss how to accurately account for the effect of the unresolved and truncated scales on the long term dynamics and show that accounting for these through a memory term significantly improves the predictive accuracy of the reduced order model. \nNB: This announcement has been updated with a new title and abstract.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-structure-preserving-reduced-order-models/
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:20210406T110000
DTEND;TZID=America/New_York:20210406T120000
DTSTAMP:20260407T021310
CREATED:20210308T181736Z
LAST-MODIFIED:20210308T181736Z
UID:10006687-1617706800-1617710400@seasevents.nmsdev7.com
SUMMARY:ESE Seminar: "Harnessing Light-Matter Interaction for Photonic Quantum Technologies"
DESCRIPTION:Photonic quantum technologies have a unique potential for applications such as large-scale quantum networks and quantum-enhanced sensing. Furthermore\, photons provide new paradigms for quantum simulations and a testbed for benchmarking the advantage of quantum simulators over the classical ones. These applications demand novel resources such as efficient single-photon sources\, large clusters of entangled photons\, and nonlinear optical gates. A 1D-atom\, a quantum emitter coupled to a single optical mode with high efficiency\, can deliver these functionalities. \nIn this talk\, I will introduce two realizations of an artificial 1D-atom based on a single quantum dot coupled to either a photonic-crystal waveguide or a tunable Fabry-Perot microcavity. To start\, I will present a single-photon source with record efficiency\, speed\, and coherence. I will then go on to discuss the transmission properties of the 1D-atom and show that we can achieve optical nonlinearities at the single-photon level. I will also show that the interplay between the local electric field and the quantum dot can lead to interesting phenomena such as directional emission and nonreciprocal transport for photons. \nAt the end of my talk\, I will present an overview of my feature research direction and my vision for quantum computing and quantum networking using photons.
URL:https://seasevents.nmsdev7.com/event/ese-seminar-harnessing-light-matter-interaction-for-photonic-quantum-technologies/
LOCATION:Zoom – Email ESE for Link jbatter@seas.upenn.edu
CATEGORIES:Seminar,Faculty,Colloquium,Student
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210406T150000
DTEND;TZID=America/New_York:20210406T160000
DTSTAMP:20260407T021310
CREATED:20210309T183147Z
LAST-MODIFIED:20210309T183147Z
UID:10006696-1617721200-1617724800@seasevents.nmsdev7.com
SUMMARY:CIS Seminar: "Bridging Learning and Decision Making"
DESCRIPTION:Machine learning is becoming widely used in decision making\, in domains ranging from personalized medicine and mobile health to online education and recommendation systems. While (supervised) machine learning traditionally excels at prediction problems\, decision making requires answering questions that are counterfactual in nature\, and ignoring this mismatch leads to unreliable decisions. As a consequence\, our understanding of the algorithmic foundations for data-driven decision making is limited\, and efficient algorithms are typically developed on an ad hoc basis. Can we bridge this gap and make decision making as easy as machine learning? \nFocusing on the contextual bandit\, a core problem in data-driven decision making\, we bridge the gap by providing the first optimal and efficient reduction to supervised machine learning. The algorithm allows users to seamlessly apply off-the-shelf supervised learning models and methods to make decisions on the fly\, and has been implemented in widely-used\, industry-standard tools for decision making. \nOur results advance a broader program to develop a universal algorithm design paradigm for data-driven decision making. I will close the talk by discussing challenges and opportunities in building such a framework\, including efforts to extend our developments to difficult reinforcement learning problems in large state spaces.
URL:https://seasevents.nmsdev7.com/event/cis-seminar-bridging-learning-and-decision-making/
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:20210407T150000
DTEND;TZID=America/New_York:20210407T160000
DTSTAMP:20260407T021310
CREATED:20210122T023817Z
LAST-MODIFIED:20210122T023817Z
UID:10006611-1617807600-1617811200@seasevents.nmsdev7.com
SUMMARY:CBE Seminar: "Engineering Microsystems and Computational Pipelines to Understand the Brain"
DESCRIPTION:Abstract \nMy lab is interested in engineering micro systems and computational tools to address questions in systems neuroscience\, developmental biology\, and cell biology that are difficult to answer with conventional techniques. We are particularly interested in the questions of how the brain is assembled during development (and changes during aging) and information processed by brain circuits. We work with a powerful genetic system – the free-living soil nematode C. elegans. In this talk\, I will introduce two sets of powerful mathematical and physics-based tools accessible by engineers to accelerate the biological understanding. I will talk about two recent developments in discrete microfluidic systems exploiting multiphase and dynamical behavior of the fluids and microswimmers (i.e. C. elegans). By designing the microfluidic system cleverly using appropriately chosen dimensionless numbers\, we can have exquisite control of the samples and experimental conditions. I will also talk about a powerful graph-theory-based framework to build probabilistic models of brain atlases. This machine-learning approach greatly reduces bias\, enables automated and robust cell identification\, and will enable a variety of applications including gene-expression analysis\, whole-brain imaging\, and connectomics.
URL:https://seasevents.nmsdev7.com/event/cbe-seminar-engineering-microsystems-and-computational-pipelines-to-understand-the-brain/
LOCATION:Zoom – Email CBE for link
CATEGORIES:Seminar
ORGANIZER;CN="Chemical and Biomolecular Engineering":MAILTO:cbemail@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210407T150000
DTEND;TZID=America/New_York:20210407T160000
DTSTAMP:20260407T021310
CREATED:20210309T190044Z
LAST-MODIFIED:20210309T190044Z
UID:10006698-1617807600-1617811200@seasevents.nmsdev7.com
SUMMARY:CIS Seminar: "Human-Centered Interactive Systems for Configuring\, Extending\, and Developing AI Applications"
DESCRIPTION:Artificial intelligence (AI) technologies are emerging and affecting our lives in many aspects. However\, the majority of individuals are merely users of AI with little capability to adapt AI to their own long-tail tasks\, preferences\, and interests that are not covered by the existing AI solutions. Democratizing AI to empower the individuals to create\, configure\, and extend AI-powered systems requires a new human-centered approach to guide the users to provide more effective inputs to the AI systems\, and to enable the systems to better understand and generalize from inputs in the user’s natural style. In this talk\, I will present my research on designing\, developing\, and studying (1) a new smartphone interactive task learning agent that empowers end-users to teach new tasks\, concepts\, and automation rules using the combination of natural language instructions and demonstrations on existing third-party app GUIs; and (2) a new developer tool that allows developers without natural language processing expertise to bootstrap a task-oriented conversational bot from existing mobile apps using the task model extracted from the app interaction traces. These projects examplify my approach of combining human-centered methods with AI techniques to create new novel interfaces and system capabilities that facilitate effective human-AI collaboration\, lowering the barriers to customizing\, extending\, and developing AI applications for end-users and non-expert developers.
URL:https://seasevents.nmsdev7.com/event/cis-seminar-3/
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:20210407T150000
DTEND;TZID=America/New_York:20210407T160000
DTSTAMP:20260407T021310
CREATED:20210403T155651Z
LAST-MODIFIED:20210403T155651Z
UID:10006742-1617807600-1617811200@seasevents.nmsdev7.com
SUMMARY:Spring 2021 GRASP SFI: “Robotic Caregivers—Sensing\, Simulation\, and Physical Human-Robot Interaction”
DESCRIPTION:Abstract: Autonomous robots have the potential to serve as versatile caregivers that improve quality of life for millions of people with disabilities worldwide. Yet\, physical robotic assistance presents several challenges\, including risks associated with physical human-robot interaction\, difficulty sensing the human body\, and a lack of tools for benchmarking and training physically assistive robots. In this talk\, I will present techniques towards addressing each of these core challenges in robotic caregiving. First\, I will introduce a method inspired by human perspective-taking which allow assistive robots to predict how their future actions will apply forces to a person’s body. I will then describe capacitive servoing\, a new sensing technique for robots to sense the human body and track trajectories along the body. Finally\, I will show how we can develop intelligent robotic caregivers via simulation and virtual reality\, and I will introduce Assistive Gym\, the first physics simulation framework for benchmarking and training physically assistive robots. \nClick here to join the Zoom meeting
URL:https://seasevents.nmsdev7.com/event/spring-2021-grasp-sfi-robotic-caregivers-sensing-simulation-and-physical-human-robot-interaction/
LOCATION:Zoom
CATEGORIES:Seminar
ORGANIZER;CN="General Robotics%2C Automation%2C Sensing and Perception (GRASP) Lab":MAILTO:grasplab@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210408T104500
DTEND;TZID=America/New_York:20210408T114500
DTSTAMP:20260407T021310
CREATED:20210325T195944Z
LAST-MODIFIED:20210325T195944Z
UID:10006735-1617878700-1617882300@seasevents.nmsdev7.com
SUMMARY:MSE Seminar: "Bio-like Structural Hydrogels with Life-like Intelligence"
DESCRIPTION:From the cellular level up to the body system level\, living organisms present elegant designs and strategies to realize the desirable structures\, properties and functions. For example\, tendons and muscles are tough but soft\, owing to highly complex hierarchical structures rarely found in synthetic materials. Plants can automatically track the sun and our body can self-regulate motions adaptively to environment\, presenting superior intelligence also lacking in manmade systems. Hydrogels\, as a class of crosslinked polymers\, not only have tissue-like water-rich porous networks and can also change their volume and physical properties in response to environmental cues including temperature\, light\, and specific molecules. At UCLA He lab\, we exploit fundamental material processing-structure-property-function studies of hydrogels and their derivatives\, to create (i) ‘bio-like’ structures and properties and (ii) ‘life-like’ intelligence in functional soft materials for applications in robotics\, biomedicine\, energy and environment. In this talk\, I will start with showcasing how the stimuli-responsiveness of hydrogels can unify sensing-diagnosis-actuation process to create ‘synthetic intelligence’ with built-in feedback loop\, such as sunflower-like light tracking for solar harvesting (Nat. Nanotech. 2019) and self-sensing actuators for autonomous soft robotics (Sci. Robotics 2019\, 2021; Matter 2021). Driven by the remaining challenges revealed in these function developments\, I will then present our structural material approaches to breaking the fundamental limits in mechanical\, diffusion and electrical properties. I will discuss the mechanics and general principles to design extreme properties\, including simultaneously high toughness and stretchability (Nature 2021; Adv. Mater. 2021; Sci. Adv. 2020)\, tunable porosity and diffusivity (Adv. Mater. 2021; EcoMat 2021) and stretchable conductive soft materials for flexible (bio)electronics (Matter\, 2020; Adv. Funct. Mater. 2020; Adv. Mater. 2019). I will conclude my talk with a perspective on future human-machine convergence enabled by soft materials.
URL:https://seasevents.nmsdev7.com/event/mse-seminar-bio-like-structural-hydrogels-with-life-like-intelligence/
LOCATION:PA
ORGANIZER;CN="Materials Science and Engineering":MAILTO:johnruss@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210408T150000
DTEND;TZID=America/New_York:20210408T160000
DTSTAMP:20260407T021310
CREATED:20201207T171307Z
LAST-MODIFIED:20201207T171307Z
UID:10006562-1617894000-1617897600@seasevents.nmsdev7.com
SUMMARY:BE Seminar: "Reaction-Coupled Solid-State Nanopore Digital Counting: Towards Sensitive\, Selective and Fast Nucleic Acid Testing" (Weihua Guan)
DESCRIPTION:This seminar will be held virtually on Zoom (details coming soon). \nDue to their conceptual simplicity\, the nanopore sensors have attracted intense research interest in electronic single molecule detection. While considerable success has been achieved\, the solid-state nanopores still face three significant challenges\, including repeatable nanopore size control\, introduction sensing specificity\, and prolonged sensor response time at low concentrations. In this talk\, I will discuss a calibration-free solid-state nanopore counting method and two representative applications in nucleic acid testing. One is an isothermal amplification-coupled nanopore counting for malaria analysis. The other is the CRISPR-cas12a-coupled nanopore counting for HIV analysis.  Finally\, I will also discuss how we can develop a fully integrated ‘sample-to-result’ nucleic acid testing device using the solid-state counting strategy. I believe the reaction-coupled solid-state nanopore digital counting could open a new avenue towards compact\, robust\, low-cost electronic nucleic acid testing at the point of care.
URL:https://seasevents.nmsdev7.com/event/be-seminar-weihua-guan/
LOCATION:PA
CATEGORIES:Seminar
ORGANIZER;CN="Bioengineering":MAILTO:be@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210408T150000
DTEND;TZID=America/New_York:20210408T160000
DTSTAMP:20260407T021310
CREATED:20210309T194731Z
LAST-MODIFIED:20210309T194731Z
UID:10006700-1617894000-1617897600@seasevents.nmsdev7.com
SUMMARY:CIS Seminar: "AI for Population Health: Melding Data and Algorithms on Networks"
DESCRIPTION:As exemplified by the COVID-19 pandemic\, our health and wellbeing depend on a difficult-to-measure web of societal factors and individual behaviors. My research aims to build AI which can impact such social challenges\, advancing health and equity on a population level. This effort requires new algorithmic and data-driven paradigms which span the full process of gathering costly data\, developing machine learning models to understand and predict interactions\, and optimizing the use of limited resources in interventions. In response to these needs\, I will present methodological developments at the intersection of machine learning\, optimization\, and social networks which are motivated by on-the-ground collaborations on HIV prevention\, tuberculosis treatment\, and the COVID-19 response. These projects have produced deployed applications and policy impact. For example\, I will present the development of an AI-augmented intervention for HIV prevention among homeless youth. This system was evaluated in a field test enrolling over 700 youth and found to significantly reduce key risk behaviors for HIV.
URL:https://seasevents.nmsdev7.com/event/cis-seminar-ai-for-population-health-melding-data-and-algorithms-on-networks/
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:20210409T110000
DTEND;TZID=America/New_York:20210409T123000
DTSTAMP:20260407T021310
CREATED:20210403T155854Z
LAST-MODIFIED:20210403T155854Z
UID:10006743-1617966000-1617971400@seasevents.nmsdev7.com
SUMMARY:GRASP On Robotics: “Perspectives on Machine Learning for Adaptive Robotic Systems”
DESCRIPTION:Abstract: Recent advances in machine learning are leading to new tools for designing intelligent robots: functions relied on to govern a robot’s behavior can be learned from a robot’s interaction with its environment rather than hand-designed by an engineer. Many machine learning methods assume little prior knowledge and are extremely flexible\, they can model almost anything! But\, this flexibility comes at a cost. The same algorithms are often notoriously data hungry and computationally expensive\, two problems that can be debilitating for robotics. In this talk I’ll discuss how machine learning can be combined with prior knowledge and structure to build effective solutions to robotics problems. I’ll introduce an online learning perspective on robot adaptation that unifies well-known algorithms and suggests new approaches. I’ll discuss how structure and simulation can augment learning and how imperfect models\, simple policies\, and hierarchical abstractions can help to build adaptive\, resilient systems. I will also show how we have applied some of these ideas to several robotics tasks that require impressive sensing\, speed\, and agility to complete. \nClick here to join the Zoom Webinar
URL:https://seasevents.nmsdev7.com/event/grasp-on-robotics-perspectives-on-machine-learning-for-adaptive-robotic-systems/
LOCATION:https://upenn.zoom.us/j/96715197752
CATEGORIES:Seminar
ORGANIZER;CN="General Robotics%2C Automation%2C Sensing and Perception (GRASP) Lab":MAILTO:grasplab@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210409T120000
DTEND;TZID=America/New_York:20210409T130000
DTSTAMP:20260407T021310
CREATED:20210406T133212Z
LAST-MODIFIED:20210406T133212Z
UID:10006749-1617969600-1617973200@seasevents.nmsdev7.com
SUMMARY:ODEI Spotlight: Career Tools Webinar Series: Navigating the Job Search & Workplace Authentically
DESCRIPTION:Friday\, April 9th at 12pm \nRegister HERE \nOne’s identity is multi-faceted and complex\, shaped by different affinities and experiences that impact the way we see (and are seen) and operate in the world. However\, research shows that having to hide parts of your identity at work can be exhausting and mentally draining. \nThis panel will feature Penn alumni discussing strategies for both finding inclusive employers that welcome and value your identities and ways to effectively navigate the work environment as your authentic self in terms of cultural and socioeconomic backgrounds\, sexual orientation\, and lived experiences. Individuals who supervise others will also find the panel useful for tips on creating the most inclusive workplace culture possible.\nModerator: Malik Muhammad\, Associate Director\, University of Pennsylvania’s (Penn) LGBT Center. \nPanelists: \n\nMisha Chakrabarti is a speaker\, podcaster\, comedian\, and coach.\nKaren Lithgow innovates and transforms brands and businesses\nVictor Anthony Scotti\, Jr. is a DEI practitioner and Black student advocate.\n\n  \nIn partnership with Career Services and Alumni Relations
URL:https://seasevents.nmsdev7.com/event/odei-spotlight-career-tools-webinar-series-navigating-the-job-search-workplace-authentically/
LOCATION:PA
CATEGORIES:Diversity, Equity and Inclusion
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210409T140000
DTEND;TZID=America/New_York:20210409T150000
DTSTAMP:20260407T021310
CREATED:20210403T160627Z
LAST-MODIFIED:20210403T160627Z
UID:10006744-1617976800-1617980400@seasevents.nmsdev7.com
SUMMARY:Spring 2021 GRASP Seminar: “Propelling Robot Manipulation of Unknown Objects using Learned Object Centric Models”
DESCRIPTION:Abstract: There is a growing interest in using data-driven methods to scale up manipulation capabilities of robots for handling a large variety of objects. Many of these methods are oblivious to the notion of objects and they learn monolithic policies from the whole scene in image space. As a result\, they don’t generalize well to different scenes\, viewpoints\, and lighting changes. In addition\, these models cannot be combined with other components and constraints without re-training. In this talk\, I will present our approach for learning object centric models trained on 3D depth data. I will show how these approaches are combined with each other to accomplish tasks on unseen objects and environments. In particular\, I will cover our works on grasping and segmenting unknown objects\, obstacle avoidance\, and task planning for unknown object rearrangement task. \nClick here to join the Zoom meeting
URL:https://seasevents.nmsdev7.com/event/spring-2021-grasp-seminar-propelling-robot-manipulation-of-unknown-objects-using-learned-object-centric-models/
LOCATION:Zoom
CATEGORIES:Seminar
ORGANIZER;CN="General Robotics%2C Automation%2C Sensing and Perception (GRASP) Lab":MAILTO:grasplab@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210409T140000
DTEND;TZID=America/New_York:20210409T160000
DTSTAMP:20260407T021310
CREATED:20210405T130937Z
LAST-MODIFIED:20210405T130937Z
UID:10006746-1617976800-1617984000@seasevents.nmsdev7.com
SUMMARY:PICS: " Houdini Presentation"
DESCRIPTION:Cinematic scientific visualization makes three dimensional scientific phenomena approachable for mass audiences by using the artistic language of film including elements like camera choreography\, lighting design\, comprehensive scenic environments\, and more. Cinematic scientific visualizations are an engaging way for domain experts to communicate niche information with the public\, to refute widely held misconceptions\, and to inspire the scientists of the future. Science films that feature these visualizations are screened at science centers to millions of viewers over the span of 10+ years and bridge different languages and cultures. They are shared widely on social media\, featured regularly in television programs\, and contribute to the success of public lectures. \nIf you are a domain expert looking to share your data more widely\, or a visualization designer who has focused on more analytical tools\, what better way is there to get started with a Hollywood style than by using Hollywood tools? This workshop will introduce participants to Houdini\, a visual effects software package that can generate cinematic-quality data visualizations with ease and efficiency. It is used and appreciated by most major animation and visual effects film studios for its procedural architecture\, its modular design\, and out-of-the-box rendering algorithms\, all important features for ease-of-use in the field of data visualization. Houdini is a general-purpose image-making software that differs from most traditional scientific visualization tools in that it is optimized for look development and design functionality. \nThis session will consist of a presentation about using scientific data in Houdini\, and a demonstration breakdown of a working Houdini scene file.\n\nNo registration is necessary.
URL:https://seasevents.nmsdev7.com/event/pics-houdini-presentation/
LOCATION:Zoom – email kathom@seas.upenn.edu
CATEGORIES:Seminar
ORGANIZER;CN="Penn Institute for Computational Science (PICS)":MAILTO:dkparks@seas.upenn.edu
END:VEVENT
END:VCALENDAR