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DTSTART;TZID=America/New_York:20240919T103000
DTEND;TZID=America/New_York:20240919T120000
DTSTAMP:20260403T135248
CREATED:20230908T153334Z
LAST-MODIFIED:20230908T153334Z
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SUMMARY:MSE Seminar: "Materials\, Mechanics\, and Performance of Flexible Polymeric Actuators in Robotics" (Sameh Tawfick - University of Illinois\, Urbana-Champaign)
DESCRIPTION:My group is developing a roadmap for soft actuating materials to replace bulky electric motors in miniature robots requiring large mechanical work output. \nFirst\, I will describe the materials microstructure and mechanics of polymeric coiled muscles made by twisting nylon fishing lines\, and how these actuators use internal strain energy to achieve a “record breaking” performance. Then I will describe intriguing hierarchical super-\, and hyper-coiled artificial muscles which\, inspired by natural muscles\, exploit the interplay between nonlinear mechanics and material microstructure. \nNext\, I will describe their use to actuate the dynamic snapping of insect-scale jumping robots. The combination of strong but slow muscles with a fast-snapping beam gives rise to dynamic buckling cascade phenomena leading to effective robotic jumping mechanisms. These examples shed light on the future of robotics propelled by new bioinspired materials\, nonlinear mechanics\, and unusual manufacturing processes.
URL:https://seasevents.nmsdev7.com/event/mse-seminar-materials-mechanics-and-performance-of-flexible-polymeric-actuators-in-robotics-sameh-tawfick-university-of-illinois-urbana-champaign/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Materials Science and Engineering":MAILTO:johnruss@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240919T153000
DTEND;TZID=America/New_York:20240919T163000
DTSTAMP:20260403T135248
CREATED:20240905T123157Z
LAST-MODIFIED:20240905T123157Z
UID:10008088-1726759800-1726763400@seasevents.nmsdev7.com
SUMMARY:CIS Seminar: "Learning Controllers for multi-robot Teams"
DESCRIPTION:We have recently demonstrated the possibility of learning controllers that are zero-shot transferable to groups of real quadrotors via large-scale\, multi-agent\, end-to-end reinforcement learning. We train policies parameterized by neural networks that can control individual drones in a group in a fully decentralized manner. Our policies\, trained in simulated environments with realistic quadrotor physics\, demonstrate advanced flocking behaviors\, perform aggressive maneuvers in tight formations while avoiding collisions with each other\, break and re-establish formations to avoid collisions with moving obstacles\, and efficiently coordinate in pursuit-evasion tasks. The model learned in simulation transfers to highly resource-constrained physical quadrotors performing station-keeping and goal-swapping behaviors. Motivated by these results and the observation that neural control of memory-constrained\, agile robots requires small yet highly performant models\, the talk will conclude with some thoughts on coaxing learned models onto devices with modest computational capabilities.
URL:https://seasevents.nmsdev7.com/event/cis-seminar-learning-controllers-for-multi-robot-teams/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="Computer and Information Science":MAILTO:cherylh@cis.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240920T090000
DTEND;TZID=America/New_York:20240920T110000
DTSTAMP:20260403T135248
CREATED:20240904T192801Z
LAST-MODIFIED:20240904T192801Z
UID:10008085-1726822800-1726830000@seasevents.nmsdev7.com
SUMMARY:BE Doctoral Dissertation Defense: "Extracting Insights From Electronic Health Records Using Optimized Large Language Models" (Kevin Xie)
DESCRIPTION:The Department of Bioengineering at the University of Pennsylvania and Dr. Brian Litt are pleased to announce the Doctoral Dissertation Defense of Kevin Xie.\n\nTitle: Extracting Insights From Electronic Health Records Using Optimized Large Language Models\nDate: September 20th\, 2024\nTime: 9:00 AM\nLocation: Glandt Forum\, Singh Center for Nanotechnology\nAdvisor: Dr. Brian Litt\nZoom link: https://upenn.zoom.us/j/91430247073?pwd=nsDh7ebUjVZNHbdVldc9OfxmavzGri.1\n\nThe public is welcome to attend.
URL:https://seasevents.nmsdev7.com/event/be-doctoral-dissertation-defense-extracting-insights-from-electronic-health-records-using-optimized-large-language-models-kevin-xie/
LOCATION:Glandt Forum\, Singh Center for Nanotechnology\, 3205 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Doctoral,Graduate,Student,Dissertation or Thesis Defense
ORGANIZER;CN="Bioengineering":MAILTO:be@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240920T103000
DTEND;TZID=America/New_York:20240920T114500
DTSTAMP:20260403T135248
CREATED:20240913T181149Z
LAST-MODIFIED:20240913T181149Z
UID:10008096-1726828200-1726832700@seasevents.nmsdev7.com
SUMMARY:GRASP Industry Talk: Amazon Robotics - Scanless Technologies\, “Amazon Robotics Tech Talk”
DESCRIPTION:This will be an in-person event only with in-person attendance in Wu and Chen. This seminar will not be recorded or available on Zoom. \nAmazon Robotics Tech Talk & Networking Event \n  \nAmazon Robotics’ Scanless Technologies Team is excited to invite you to join us for a Tech Talk presentation followed by a Networking Event on Friday\, 9/20 starting at 10:30am in Wu & Chen Auditorium\, 101 Levine Hall. \nJoin us for the Tech Talk from 10:30-11:45am to get the unique opportunity to learn about how our applied science research work in computer vision addresses real-world challenges within Amazon’s fulfillment centers in the areas of action detection/recognition and item identification. \nConnect in person and engage in meaningful discussions with Amazon Robotics applied scientists\, software managers\, and campus recruiters at our Networking Event in Levine 512 immediately following the Tech Talk. The Networking Event will run from 12:00pm – 1:00pm. \nMeet our Amazon Robotics Scanless Technologies speakers for this event: \n\nNick Hu\, Applied Scientist II (GRASP alum)\nMansi Agarwal\, Applied Scientist\nFrank Preiswerk\, Principal Applied Scientist\nCara Garber\, Software Development Manager\n\nWe look forward to meeting you all!
URL:https://seasevents.nmsdev7.com/event/grasp-industry-talk-amazon-robotics-scanless-technologies-amazon-robotics-tech-talk/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
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:20240920T130000
DTEND;TZID=America/New_York:20240920T180000
DTSTAMP:20260403T135248
CREATED:20240821T150341Z
LAST-MODIFIED:20240821T150341Z
UID:10008070-1726837200-1726855200@seasevents.nmsdev7.com
SUMMARY:ASSET & Warren Center Research Mixer
DESCRIPTION:The ASSET and Warren Center will be hosting a research mixer to welcome new PhD students and expose new and current students to the wide range of research in Penn Engineering in AI/ML. The program will consist of (short) faculty talks\, poster presentations by students/postdocs\, and a reception to end the night!
URL:https://seasevents.nmsdev7.com/event/warren-asset-center-research-mixer/
LOCATION:Glandt Forum\, Singh Center for Nanotechnology\, 3205 Walnut Street\, Philadelphia\, PA\, 19104\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240920T140000
DTEND;TZID=America/New_York:20240920T150000
DTSTAMP:20260403T135248
CREATED:20240828T184618Z
LAST-MODIFIED:20240828T184618Z
UID:10008076-1726840800-1726844400@seasevents.nmsdev7.com
SUMMARY:PICS Colloquium: Motion-based rules and solitary waves: ameloblasts and birds
DESCRIPTION:Information is usually expected to propagate across a population of active agents such as living cells or birds in a diffusive manner. Yet waviness in the microstructure of dental enamel implies coherent wave motion among the generative ameloblast cells over exceptionally long periods\, from a week in the mouse to months in the human; and trajectory data for starling flocks show solitary wave disturbances that propagate in much shorter times but with constant speed and amplitude across the entire flock. Necessary conditions for coherent wave behavior include persistent network invariance\, i.e.\, neighbor sets not changing too quickly\, and some form of non-mass inertia. \nIn a top-down strategy\, a wave equation is conjectured for 2D sheets of ameloblasts with a single unknown parameter supplying rate inertia. Optimizing only \, the wave equation can predict the observed cross-sectional shape of the enamel body in the mouse incisor. Nonlinearity in the wave equation gives rise to solitary waves\, which also predict the time of a key transformation in ameloblast behavior. Most interestingly for modeling biological development\, solitary waves coupled explicitly to shape or motility transformations in cells have characteristics determined internally and independent of remote conditions. Complex pattern formation\, e.g.\, segmentation\, can be derived from coupled systems of transformations\, using rules based only on displacements and their derivatives. \nGoverning equations for solitary waves can be constructed for ameloblasts and birds that deal with different variables but are isomorphic and account successfully for observations in both populations\, an instance of substrate independence in rules for emergent behavior.
URL:https://seasevents.nmsdev7.com/event/pics-colloquium-motion-based-rules-and-solitary-waves-ameloblasts-and-birds/
LOCATION:PICS Conference Room 534 – A Wing \, 5th Floor\, 3401 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Colloquium
ORGANIZER;CN="Penn Institute for Computational Science (PICS)":MAILTO:dkparks@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240923T110000
DTEND;TZID=America/New_York:20240923T120000
DTSTAMP:20260403T135248
CREATED:20240821T135130Z
LAST-MODIFIED:20240821T135130Z
UID:10008061-1727089200-1727092800@seasevents.nmsdev7.com
SUMMARY:ESE Fall Seminar - "Beyond the Exit of the Device Miniaturization Tunnel"
DESCRIPTION:For the past fifty years\, researchers of semiconductor technology have felt like walking inside a tunnel. There was a single path forward – two-dimensional down-scaling of device sizes\, also referred to as 2D miniaturization. With device features approaching atomic scale\, semiconductor technology has reached the exit of this tunnel. The future is bright at the exit of the tunnel as there are many possible paths that create new opportunities for architectures that are extremely difficult (or even impossible) to implement using existing technology approaches that rely mainly on 2D miniaturization. It is no longer possible to draw a clear boundary between design and fabrication. Many innovations across the entire system stack – from architectures to circuits\, devices\, fabrication processes\, and materials – will provide large multiplicative benefits at the system level. And there will be a change in the supplier-integrator and fabless-foundry ecosystems that have been in place for over 30 years. \nSpecifically\, AI computation benefits tremendously simply by having more on-chip memory capacity. To derive the highest energy efficiency\, not only the chip architecture needs to be application domain specific\, but the memory technologies must also be application domain-specific to capture the highest benefits while incurring the lowest impact on latency\, bandwidth and cost. As an example\, I will describe the use of ultra-low leakage transistors (< 10-18 A/µm) with large band gap oxide semiconductor materials to build gain cell memory. Gain cell memory has only two transistors and can be built in a 3D stack (thus small cell size and high density) that can complement SRAM to provide larger on-chip memory capacity [1]. More importantly\, these memory technologies can be integrated on Si logic chips to arrive at a computing system that integrates multiple heterogeneous device technologies in a 3D monolithic\, stacked\, assembled integrated circuit: 3D MOSAIC [2]. These Differentiated Access Memory (DAM) systems will require us to revisit and re-examine computing at all levels\, from the design of new memory technologies to high-level algorithms. \n[1] S. Liu et al.\, “Gain Cell Memory on Logic Platform – Device Guidelines for Oxide Semiconductor Transistor Materials Development\,” 2023 International Electron Devices Meeting (IEDM)\, San Francisco\, CA\, USA\, 2023\, pp. 1-4\, doi: 10.1109/IEDM45741.2023.10413726. \n[2] R. M. Radway et al.\, “The Future of Hardware Technologies for Computing: N3XT 3D MOSAIC\, Illusion Scaleup\, Co-Design\,” 2021 IEEE International Electron Devices Meeting (IEDM)\, San Francisco\, CA\, USA\, 2021\, pp. 25.4.1-25.4.4\, doi: 10.1109/IEDM19574.2021.9720647. \nAcknowledgements: This presentation benefits from a long-time collaboration with Prof. Subhasish Mitra (Stanford). This work is supported in part by Department of Defense Microelectronics Commons California-Pacific-Northwest AI Hardware Hub\, Eccalon/DoD\, National Science Foundation (award number 2235329)\, SRC JUMP 2.0 CHIMES Center and PRISM Center\, and member companies of the Stanford SystemX Alliance\, Stanford Non-Volatile Memory Technology Research Initiative (NMTRI)\, and the Stanford Differentiated Access Memory (DAM) industry affiliate programs.
URL:https://seasevents.nmsdev7.com/event/ese-fall-seminar-title-tbd-10/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Colloquium
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240924T101500
DTEND;TZID=America/New_York:20240924T111500
DTSTAMP:20260403T135248
CREATED:20240821T173619Z
LAST-MODIFIED:20240821T173619Z
UID:10008071-1727172900-1727176500@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Controlling Adhesion and Friction of Soft Interfaces by Meso-Scale Structures"
DESCRIPTION:Discoveries of unique adhesive and frictional properties in biological attachment systems have\, over the past two decades\, demonstrated how near-surface architecture at lengths between the molecular (a few nm) and continuum (mm) scales can be used to achieve interesting and unique surface mechanical properties. This has spurred considerable research activity in design of meso-scale\, near-surface architecture (typically at micron to mm length scales). This talk will present some of our group’s contributions to this field\, including design and understanding of film-terminated fibrillar structures for adhesion and friction enhancement\, shape-complementary interfaces for controlled adhesion and friction\, enhancement of elasto-hydrodynamic friction by surfaces with periodic modulation of properties\, and understanding of transitions in lubricated friction of soft solids.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-controlling-adhesion-and-friction-of-soft-interfaces-by-meso-scale-structures/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240924T140000
DTEND;TZID=America/New_York:20240924T140000
DTSTAMP:20260403T135248
CREATED:20240923T203037Z
LAST-MODIFIED:20240923T203037Z
UID:10008105-1727186400-1727186400@seasevents.nmsdev7.com
SUMMARY:ESE Ph.D. Thesis Defense: "Design and control of dynamical systems: a classical to quantum perspective"
DESCRIPTION:The design and control of dynamical systems have long been core objectives of engineering. In this thesis\, we tackle the complexities of design and control across paradigms ranging from Boolean models of genetic networks\, to thermally driven stochastic systems\, to quantum-mechanical systems. These disparate domains share common challenges\, including the large dimensionality of the design space and the computational intractability of objective functions. For classical systems\, we draw inspiration from optimization heuristics and genetic programming\, leveraging the inherent symmetries within these problems. This approach led to the discovery of a novel symmetry in biological systems\, which we term dynamical mirror symmetry\, and the subsequent design of artificial mechanical structures that emulate the behavior of biological prions. Quantum systems introduce an additional layer of complexity: the exponential growth in the dimensionality of the Hilbert space\, which makes classical simulations impractical. As a test platform\, we develop control sequences tailored for nitrogen vacancy centers to achieve precise control. Our approach begins with the use of standardized quantum control sequences\, demonstrating their capability to infer the parameters of a quantum Hamiltonian. We then develop a more general method inspired by diagrammatic path integrals\, which enables full differentiability and supports perturbative expansions for optimization and control.
URL:https://seasevents.nmsdev7.com/event/ese-ph-d-thesis-defense-design-and-control-of-dynamical-systems-a-classical-to-quantum-perspective/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Dissertation or Thesis Defense
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240924T153000
DTEND;TZID=America/New_York:20240924T163000
DTSTAMP:20260403T135248
CREATED:20240912T151852Z
LAST-MODIFIED:20240912T151852Z
UID:10008094-1727191800-1727195400@seasevents.nmsdev7.com
SUMMARY:CIS Seminar: "Machine learning for discovery: deciphering RNA splicing logic"
DESCRIPTION:Recent advances in machine learning such as deep learning have led to powerful tools for modeling complex data with high predictive accuracy. However\, the resulting models are typically black box\, limiting their usefulness in scientific discovery. I will describe an “interpretable-by-design” machine learning model capturing a fundamental cellular process known as RNA splicing. Our model provides a systematic understanding of RNA splicing logic\, recapitulating and extending existing domain knowledge. \nThe talk will not assume any prior knowledge\, and should be accessible to a broad audience.
URL:https://seasevents.nmsdev7.com/event/cis-seminar-machine-learning-for-discovery-deciphering-rna-splicing-logic/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="Computer and Information Science":MAILTO:cherylh@cis.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240925T110000
DTEND;TZID=America/New_York:20240925T120000
DTSTAMP:20260403T135248
CREATED:20240821T135416Z
LAST-MODIFIED:20240821T135416Z
UID:10008062-1727262000-1727265600@seasevents.nmsdev7.com
SUMMARY:ESE Fall Seminar - "Power to the People (and to the Datacenters)! Achieving the dream of a clean and reliable electricity supply"
DESCRIPTION:Most of us think of electricity as a clean and reliable source of energy\, which flows out of the plug whenever we need it. The reality is far more complex. The reliability of our electricity supply is increasingly threatened by extreme weather\, causing more frequent power outages. The cleanliness of our electricity supply varies greatly between different locations and hours of the day\, depending on the availability of solar and wind power. \nOur research combines insights from power engineering with tools from linear programming\, stochastic optimization and risk analysis to develop methods that address pressing questions related to our electricity supply. In this talk\, I will present results from two main topics. First\, I will discuss how consumers can reduce their carbon footprint by using electricity where and when low carbon power is available. We will focus on hyperscale data centers as an example\, though the insights apply to anyone consuming electricity (including you and me!). Second\, as an example of extreme weather impacts\, I will discuss the intersection between power grids and wildfires. Specifically\, we will look at how utilities can optimize their operations to minimize both the risk of wildfire ignitions and the extent of power outages.
URL:https://seasevents.nmsdev7.com/event/ese-fall-seminar-title-tbd-12/
LOCATION:Towne 327
CATEGORIES:Colloquium
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240925T120000
DTEND;TZID=America/New_York:20240925T131500
DTSTAMP:20260403T135248
CREATED:20240709T173551Z
LAST-MODIFIED:20240709T173551Z
UID:10008009-1727265600-1727270100@seasevents.nmsdev7.com
SUMMARY:ASSET Seminar: "Robustness in the Era of LLMs: Jailbreaking Attacks and Defenses"
DESCRIPTION:Abstract: \nDespite efforts to align large language models (LLMs) with human intentions\, popular LLMs such as chatGPT\, Llama\, Claude\, and Gemini are susceptible to jailbreaking attacks\, wherein an adversary fools a targeted LLM into generating objectionable content. For this reason\, interest has grown in improving the robustness of LLMs against such attacks. In this talk\, we review the current state of the jailbreaking literature\, including new questions about robust generalization\, discussions of new black-box attacks on LLMs\, defenses against jailbreaking attacks\, and a new leaderboard to evaluate the robust generalization of production LLMs. \nZoom Link (if unable to attend in-person): https://upenn.zoom.us/j/93335180566
URL:https://seasevents.nmsdev7.com/event/asset-seminar-hamed-hassani-university-of-pennsylvania/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240925T150000
DTEND;TZID=America/New_York:20240925T160000
DTSTAMP:20260403T135248
CREATED:20240916T140916Z
LAST-MODIFIED:20240916T140916Z
UID:10008098-1727276400-1727280000@seasevents.nmsdev7.com
SUMMARY:Fall 2024 GRASP SFI: Baxi Chong\, Georgia Institute of Technology\, “Mechanical intelligence in locomotion: from information theory to mesoscale robots”
DESCRIPTION:This will be a hybrid event with in-person attendance in Levine 307 and virtual attendance on Zoom. \nABSTRACT\nLocomotion in complex environments (e.g.\, rubble\, leaf litter\, granular media) is essential to mobile engineered systems such as robots. Effective locomotion requires complex control strategies to interact with terrain heterogeneity. Computational intelligence (CI)\, which typically includes rapid terrain sensing and active feedback controls\, is a widely recognized component in locomotion strategy. Alternatively\, mechanical intelligence (MI) – passive response to environmental perturbation governed by physics laws or mechanical constraints – is an important yet less studied component. In this talk\, I will discuss “why” and “how” MI can contribute to effective locomotion using the examples of multi-legged robots (redundantly segmented bodies with simple legs). For the “why\,” I will quantify a specific MI that emerges from leg redundancy. By modeling locomotion as a stochastic process (analogous to signal transmission over noisy channels)\, I will show that MI\, without any CI\, is sufficient to generate reliable and effective locomotion. To explore the “how\,” I will take a quantitative analogy to signal transmission algorithms (e.g.\, error correcting/detecting codes) and propose a co-design coding scheme for multi-legged locomotion. Specifically\, my talk will cover that (i) additional legs\, with higher control dimensions\, can enable a broader spectrum of capabilities\, including load carrying/pulling\, sidewinding\, rolling\, and obstacle-climbing; (ii) the inclusion of CI (feedback controls) can enhance multi-legged locomotion speed while preserving the feature of robustness; and (iii) CI might reduce the number of redundant legs required to navigate a particular terrain. Finally\, I will discuss the coordination and competition between MI and CI in a broader framework termed Embedded Intelligence (EI) and illustrate the applications of MI-dominated systems in fields like search-and-rescue\, agriculture\, and the development of soft\, micro\, and modular robots.
URL:https://seasevents.nmsdev7.com/event/fall-2024-grasp-sfi-baxi-chong/
LOCATION:Levine 307\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
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:20240925T153000
DTEND;TZID=America/New_York:20240925T163000
DTSTAMP:20260403T135248
CREATED:20240816T202025Z
LAST-MODIFIED:20240816T202025Z
UID:10008050-1727278200-1727281800@seasevents.nmsdev7.com
SUMMARY:BRITTON CHANCE DISTINGUISHED LECTURE (CBE): "Next-generation Cancer Therapeutics Guided by Structural\, Mechanistic\, and Biophysical Properties" (Jennifer Cochran\, Stanford University)
DESCRIPTION:Bio & Abstract: \nJennifer Cochran is the Macovski Professor of Bioengineering and Senior Associate Vice Provost for Research at Stanford University. Prof. Cochran was recruited in 2005 as one of the founding faculty members in Stanford’s Bioengineering department and served as its Chair from 2017-2022. She is also a member of the chemical engineering\, immunology\, biophysics\, and cancer biology graduate groups. Her research and translational interests focus on protein-based drug discovery for applications in oncology\, immunology\, and regenerative medicine. Prof. Cochran’s work also encompasses designer protein inputs and biochemical circuits for engineered cell therapies\, new tools for high throughput protein engineering and analysis\, and molecular engineering for climate and sustainability solutions. Leveraging her entrepreneurial experience\, she enjoys mentoring others on technology transfer and life science company formation and is the faculty Director of Protein Therapeutics at Stanford’s Innovative Medicine Accelerator. Dr. Cochran’s seminar will discuss protein engineering approaches for targeted cancer treatment\, guided by macromolecular analysis and biophysical design criteria. Examples include structural and mechanistic elucidation of protein-based immunotherapeutics\, engineered ligand and receptor inhibitors\, and tumor-targeted immunostimulants and chemotherapeutics. The Britton Chance lecture has special meaning as she considers Penn part of her educational roots\, having spent time in the Stellar Chance Laboratory building as a postdoctoral fellow.
URL:https://seasevents.nmsdev7.com/event/britton-chance-distinguished-lecture-cbe-next-generation-cancer-therapeutics-guided-by-structural-mechanistic-and-biophysical-properties-jennifer-cochran-stanford-university/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Chemical and Biomolecular Engineering":MAILTO:cbemail@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240926T103000
DTEND;TZID=America/New_York:20240926T120000
DTSTAMP:20260403T135248
CREATED:20240902T210919Z
LAST-MODIFIED:20240902T210919Z
UID:10008081-1727346600-1727352000@seasevents.nmsdev7.com
SUMMARY:MSE Seminar: "Opportunities in Whispering-Gallery Microresonators: Fundamentals and Applications"
DESCRIPTION:Light-matter interactions form the fundamental basis for numerous phenomena and processes in optical devices. This talk will cover ultra-high-quality (Q) whispering-gallery-mode (WGM) optical microresonators which have an unprecedented capability to trap light in a highly confined volume smaller than a strand of human hair. Light-matter interactions are significantly enhanced in high-quality WGM resonators\, creating the potential for a wealth of new scientific discoveries and technological breakthroughs. High-Q microresonators and microlasers provide excellent platforms for both fundamental science and engineering applications; the choices of materials for the photonic resonators enable various opportunities for different applications. This talk will start with the introduction of diverse sensing mechanisms and strategies developed around high-Q microresonators. It will include discussions on ultra-sensitive self-referencing detection and sizing of nanoparticles\, including single virions. Various strategies\, such as mechanical solitons through optomechanical effects in a microtoroid resonator\, a barcode technology based on collective behaviors of multiple resonances\, and AI-enhanced target classification\, will be introduced for sensing applications with resonators. Furthermore\, our recent exploration of fundamental physics\, such as non-Hermitian physics in high-Q WGM resonators\, have unraveled innovative strategies to achieve a new generation of optical systems enabling unconventional control of light flow. Examples including nonreciprocity in a parity-time (PT)-symmetry resonator system\, loss engineering in a lasing system\, directional lasing emission at an exceptional point (EP)\, and EP-enhanced sensing will be presented. To conclude\, the applications of resonators in photonic integrated circuits (PIC) will be discussed. Our research discoveries just represent a glimpse of the potential of photonic resonators; there are still many exciting opportunities by leveraging the enhanced light-matter interactions through resonant effects in the future.
URL:https://seasevents.nmsdev7.com/event/mse-seminar-opportunities-in-whispering-gallery-microresonators-fundamentals-and-applications/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Materials Science and Engineering":MAILTO:johnruss@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240927T140000
DTEND;TZID=America/New_York:20240927T150000
DTSTAMP:20260403T135248
CREATED:20240828T185523Z
LAST-MODIFIED:20240828T185523Z
UID:10008077-1727445600-1727449200@seasevents.nmsdev7.com
SUMMARY:PICS Colloquium: Combining High-Throughput Workflows\, Quantum Chemistry\, and AI for the Discovery of Tunable Materials with Unprecedented Properties
DESCRIPTION:The solutions to many of society’s most pressing problems rely on the discovery of materials with unprecedented physical and chemical properties that are tailored to an application of interest. Typically\, it is not a matter of incremental improvements over existing technologies; rather\, there is often an urgent need to identify new kinds of materials altogether. In this talk\, I will discuss how quantum chemistry\, high-throughput computing\, and machine learning can help guide the discovery of novel\, energy-relevant materials. I will highlight several representative success stories for this approach as well as potential shortcomings\, using the areas of chemical separations and catalysis as demonstrative topics. I will also briefly highlight the open-source software and community science efforts I have contributed to that enables us to leverage the full capabilities of modern high-performance computing resources for materials discovery problems.
URL:https://seasevents.nmsdev7.com/event/andrew-rosen-combining-high-throughput-workflows-quantum-chemistry-and-ai-for-the-discovery-of-tunable-materials-with-unprecedented-properties/
LOCATION:PICS Conference Room 534 – A Wing \, 5th Floor\, 3401 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Colloquium
ORGANIZER;CN="Penn Institute for Computational Science (PICS)":MAILTO:dkparks@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240930T100000
DTEND;TZID=America/New_York:20240930T120000
DTSTAMP:20260403T135248
CREATED:20240926T021104Z
LAST-MODIFIED:20240926T021104Z
UID:10008108-1727690400-1727697600@seasevents.nmsdev7.com
SUMMARY:MSE Thesis Defense: “Imaginary-Index-Driven Programmable Integrated Photonics for Optical Computing and Networking"
DESCRIPTION:Photonics serves as the backbone of modern information infrastructure\, transmitting and processing data at unparalleled speeds with minimal energy consumption by harnessing the inherent parallelism\, high-frequency operation\, and expansive bandwidths. In the past decade\, the surging advancements of artificial intelligence has revolutionized the traditional definition of computing algorithms. By bridging the gap between optical hardware and software-defined functionality\, programmable integrated photonics\, where on-chip photonic circuits are dynamically reconfigured by tunable optical components including modulators\, amplifiers\, and switches\, opens new avenues for optical routing\, computing and networking. However\, the existing programmable integrated photonic platforms employ discrete\, single-function devices\, leading to exponential architectural complexity and hindering full programmability. Additionally\, fabrication imperfections may compromise performance\, impeding the advancement of large-scale photonic processors designed for data-intensive applications. In contrast to the state of the art\, we explores programmable integrated photonic platforms driven by the imaginary part of the permittivity in semiconductor-based optical gain materials. First\, a topological photonic system is presented\, demonstrating robust and reconfigurable light steering immune to fabrication defects\, driven by non-Hermitian physics. By interacting with the pseudospin degree of freedom\, defined by the circulating direction in photonic cavities\, a non-blocking scheme is realized. Next a novel lithography-free paradigm for integrated photonic computing is proposed and demonstrated in an unpatterned device fully driven by the imaginary index. This new platform enables field-programmability and dynamic robustness\, culminating in a high-fidelity photonic matrix processor capable of real-time error correction and in-situ photonic network training for practical tasks. Furthermore\, the capabilities of photonic field-programmability can be pushed into the nonlinear realm by the spatial control of carrier excitations and their dynamics within the active semiconductor\, achieving programmable photonic nonlinear functions. Leveraging the architecture of photonic nonlinear computing through polynomial building blocks\, training of integrated photonic polynomial networks is demonstrated. This new type of neural networks serves as a pioneering example in the exploration of photonic paradigms tailored for computing and networking with light.
URL:https://seasevents.nmsdev7.com/event/mse-thesis-defense-imaginary-index-driven-programmable-integrated-photonics-for-optical-computing-and-networking/
LOCATION:Zoom
CATEGORIES:Dissertation or Thesis Defense
ORGANIZER;CN="Materials Science and Engineering":MAILTO:johnruss@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241001T101500
DTEND;TZID=America/New_York:20241001T111500
DTSTAMP:20260403T135248
CREATED:20240913T133412Z
LAST-MODIFIED:20240913T133412Z
UID:10008095-1727777700-1727781300@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Powering the Future Through Hydrogen Hubs and International Partnerships for Materials and Engineering System Solutions"
DESCRIPTION:The U.S. Department of Energy’s Energy Earthshots Initiative aims to accelerate breakthroughs of more abundant\, affordable and reliable clean energy solutions\, to tackle the toughest remaining barriers to addressing the climate crisis and achieving net-zero carbon emissions by 2050. Specifically\, the Hydrogen Energy Shot seeks to reduce the cost of clean hydrogen by 80% in one decade. The regional clean hydrogen Hub initiative aims to catalyze the deployment of hydrogen into the economy through a network of producers\, end-users\, and required infrastructure. \nIn this presentation\, after the introduction of the hydrogen hub initiative\, the focus will be on the science and engineering of hydrogen/materials interactions. Development and validation of a lifetime prediction methodology for failure of materials used for hydrogen containment components requires thorough understanding of the deformation and fracture mechanisms at the atom- and micro-scale along with a mechanics approach to link these mechanisms with the macroscopically observed failure at the macroscale. We will try to establish this link between micro-scale and macro-scale through experiment\, modeling\, and simulation for a number of materials systems and failure modes. \nRecent experimental studies of the microstructure beneath fracture surfaces of ferritic steel\, lath martensitic steel\, stainless steel\, and nickel specimens fractured in hydrogen suggest that the dislocation structure and hydrogen transported by mobile dislocations play important roles in the evolution of the fracture process/event. After reviewing this plasticity-mediated hydrogen-induced failure\, we present a number of models and simulations that can be used for the design against hydrogen-induced failure: i) for the case of low alloy martensitic steels results demonstrate that hydrogen induced failures are complex phenomena that can be explained by a combination of hydrogen-enhanced plasticity and decohesion and require factors such as stress\, strain\, and hydrogen concentration to all act in concert to bring about failure; ii) for ferritic systems subjected to cyclic loading\, we present an approach to mitigate the hydrogen effect through a few molecules of oxygen per million molecules of hydrogen in order to markedly increase the magnitude of the stress intensity factor range at which hydrogen-accelerated fatigue commences; iii) at high temperatures and hydrogen pressures\, internal hydrogen can accelerate creep deformation in steel and react with carbides to form internal methane gas with an associated loss in strength due to decarburization that can lead to fracture\, a phenomenon known as high temperature hydrogen attack. Based on the underlying deformation and fracture mechanisms\, we propose a new tool to ascertain fitness-for-service of components in service and the results are discussed in relation to the empirical Nelson curves that are used in industrial practice.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-powering-the-future-through-hydrogen-hubs-and-international-partnerships-for-materials-and-engineering-system-solutions/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241002T120000
DTEND;TZID=America/New_York:20241002T131500
DTSTAMP:20260403T135248
CREATED:20240709T173718Z
LAST-MODIFIED:20240709T173718Z
UID:10008010-1727870400-1727874900@seasevents.nmsdev7.com
SUMMARY:ASSET Seminar: "Towards Pluralistic Alignment: Foundations for Learning from Diverse Human Preferences"
DESCRIPTION:Abstract: \nLarge pre-trained models trained on internet-scale data are often not ready for safe deployment out-of-the-box. They are heavily fine-tuned and aligned using large quantities of human preference data\, usually elicited using pairwise comparisons. While aligning an AI/ML model to human preferences or values\, it is important to ask whose preference and values we are aligning it to? The current approaches of alignment are severely limited due to their inherent uniformity assumption. While there is rich literature on learning preferences from human judgements using comparison queries\, the models often focus on learning average preference over the population due to the limitations on the amount of data available per individual or on learning an individual’s preference using a lot of queries.  Furthermore\, the knowledge of the metric\, i.e.\, the way humans judge similarity and dissimilarity\, is assumed to be known which does not hold in practice. We aim to overcome these limitations by building mathematical foundations for learning from diverse human preferences. \nIn this talk\, I will present\, PAL\, a personalize-able reward modelling framework for pluralistic alignment\, which captures diversity in preferences while also capturing commonalities that can be learned by pooling together data from individuals. I will also discuss some recent theoretical results on per user sample complexity for generalization and fundamental limitations when there are limited pairwise comparisons. \nBased on work with Daiwei Chen\, Yi Chen\, Aniket Rege\, Zhi Wang\, Geelon So\, Greg Canal\, Blake Mason\, Gokcan Tatli\, and Rob Nowak. References: \n\nPAL: Pluralistic Alignment Framework for learning from heterogeneous preferences  (preprint\, 2024)\nOne-for-all: Simultaneous metric and preference learning (appeared in Neurips 2022)\nMetric learning via limited pairwise comparisons (appeared in UAI 2024)\, and\nLearning Populations of Preferences via pairwise comparisons (appeared in AISTATS 2024).\n\nZoom Link (if unable to attend in-person): https://upenn.zoom.us/j/95536358996
URL:https://seasevents.nmsdev7.com/event/asset-seminar-ramya-korlakai-vinayak-university-of-wisconsin-madison/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241002T150000
DTEND;TZID=America/New_York:20241002T160000
DTSTAMP:20260403T135248
CREATED:20240919T172921Z
LAST-MODIFIED:20240919T172921Z
UID:10008101-1727881200-1727884800@seasevents.nmsdev7.com
SUMMARY:Fall 2024 GRASP SFI: Tony Samaritano\, Verge Aero\, “Building the Drone Show Industry”
DESCRIPTION:This will be a hybrid event with in-person attendance in Levine 307 and virtual attendance on Zoom. \nABSTRACT\nIn this talk\, I will discuss how we engineered the foundation of the drone show industry\, creating a new form of entertainment that rivals traditional fireworks. Our journey began at Pennovation\, where we designed and tested our initial multi-drone system\, which helped us navigate the challenges of the COVID-19 pandemic. \nI will delve into the development of a scalable\, centralized control architecture for robotic agents\, enabling the safe operation of more than ten thousand drones globally. The transition from prototyping to scalable manufacturing required the tight integration of hardware and software\, including the implementation of an extensive IEEE 802.15.4 network stack on an RTOS\, and advanced flight planning software. \nSafety was paramount; sensor redundancy and reliable radio communications were crucial to creating a secure drone show system. I will also explore how balancing engineering and business imperatives—through first-principles thinking and a customer-centric approach—enabled us to navigate trade-offs among cost\, speed\, and performance effectively. \nAttendees will gain practical insights into simplifying complex systems\, making strategic trade-offs\, and employing rapid iteration to transform technical innovation into a viable market product. This talk offers valuable perspectives for engineers interested in scalable robotic systems and academics exploring the intersection of technology and entrepreneurship\, demonstrating how aligning engineering excellence with entrepreneurial vision makes success achievable.
URL:https://seasevents.nmsdev7.com/event/fall-2024-grasp-sfi-tony-samaritano/
LOCATION:Levine 307\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
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:20241002T153000
DTEND;TZID=America/New_York:20241002T163000
DTSTAMP:20260403T135248
CREATED:20240816T202240Z
LAST-MODIFIED:20240816T202240Z
UID:10008051-1727883000-1727886600@seasevents.nmsdev7.com
SUMMARY:CBE Seminar: "Filling the Gaps of Machine Learning Workflows in the Molecular Sciences" (Yamil Colón\, University of Notre Dame)
DESCRIPTION:Abstract: \nOne of the main challenges in the molecular sciences is identifying promising candidates for a target application given the virtually limitless number of possibilities. Computer simulations and machine learning (ML) are integral tools to survey the vast molecular landscape and the thermodynamic conditions to evaluate their performance. Broadly\, a typical pipeline for molecular characterization\, design\, and discovery with ML involves data curation and generation\, featurization and/or descriptor development\, and a model or algorithm for the final prediction. However\, these tools are currently severely limited due to the lack of available data\, because of the time and resource expense to generate it. In this seminar\, I will discuss our research efforts focused on metal-organic frameworks (MOFs)\, which are nanoporous\, crystalline materials composed of inorganic and organic components. I will begin by showcasing our use of large-scale\, high-throughput computational screening techniques to generate gas adsorption data. Following this\, I will illustrate how transfer learning and active learning can significantly reduce the data requirements of ML models\, uncover insights into the material space\, and enhance model capabilities when combined with thermodynamic information. Finally\, I will present our recent work on utilizing sigma profiles—descriptions of molecular electrostatic surface charges—as chemical descriptors to efficiently navigate and optimize molecular landscapes.
URL:https://seasevents.nmsdev7.com/event/cbe-seminar-filling-the-gaps-of-machine-learning-workflows-in-the-molecular-sciences-yamil-colon-university-of-notre-dame/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Chemical and Biomolecular Engineering":MAILTO:cbemail@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241004T080000
DTEND;TZID=America/New_York:20241004T200000
DTSTAMP:20260403T135248
CREATED:20240926T071243Z
LAST-MODIFIED:20240926T071243Z
UID:10008109-1728028800-1728072000@seasevents.nmsdev7.com
SUMMARY:PRECISE Industry Day 2024 (an AI Conference)
DESCRIPTION:Theme: “Building an AI Ecosystem for a Brand New Generation of Computing”\n\n\n\n\nSection I: \n\n\nLeadership Lightning Insights Series\n\n\n\n\nSection II:\n\n\nKeynote Address\n\n\n\n\nSection III:\n\n\nExecutive Panel Discussions
URL:https://seasevents.nmsdev7.com/event/precise-industry-day-2024-an-ai-conference/
LOCATION:Wu & Chen Auditorium
ORGANIZER;CN="PRECISE":MAILTO:wng@cis.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241004T110000
DTEND;TZID=America/New_York:20241004T123000
DTSTAMP:20260403T135248
CREATED:20240920T201734Z
LAST-MODIFIED:20240920T201734Z
UID:10008102-1728039600-1728045000@seasevents.nmsdev7.com
SUMMARY:CBE Doctoral Dissertation Defense: "Quantitative Analysis on Enhancer-Mediated Dynamic Gene Regulations in Drosophila Embryos" (Hao Dennis Deng)
DESCRIPTION:Abstract:\nProper organism development relies on precise gene regulation\, where genes are expressed at specific times\, locations\, and quantities. Non-coding DNA sequences called enhancers play a crucial role in this process by interacting with their target promoters to initiate transcription and regulate gene expression. While the mechanisms of enhancer-mediated gene control have been extensively studied\, the dynamics of the interactions between enhancers and promoters\, especially within complex genomic environments\, remain unclear. My research employed quantitative live cell imaging in early Drosophila embryos to explore the dynamics of enhancer-promoter interactions and their impact on gene expression\, revealing several key findings. First\, we observed that strong enhancer-promoter interactions induce interallelic competition such that each homozygous allele produces fewer mRNAs than the single hemizygous allele. Through systematic gene construct modulation\, we found evidence suggesting that the transcription machinery binding to both enhancer and promoter regions\, such as RNA Pol II and pre-initiation complex molecules\, act as limiting factors that cause reduced transcription. Second\, we investigated spatiotemporal thresholds of enhancer-promoter interactions required for transcription initiation and found that enhancers need to be within 500 nm of their target promoters for more than four minutes. These spatiotemporal requirements highlight the importance of sustained proximity between enhancers and target promoters in gene activation\, providing insights into the mechanisms of precise gene control. Both studies support the hypothesis that transcription hubs form at regulatory elements\, facilitating transcription through a local concentration of transcription machinery. These findings significantly advance our understanding of the dynamics of enhancer-promoter interplay and gene regulation at the single-cell level. The knowledge can be a foundation for precise gene control in various biological contexts\, from developmental processes to potential therapeutic applications. \nZoom Link: https://upenn.zoom.us/j/94986872387?\nMeeting ID: 949 8687 2387 | Passcode: 041979
URL:https://seasevents.nmsdev7.com/event/cbe-doctoral-dissertation-defense-quantitative-analysis-on-enhancer-mediated-dynamic-gene-regulations-in-drosophila-embryos-hao-dennis-deng/
LOCATION:Room 313\, Singh Center for Nanotechnology\, 3205 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Doctoral,Student,Dissertation or Thesis Defense
ORGANIZER;CN="Chemical and Biomolecular Engineering":MAILTO:cbemail@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241008T101500
DTEND;TZID=America/New_York:20241008T111500
DTSTAMP:20260403T135248
CREATED:20240816T191634Z
LAST-MODIFIED:20240816T191634Z
UID:10008047-1728382500-1728386100@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Systemic Disadvantages for LGBTQ Professionals in STEM"
DESCRIPTION:Researchers have documented race and gender bias in STEM for decades\, but there has been little parallel examination of LGBTQ status as an axis of inequality. How do LGBTQ-identifying STEM professionals fare in STEM? Drawing on data from her NSF-funded STEM Inclusion Study\, which included surveys of over 25\,000 STEM workers\, Dr. Cech will discuss her research on LGBTQ inequality among STEM professionals. Her research is the first to document persistent and systemic disadvantages for LGBTQ STEM professionals\, compared to their non-LGBTQ peers. She documents these disadvantages along numerous dimensions\, including day-to-day workplace experiences\, career limitations\, professional devaluation\, marginalization and harassment\, turnover intentions\, and health and wellness issues. Her presentation will situate these findings within the broader context of intersectional inequality in STEM. This research reveals LGBTQ status as a clear axis of inequality in STEM and underscores the need for organizational and cultural shifts to address these patterns. \nThis seminar\, held in recognition of LGBT History Month and National Coming out Day\, is co-sponsored with SEAS’ Office of Diversity\, Equity and Inclusion\, the Penn LGBT Center\, Penn Out in STEM (oSTEM)\, and the Eidos LGBTQ+ Health Initiative.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-systemic-disadvantages-for-lgbtq-professionals-in-stem/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241008T110000
DTEND;TZID=America/New_York:20241008T120000
DTSTAMP:20260403T135248
CREATED:20240821T140330Z
LAST-MODIFIED:20240821T140330Z
UID:10008063-1728385200-1728388800@seasevents.nmsdev7.com
SUMMARY:ESE Fall Seminar - "Reflections on learning about learning: A case study on where ideas come from in (In-)Secure Processor Design"
DESCRIPTION:When it comes to security\, hardware is the new software. Starting some years ago\, this shift was made plain when a litany of attacks\, such as “Spectre/Meltdown” and “Rowhammer”\, shattered our confidence in processors as a root of trust. Making matters worse\, modern processors are incredibly complex\, and have (as it turns out) been designed without proper attention given to security for decades. As a result\, it’s not clear the extent of the problem or where to start to fix it—forcing both attackers and defenders into an arms race whose endpoint is unclear. \nThis talk will start with an overview of my group’s work to help address the processor security problem. I will then describe a specific recent project that we published earlier this year. Finally\, I will describe an eight-year journey that led to this project\, while trying to highlight the many people responsible and the various twists and turns that our thinking took along the way.
URL:https://seasevents.nmsdev7.com/event/ese-fall-seminar-title-tbd-13/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Colloquium
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241008T153000
DTEND;TZID=America/New_York:20241008T163000
DTSTAMP:20260403T135248
CREATED:20240927T162100Z
LAST-MODIFIED:20240927T162100Z
UID:10008110-1728401400-1728405000@seasevents.nmsdev7.com
SUMMARY:CIS Special Industry Seminar: "Improving Foundation Models Using Human Data"
DESCRIPTION:Foundation models including LLMs and multi-modal models released by OpenAI (GPT)\, Anthropic (Claude)\, Google (Gemini)\, Meta (Llama)\, and others have shown very impressive capabilities across a range of tasks. Some key drivers of this performance — such as investments in GPUs/compute\, model size\, and pre-training data — are relatively well understood. \nThis presentation will focus on a less understood\, yet extremely powerful lever that creates significant differentiation and competitive advantage among state-of-the-art models: the use of expert human data for Evaluations (“Evals”)\, Supervised Fine Tuning (“SFT”)\, Reinforcement Learning with Human Feedback (“RLHF”)\, and Direct Preference Optimization (“DPO”). \nThe talk will also outline some best practices for maximizing returns on financial investments in human data to achieve optimal model performance. This includes effective strategies for sourcing\, vetting\, hiring\, and managing expert human data teams\, as well as task design for Evals\, SFT\, RLHF\, and DPO\, along with processes and tooling to optimize team performance\, data quality and throughput. \nThis talk is open to Penn students\, COLM attendees\, and anyone in the Philadelphia area interested in AI\, data science\, or machine learning. You don’t need to be affiliated with the conference or the university — if you’re part of the DS/ML/AI community\, you’re welcome to join us.
URL:https://seasevents.nmsdev7.com/event/cis-special-industry-seminar-improving-foundation-models-using-human-data/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="Computer and Information Science":MAILTO:cherylh@cis.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241009T090000
DTEND;TZID=America/New_York:20241009T110000
DTSTAMP:20260403T135248
CREATED:20240925T180900Z
LAST-MODIFIED:20240925T180900Z
UID:10008107-1728464400-1728471600@seasevents.nmsdev7.com
SUMMARY:CBE Doctoral Dissertation Defense: "Investigation of Thin-film Cerium-based Oxides and Single Atom Catalysts Prepared by Atomic Layer Deposition" (Kai Shen)
DESCRIPTION:Abstract:\nMetal oxides play a critical role in heterogeneous catalysis\, acting as active catalysts in a wide range of reactions from dehydrogenation to selective and complete oxidation. However\, many catalytically active metal oxides suffer from low surface areas in their bulk form\, particularly after high-temperature calcination. This thesis focuses on cerium-based oxides due to their exceptional catalytic properties\, particularly in oxygen storage and release. By synthesizing these materials as supported thin films on high surface area γ-Al2O3\, we demonstrate that CeFeOx\, CeMnOx\, and CeVOx thin films not only retain high surface areas under elevated temperatures but also exhibit unique properties compared to their bulk counterparts\, including differences in lattice structure and redox behavior. When employed as a support for precious metals like Pt\, the choice of reducing or oxidizing pretreatment significantly impacts catalytic activity\, as it modulates the interaction between the support and metal. Furthermore\, we extended the ALD technique to the synthesis of single-atom catalysts. We invented a novel method\, Diluted Atomic Layer Deposition (DALD)\, to fabricate single-atom catalysts with precisely tunable weight loadings. DALD involves using a mixture of metal precursors and non-metallic ligands (e.g.\, Pt(acac)2 and Hacac) in controlled ratios. We demonstrated the successful deposition of Ir\, Rh\, and Pt onto γ-Al2O3\, achieving weight loadings ranging from 4 wt% down to as low as 0.04 wt%\, with corresponding metal particle sizes ranging from ~2 nm down to isolated single atoms. \nZoom Information:\nMeeting ID: 397 149 8817\nPasscode: uuVkZ2
URL:https://seasevents.nmsdev7.com/event/cbe-doctoral-dissertation-defense-investigation-of-thin-film-cerium-based-oxides-and-single-atom-catalysts-prepared-by-atomic-layer-deposition-kai-shen/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Doctoral,Student,Dissertation or Thesis Defense
ORGANIZER;CN="Chemical and Biomolecular Engineering":MAILTO:cbemail@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241009T120000
DTEND;TZID=America/New_York:20241009T131500
DTSTAMP:20260403T135248
CREATED:20240709T173859Z
LAST-MODIFIED:20240709T173859Z
UID:10008011-1728475200-1728479700@seasevents.nmsdev7.com
SUMMARY:ASSET Seminar: "Wood Wide Models"
DESCRIPTION:Abstract:  \nFoundation models are monolithic models that are trained on a broad set of data\, and which are then in principle fine-tuned to various specific tasks. But they are ill-suited to many heterogeneous settings\, for instance numeric tabular data\, or numeric time-series data\, where training a single monolithic model over a large collection of such datasets is not meaningful. For instance\, why should numeric times series of stock prices have anything to do with time series comprising the vital signs of an ICU patient? For such settings\, we propose the class of wood wide models. \nThe wood wide web is often used to describe an underground network of fungal threads that connect many trees and plants together\, which stands in contrast to a large concrete foundation on top of which we might build specialized buildings. Analogously\, in contrast to a single foundation model upon which one might build specialized models\, we can have many smaller wood wide models that all borrow subtler ingredients from each other. But to be able to share nutrients from the wood wide web\, trees need a special root based architecture that can connect to these fungal threads. Accordingly\, to operationalize wood wide models\, we develop a novel neuro-symbolic architecture\, that we term “neuro-causal”\,  that uses a synthesis of deep neural models and causal graphical models to automatically infer higher level symbolic information from lower level “raw features”\, while also allowing for rich relationships among the symbolic variables. Neuro-causal models retain the flexibility of modern deep neural network architectures while simultaneously capturing statistical semantics such as identifiability and causality\, which are important to discuss ideal\, target representations and their tradeoffs. But most interestingly\, these can further form a web of wood wide models when they borrow in part from a shared conceptual ontology\, as well as causal mechanisms. We provide conditions under which this entire architecture can be recovered uniquely. We also discuss efficient algorithms and provide experiments illustrating the algorithms in practice. \nZoom Link (if unable to attend in-person): https://upenn.zoom.us/j/98029108883
URL:https://seasevents.nmsdev7.com/event/asset-seminar-pradeep-ravikumar-carnegie-melon-university/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241009T150000
DTEND;TZID=America/New_York:20241009T160000
DTSTAMP:20260403T135248
CREATED:20241004T191650Z
LAST-MODIFIED:20241004T191650Z
UID:10008122-1728486000-1728489600@seasevents.nmsdev7.com
SUMMARY:Fall 2024 GRASP SFI: Tushar Kusnur\, The AI Institute\, “Robotic Information Gathering: Experiences and Perspectives”
DESCRIPTION:This will be a hybrid event with in-person attendance in Levine 307 and virtual attendance on Zoom. \nABSTRACT\nDespite all the interest and hype both from within and outside our community\, robotics is a relatively new and highly specialized field. We embody substantial diversity in research direction and style\, the focus and flavor of industrial companies\, and academic departments and programs. There is no single way to “do” robotics. Similarly\, the journeys of any two people in this field are unlikely to look the same. My work so far has shared a theme of “active information gathering” in academic research for novel approaches in specific contexts\, as well as industry research that balances novel and practical systems in broader contexts. Consistent with that distinction\, I will first talk about my past work at Carnegie Mellon including planning for robotic coverage and active sensing grounded in a system of multiple aerial vehicles. Next\, I will briefly highlight some technical challenges relevant to my current work on the “Watch\, Understand\, Do” project at the AI Institute\, grounded in mobile manipulation. I will share what I have learned about how different styles of research\, collaboration\, and project planning lend themselves to these different settings\, with an eye on how we can make more informed decisions in our individual and collective journeys as roboticists.
URL:https://seasevents.nmsdev7.com/event/fall-2024-grasp-sfi-tushar-kusnur/
LOCATION:Levine 307\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="General Robotics%2C Automation%2C Sensing and Perception (GRASP) Lab":MAILTO:grasplab@seas.upenn.edu
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241009T153000
DTEND;TZID=America/New_York:20241009T163000
DTSTAMP:20260403T135248
CREATED:20240816T202436Z
LAST-MODIFIED:20240816T202436Z
UID:10008052-1728487800-1728491400@seasevents.nmsdev7.com
SUMMARY:CBE Seminar: "Organic Batteries for a More Sustainable Future" (Jodie Lutkenhaus\, Texas A&M University)
DESCRIPTION:Abstract: \nCobalt\, nickel\, and lithium are essential ingredients in today’s lithium-ion batteries (LIBs)\, but their continued use presents economic\, ethical\, and environmental challenges. Society must now begin to consider the implications of a LIB’s full life cycle\, including the carbon footprint\, the economic and environmental costs\, and material access. These challenges motivate the case for degradable or recyclable batteries sourced from earth-abundant materials whose life cycle bears minimal impact on the environment. This presentation considers organic polymer-based batteries\, which have the potential to address many of these issues. Redox-active polymers form the positive and negative electrodes\, storing charge through a reversible redox mechanism. We demonstrate polypeptide radical batteries that degrade on command into amino acids and by-products as a first step toward circular organic batteries. Further\, we show the recycling of redox-active polymer electrodes using a solvent-based approach. Polymer-air batteries are examined as high-capacity alternatives to metal-air batteries. The molecular mechanism for each case is investigated\, revealing pathways forward for improving each polymer’s performance. Taken together\, organic batteries offer the promise of a circular platform free of critical elements.
URL:https://seasevents.nmsdev7.com/event/cbe-seminar-organic-batteries-for-a-more-sustainable-future-jodie-lutkenhaus-texas-am-university/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Chemical and Biomolecular Engineering":MAILTO:cbemail@seas.upenn.edu
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