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DTSTART;TZID=America/New_York:20210301T120000
DTEND;TZID=America/New_York:20210301T130000
DTSTAMP:20260407T054647
CREATED:20210120T164831Z
LAST-MODIFIED:20210120T164831Z
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SUMMARY:PSOC Webinar: "Polarity Signaling Ensures Epidermal Homeostasis By Coupling Cellular Mechanics and Genomic Integrity" (Sandra Iden)
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-polarity-signaling-ensures-epidermal-homeostasis-by-coupling-cellular-mechanics-and-genomic-integrity-sandra-iden/
LOCATION:PA
CATEGORIES:Seminar
ORGANIZER;CN="PSOC":MAILTO:manu@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210302T103000
DTEND;TZID=America/New_York:20210302T120000
DTSTAMP:20260407T054647
CREATED:20210226T162055Z
LAST-MODIFIED:20210226T162055Z
UID:10006676-1614681000-1614686400@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Fusion for Robot Perception and Controls"
DESCRIPTION:Machine learning has led to powerful advances in robotics: deep learning for visual perception from raw images and deep reinforcement learning (RL) for learning controls from trial and error. Yet\, these black-box techniques can often require large amounts of data\, have results difficult to interpret\, and fail catastrophically when dealing with out-of-distribution data. In this talk\, I will introduce the concept of “fusion” in robot perception and controls for robust\, sample efficient\, and generalizable robot learning. On the perception side\, we fuse multiple sensor modalities and demonstrate generalization to new task instances and robustness to sensor failures that are out-of-distribution. On the controls side\, we leverage fusion by combining known models with learned policies\, making our policy learning substantially more sample efficient.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-fusion-for-robot-perception-and-controls/
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:20210302T104500
DTEND;TZID=America/New_York:20210302T114500
DTSTAMP:20260407T054647
CREATED:20210222T205834Z
LAST-MODIFIED:20210222T205834Z
UID:10006666-1614681900-1614685500@seasevents.nmsdev7.com
SUMMARY:MSE Seminar: "Engineering topological phases in graphene moiré heterostructures"
DESCRIPTION:Taming topological electronic phases is a fundamental challenge and an important milestone on the way towards novel electronic devices and topological quantum computation. Recent advances in fabrication techniques have made van der Waals (vdW) heterostructures one of the most active platforms for the experimental investigation of topological electronic phases in 2D. Moiré superlattices\, which arise from small rotational misalignment between layers in vdW structures\, provide a powerful new way to control the electronic band structure. My talk will focus on using moiré superlattices in graphene heterostructures to realize quantum anomalous Hall (QAH) states that exhibit topological properties even in the absence of an external magnetic field. In contrast to magnetically doped topological insulators\, the QAH states in these moiré systems are driven by intrinsic strong electronic interactions rather than by magnetic doping. Remarkably\, the magnetization of this new family of QAH states arises predominantly from the orbital motion of the electrons rather than the electron spin. I will also discuss a novel effect originating from the curious magnetic properties of these “orbital magnets” that enables non-volatile electrical switching of the magnetic and topological orders.
URL:https://seasevents.nmsdev7.com/event/mse-seminar-engineering-topological-phases-in-graphene-moire-heterostructures/
LOCATION:PA
ORGANIZER;CN="Materials Science and Engineering":MAILTO:johnruss@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210302T140000
DTEND;TZID=America/New_York:20210302T150000
DTSTAMP:20260407T054647
CREATED:20201214T204837Z
LAST-MODIFIED:20201214T204837Z
UID:10006567-1614693600-1614697200@seasevents.nmsdev7.com
SUMMARY:MEAM Ph.D. Thesis Defense: "Reactive Planning with Legged Robots in Unknown Environments”
DESCRIPTION:Unlike the problem of safe task and motion planning in a completely known environment\, the setting where the obstacles in a robot’s workspace are not initially known and are incrementally revealed online has so far received little theoretical interest\, with existing algorithms usually demanding constant deliberative replanning in the presence of unanticipated conditions. Moreover\, even though recent advances show that legged platforms are becoming better at traversing rough terrains and environments\, legged robots are still mostly used as locomotion research platforms\, with applications restricted to domains where interaction with the environment is usually not needed and actively avoided. \nIn order to accomplish challenging tasks with such highly dynamic robots in unexplored environments\, this research suggests with formal arguments and empirical demonstration the effectiveness of a hierarchical control structure\, that we believe is the first provably correct deliberative/reactive planner to engage an unmodified general purpose mobile manipulator in physical rearrangements of its environment. To this end\, we develop the mobile manipulation maneuvers to accomplish each task at hand\, successfully anchor the useful kinematic unicycle template to control our legged platforms\, and integrate perceptual feedback with low-level control to coordinate each robot’s movement. \nAt the same time\, this research builds toward a useful abstraction for task planning in unknown environments\, and provides an avenue for incorporating partial prior knowledge within a deterministic framework well suited to existing vector field planning methods\, by exploiting recent developments in semantic SLAM and object pose and triangular mesh extraction using convolutional neural net architectures. Under specific sufficient conditions\, formal results guarantee collision avoidance and convergence to designated (fixed or slowly moving) targets\, for both a single robot and a robot gripping and manipulating objects\, in previously unexplored workspaces cluttered with non-convex obstacles. We encourage the application of our methods by providing accompanying software with open-source implementations of our algorithms.
URL:https://seasevents.nmsdev7.com/event/meam-ph-d-thesis-defense-reactive-planning-with-legged-robots-in-unknown-environments/
LOCATION:Zoom – Email MEAM for Link\, peterlit@seas.upenn.edu
CATEGORIES:Seminar,Dissertation or Thesis Defense
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210302T150000
DTEND;TZID=America/New_York:20210302T160000
DTSTAMP:20260407T054647
CREATED:20210210T205514Z
LAST-MODIFIED:20210210T205514Z
UID:10006646-1614697200-1614700800@seasevents.nmsdev7.com
SUMMARY:CIS Seminar: " Exterminating bugs in real systems"
DESCRIPTION:Software is everywhere\, and almost everywhere\, software is broken. Some bugs just crash your printer; others hand an identity thief your bank account number; still others let nation-states spy on dissidents and persecute minorities. \nThis talk outlines my work preventing bugs using a blend of programming languages techniques and systems design. First\, I’ll talk about securing massive\, security-critical codebases without clean slate rewrites. This means rooting out hard-to-find bugs—as in Sys\, which scales symbolic execution to find exploitable bugs in systems like the twenty-million line Chrome browser. It also means proving correctness of especially vulnerable pieces of code—as in VeRA\, which automatically verifies part of the Firefox JavaScript engine. Finally\, I’ll discuss work on stronger foundations for new systems—as in CirC\, a recent project unifying compiler infrastructure for program verification\, cryptographic proofs\, optimization problems\, and more.
URL:https://seasevents.nmsdev7.com/event/cis-seminar-exterminating-bugs-in-real-systems/
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:20210303T110000
DTEND;TZID=America/New_York:20210303T123000
DTSTAMP:20260407T054647
CREATED:20210226T171601Z
LAST-MODIFIED:20210226T171601Z
UID:10006677-1614769200-1614774600@seasevents.nmsdev7.com
SUMMARY:Spring 2021 GRASP SFI: “Safe and Data-efficient Learning for Robotics”
DESCRIPTION:Abstract: For successful integration of autonomous systems such as drones and self-driving cars in our day-to-day life\, they must be able to quickly adapt to ever-changing environments\, and actively reason about their safety and that of other users and autonomous systems around them. Even though control-theoretic approaches have been used for decades now for the control and safety analysis of autonomous systems\, these approaches typically operate under the assumption of a known system dynamics model and the environment in which the system is operating. To overcome these challenges\, machine learning approaches have been explored to operate autonomous systems intelligently and reliably in unpredictable environments based on prior data. However\, learning techniques widely used today are extremely data inefficient\, making it challenging to apply them to real-world physical systems. Moreover\, they lack the necessary mathematical framework to provide guarantees on correctness\, causing safety concerns as data-driven physical systems are integrated in our society.\nIn this talk\, we will present a toolbox of methods combining robust optimal control with data-driven techniques inspired by machine learning\, to enable performance improvement while maintaining safety. In particular\, we design modular architectures that combine system dynamics models with modern learning-based perception approaches to solve challenging perception and control problems in ​a priori unknown environments in a data-efficient fashion. These approaches are demonstrated on a variety of ground robots navigating in unknown buildings around humans based only on onboard visual sensors. Next\, we discuss how we can use optimal control methods not only for data-efficient learning\, but also to monitor and recognize the learning system’s failures\, and to provide online corrective safe actions when necessary. This allows us to provide safety assurances for learning-enabled systems in unknown and human-centric environments\, which has remained a challenge to date. \nClick here to join the Zoom meeting
URL:https://seasevents.nmsdev7.com/event/spring-2021-grasp-sfi-safe-and-data-efficient-learning-for-robotics/
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:20210303T150000
DTEND;TZID=America/New_York:20210303T160000
DTSTAMP:20260407T054647
CREATED:20210122T020721Z
LAST-MODIFIED:20210122T020721Z
UID:10006608-1614783600-1614787200@seasevents.nmsdev7.com
SUMMARY:CBE Seminar: "Metal-Organic Frameworks as Tunable Platforms for Gas Storage\, Chemical Separations and Catalysis"
DESCRIPTION:Abstract \nMetal-organic frameworks (MOFs) are a versatile class of nanoporous materials synthesized in a “building-block” approach from inorganic nodes and organic linkers.  By selecting appropriate building blocks\, the structural and chemical properties of the resulting materials can be finely tuned\, and this makes MOFs promising materials for applications such as gas storage\, chemical separations\, sensing\, drug delivery\, and catalysis.  This talk will focus on efforts to design or screen MOFs for separating mixtures of small molecules\, for gas storage\, and for catalysis.  Because of the predictability of MOF synthetic routes and the nearly infinite number of possible structures\, molecular modeling is an attractive tool for screening new MOFs before they are synthesized.  Modeling can also provide insight into the molecular-level details that lead to observed macroscopic properties.  This talk will illustrate how a combined modeling and experimental approach can be used to discover\, develop\, and ultimately design new MOFs for desired separation\, storage\, and catalysis applications.
URL:https://seasevents.nmsdev7.com/event/cbe-seminar-metal-organic-frameworks-as-tunable-platforms-for-gas-storage-chemical-separations-and-catalysis/
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:20210303T150000
DTEND;TZID=America/New_York:20210303T160000
DTSTAMP:20260407T054647
CREATED:20210210T211157Z
LAST-MODIFIED:20210210T211157Z
UID:10006647-1614783600-1614787200@seasevents.nmsdev7.com
SUMMARY:CIS Seminar: "The Measurement and Mismeasurement of Trustworthy ML"
DESCRIPTION:Across healthcare\, science\, and engineering\, we increasingly employ machine learning (ML) to automate decision-making that\, in turn\, affects our lives in profound ways. However\, ML can fail\, with significant and long-lasting consequences. Reliably measuring such failures is the first step towards building robust and trustworthy learning machines. Consider algorithmic fairness\, where widely-deployed fairness metrics can exacerbate group disparities and result in discriminatory outcomes. Moreover\, existing metrics are often incompatible. Hence\, selecting fairness metrics is an open problem. Measurement is also crucial for robustness\, particularly in federated learning with error-prone devices. Here\, once again\, models constructed using well-accepted robustness metrics can fail. Across ML applications\, the dire consequences of mismeasurement are a recurring theme. This talk will outline emerging strategies for addressing the measurement gap in ML and how this impacts trustworthiness.
URL:https://seasevents.nmsdev7.com/event/4212/
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:20210304T104500
DTEND;TZID=America/New_York:20210304T114500
DTSTAMP:20260407T054647
CREATED:20210222T210218Z
LAST-MODIFIED:20210222T210218Z
UID:10006667-1614854700-1614858300@seasevents.nmsdev7.com
SUMMARY:MSE Seminar: "Engineering nanoparticle-cell interactions: using a library-based approach to guide drug carrier design"
DESCRIPTION:Nanoparticles offer huge promise as drug delivery vehicles\, though their translation to the clinic is hampered due to limited accumulation at target disease sites. To overcome this hurdle\, we have employed colloidal layer-by-layer assembly to generate comprehensive nanoparticle libraries to study the role of chemical composition in nanoparticle targeting\, trafficking\, and uptake. In this seminar\, use of these libraries to study interactions with ovarian cancer cells and develop a new class of multifunctional drug carriers will first be discussed. Expansion of our library-based approach via the use of high throughput\, pooled screening and correlative genomics will be detailed in the second half. Key underlying principles from these studies will be highlighted for their potential to influence future nanocarrier design.
URL:https://seasevents.nmsdev7.com/event/mse-seminar-engineering-nanoparticle-cell-interactions-using-a-library-based-approach-to-guide-drug-carrier-design/
LOCATION:PA
ORGANIZER;CN="Materials Science and Engineering":MAILTO:johnruss@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210304T110000
DTEND;TZID=America/New_York:20210304T120000
DTSTAMP:20260407T054647
CREATED:20210211T142929Z
LAST-MODIFIED:20210211T142929Z
UID:10006649-1614855600-1614859200@seasevents.nmsdev7.com
SUMMARY:ESE Seminar: "High-Level Synthesis of Dynamically Scheduled Circuits"
DESCRIPTION:The slowdown in transistor scaling and the end of Moore’s law indicate a need to invest in new computing paradigms; specialized hardware devices\, such as FPGAs and ASICs\, are a promising solution as they can achieve high processing capabilities and energy efficiency. However\, a major barrier to the global success of specialized computing is the difficulty of hardware design. High-level synthesis (HLS) tools generate digital hardware designs from high-level programming languages (e.g.\, C/C++) and promise to liberate designers from low-level hardware description details. Yet\, HLS tools are still acceptable only for certain classes of applications and criticized for the difficulty of extracting the desired level of performance: generating good circuits still requires tedious code restructuring and hardware design expertise. \nIn this talk\, I will present a new HLS methodology that produces dynamically scheduled\, dataflow circuits out of C/C++ code; the resulting circuits achieve good performance out-of-the-box and realize behaviors that are beyond the capabilities of standard HLS tools. I will describe mathematical models to optimize the performance and area of the resulting circuits\, as well as techniques to achieve characteristics that standard HLS cannot support\, such as out-of-order memory accesses and speculative execution. These contributions redefine the HLS paradigm by introducing characteristics of modern superscalar processors to hardware designs; such behaviors are key for specialized computing to be successful in new contexts and broader application domains.
URL:https://seasevents.nmsdev7.com/event/ese-seminar-high-level-synthesis-of-dynamically-scheduled-circuits/
LOCATION:Zoom – Email ESE for Link jbatter@seas.upenn.edu
CATEGORIES:Seminar,Faculty,Colloquium,Student
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210304T110000
DTEND;TZID=America/New_York:20210304T130000
DTSTAMP:20260407T054647
CREATED:20210224T142236Z
LAST-MODIFIED:20210224T142236Z
UID:10006670-1614855600-1614862800@seasevents.nmsdev7.com
SUMMARY:BE Dissertation Defense: "Uncovering Constraints on Organoid Morphologies" (Lauren Beck)
DESCRIPTION:The Department of Bioengineering at the University of Pennsylvania and Dr. Arjun Raj are pleased to announce the Doctoral Dissertation Defense of Lauren Beck.\n\n\nTitle: Uncovering Constraints on Organoid Morphologies\n\nDate: March 4\, 2021\n\nTime: 11:00 AM\n\nZoom Link: https://upenn.zoom.us/j/94130810306\n\nThe public is welcome to attend via zoom.
URL:https://seasevents.nmsdev7.com/event/be-dissertation-defense-uncovering-constraints-on-organoid-morphologies-lauren-beck/
LOCATION:Zoom
CATEGORIES:Doctoral,Student,Dissertation or Thesis Defense
ORGANIZER;CN="Bioengineering":MAILTO:be@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210304T150000
DTEND;TZID=America/New_York:20210304T160000
DTSTAMP:20260407T054647
CREATED:20210210T215014Z
LAST-MODIFIED:20210210T215014Z
UID:10006648-1614870000-1614873600@seasevents.nmsdev7.com
SUMMARY:CIS Seminar: "Architecting Quantum Computing Systems in the Presence of Noise"
DESCRIPTION:Quantum computers may solve some problems beyond the reach of classical digital computers. However\, emerging quantum systems are typically noisy and difficult to control\, leaving a significant gap between the exacting requirements of quantum applications and the realities of noisy devices. Bridging this gap is crucial – my work adapts conventional computer systems techniques to meet the critical theoretical and experimental constraints in quantum processors. I divide my talk into three parts: (i) introducing my recent work on systematic noise mitigation for superconducting transmon qubits [MICRO’20]\, which enhances the robustness of quantum processors through coordination of control instructions; (ii) demonstrating efficient and reliable quantum memory management [ISCA’20]\, which implements automated tools for allocation\, reclamation and reuse of qubits in quantum programs\, much like in garbage collection for classical programs; (iii) discussing on-going work on implementing quasi-fault-tolerant rotation gates in quantum error correction\, which seeks to provide correctness guarantees for quantum applications by encoding quantum bits in a way that errors can be detected and corrected\, analogous to classical error-correcting codes.
URL:https://seasevents.nmsdev7.com/event/cis-seminar-architecting-quantum-computing-systems-in-the-presence-of-noise/
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:20210305T110000
DTEND;TZID=America/New_York:20210305T123000
DTSTAMP:20260407T054647
CREATED:20210226T213555Z
LAST-MODIFIED:20210226T213555Z
UID:10006678-1614942000-1614947400@seasevents.nmsdev7.com
SUMMARY:GRASP On Robotics: “Advancing Innovations for Robotic Teams in Complex Environments”
DESCRIPTION:Abstract: Complex real-world environments continue to present significant challenges for fielding robotic teams\, which often face expansive spatial scales\, difficult and dynamic terrain\, degraded environmental conditions\, and severe communication constraints. Breakthrough technologies call for integrated solutions across autonomy\, perception\, networking\, mobility\, and human teaming thrusts. As such\, the DARPA OFFSET program and the DARPA Subterranean Challenge seek novel approaches and new insights for discovering and demonstrating these innovative technologies\, to help close critical gaps for robotic operations in complex urban and underground environments. \nClick here to join the Zoom Webinar
URL:https://seasevents.nmsdev7.com/event/grasp-on-robotics-advancing-innovations-for-robotic-teams-in-complex-environments/
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:20210305T120000
DTEND;TZID=America/New_York:20210305T130000
DTSTAMP:20260407T054647
CREATED:20210211T184416Z
LAST-MODIFIED:20210211T184416Z
UID:10006650-1614945600-1614949200@seasevents.nmsdev7.com
SUMMARY:ESE Seminar: "Reliable Machine Learning in Feedback Systems"
DESCRIPTION:Machine learning techniques have been successful for processing complex information\, and thus they have the potential to play an important role in data-driven decision-making and control. However\, ensuring the reliability of these methods in feedback systems remains a challenge\, since classic statistical and algorithmic guarantees do not always hold. \nIn this talk\, I will provide rigorous guarantees of safety and discovery in dynamical settings relevant to robotics and recommendation systems. I take a perspective based on reachability\, to specify which parts of the state space the system avoids (safety) or can be driven to (discovery). For data-driven control\, we show finite-sample performance and safety guarantees which highlight relevant properties of the system to be controlled. For recommendation systems\, we introduce a novel metric of discovery and show that it can be efficiently computed. In closing\, I discuss how the reachability perspective can be used to design social-digital systems with a variety of important values in mind.
URL:https://seasevents.nmsdev7.com/event/ese-seminar-reliable-machine-learning-in-feedback-systems/
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
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