BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Penn Engineering Events - ECPv6.15.18//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-ORIGINAL-URL:https://seasevents.nmsdev7.com X-WR-CALDESC:Events for Penn Engineering Events REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20210314T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20211107T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20220313T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20221106T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20230312T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20231105T060000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=America/New_York:20220124T120000 DTEND;TZID=America/New_York:20220124T130000 DTSTAMP:20260406T072545 CREATED:20220121T221747Z LAST-MODIFIED:20220121T221747Z UID:10007032-1643025600-1643029200@seasevents.nmsdev7.com SUMMARY:BE Seminar: "Visualizing the Unseen: Enabling Precision Oncology Through Microenvironment-Triggered Diagnostics and Therapeutics" (Liangliang Hao) DESCRIPTION:The successful integration of precision diagnostics with new personalized therapies opens numerous doors to improve the management of a variety of diseases. In cancer\, tissue-environmental features of tumor progression and invasion\, including aberrant extracellular matrix remodeling\, stromal composition changes\, and immune cell engagement\, create engineering opportunities for use in developing novel biomarkers and therapeutic targets. In this seminar\, I will focus on an emerging paradigm in precision diagnostics: synthetic biomarkers. These bioengineered sensors harness microenvironment-dependent activation mechanisms to generate molecular reporters that can be read out in biofluids. To overcome the limitations often associated with molecular disease biomarkers (cross-reactivity with healthy tissues\, dilution in biofluids below detectable levels\, and rapid degradation of released material)\, I have engineered next-generation synthetic biomarker platforms with enhanced specificity and clinical actionability by 1) developing CRISPR-Cas-amplifiable urinary reporters to detect and differentiate disease states at the point-of-care; 2) advancing theranostic technologies to precisely target the hallmarks of cancer metastasis in specific tissues; 3) improving noninvasive in vivo imaging capabilities to allow for rapid assessment of disease status and interventional efficacy over time. Collectively\, these studies highlight the use of chemical tools with built-in cancer-reactive modules\, embracing a vision for precision health through integrated strategies: identification/monitoring\, imaging\, and intervention in a personalized manner.  URL:https://seasevents.nmsdev7.com/event/be-seminar-visualizing-the-unseen-enabling-precision-oncology-through-microenvironment-triggered-diagnostics-and-therapeutics-liangliang-hao/ LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States CATEGORIES:Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20220125T100000 DTEND;TZID=America/New_York:20220125T120000 DTSTAMP:20260406T072545 CREATED:20211108T221639Z LAST-MODIFIED:20211108T221639Z UID:10006961-1643104800-1643112000@seasevents.nmsdev7.com SUMMARY:MEAM Seminar: "'Tiny-but-tough' GaN- and Graphene-based Nanoelectronics for Extreme Harsh Environments" DESCRIPTION:Gallium nitride (GaN) nanoelectronics have operated at temperatures as high as 1000°C making it a viable platform for robust space-grade (“tiny-but-tough”) electronics and nano-satellites. Even with these major technological breakthroughs\, we have just begun the “GaN revolution.” New communities are adopting this nanoelectronic platform for a multitude of emerging device applications including the following: sensing\, energy harvesting\, actuation\, and communication. In this talk\, we will review and discuss the benefits of GaN’s two-dimensional electron gas (2DEG) over silicon’s p-n junction for space exploration applications (e.g.\, radiation-hardened\, temperature-tolerant Venus probes). In addition\, we will discuss the use of 2D materials such as graphene in space exploration applications\, as well as the potential for synthesis of graphene mesostructures in prolonged microgravity environments on the International Space Station (ISS). URL:https://seasevents.nmsdev7.com/event/meam-seminar-tiny-but-tough-gan-and-graphene-based-nanoelectronics-for-extreme-harsh-environments/ 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:20220125T130000 DTEND;TZID=America/New_York:20220125T140000 DTSTAMP:20260406T072545 CREATED:20220120T213214Z LAST-MODIFIED:20220120T213214Z UID:10007029-1643115600-1643119200@seasevents.nmsdev7.com SUMMARY:MEAM PhD Thesis Defense: "Accelerated Design of Architected Materials with Geometric Heterogeneity for Enhanced Failure Characteristics" DESCRIPTION:Nature provides countless examples of the use of material heterogeneity to enhance the failure properties of materials. Many biological materials\, such as bone\, marine shells\, and fish scales\, are extremely resilient to fracture and failure. These often consist of regions that are highly mineralized and stiff and regions of biopolymers that are extremely soft. In practice\, combining such disparate materials in synthetic systems is fraught with difficulties\, such as poor interfacial adhesion. However\, we will show\, geometric heterogeneity can lead to similar enhancements to failure characteristics\, including distribution of voids (inspired by bamboo) and spatial variations in fiber orientation (inspired by many materials\, such as aorta). With the nearly arbitrary arrangements of materials that is enabled by 3D printing\, it is possible to produce systems with bioinspired\, spatially-varying microstructures that results in large improvements to failure properties. \nIn this dissertation\, I will discuss two types of geometric heterogeneities that can be easily introduced to architected materials enhancing their failure characteristics. First\, inspired by the microstructure of the dactyl club of the Mantis shrimp\, we show how geometric defects that are intrinsic to extrusion-based additive processes (voids and weak interfaces) can be spatially arranged in a helical (Bouligand) pattern to produce complex crack patterns and enhanced energy absorption. Next\, we show how spatial variations in fiber orientation (inspired by aorta) can be produced using direct ink writing (DIW)\, leading to soft composites with high toughness and fatigue threshold. \nSuch geometric heterogeneities in architected materials\, and the 3D printing processes used to create them\, introduce a large number of parameters into the material design process\, such as infill layer angle\, fiber orientation\, void placement\, etc. Bio-inspiration provides a starting point and some basic intuition about how to design heterogeneous materials for improved failure properties\, but it cannot guarantee optimal failure properties. I will therefore conclude the talk with a discussion of the use of Bayesian optimization for the acceleration of the design of architected heterogeneous materials with optimal failure properties. We will introduce a multi-fidelity Bayesian optimization approach to accelerate the design of heterogeneous triangular lattices with maximal energy absorption during compressive loading. URL:https://seasevents.nmsdev7.com/event/meam-phd-thesis-defense-accelerated-design-of-architected-materials-with-geometric-heterogeneity-for-enhanced-failure-characteristics/ LOCATION:Zoom – Email MEAM for Link\, peterlit@seas.upenn.edu CATEGORIES:Doctoral,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:20220126T150000 DTEND;TZID=America/New_York:20220126T160000 DTSTAMP:20260406T072545 CREATED:20220113T181555Z LAST-MODIFIED:20220113T181555Z UID:10007013-1643209200-1643212800@seasevents.nmsdev7.com SUMMARY:Spring 2022 GRASP SFI: Paloma Sodhi\, Carnegie Mellon University\, "Learning in factor graphs for tactile perception" DESCRIPTION:*This will be a HYBRID Event with in-person attendance in Levine 307 and Virtual attendance via Zoom \nFactor graphs offer a flexible and powerful framework for solving large-scale\, nonlinear inference problems as encountered in robot perception. Typically these methods rely on handcrafted models that are efficient to optimize. However\, robots often perceive the world through complex\, high-dimensional sensor observations. For instance\, consider a robot manipulating an object in-hand and receiving high-dimensional tactile observations from which it must infer latent object poses. Can we learn models for such observations directly from sensor data? \nIn this talk\, I will discuss algorithms and representations for learning observation models end-to-end with optimizers in the loop. I will present a novel approach\, LEO\, that casts the problem of learning observation models as cost function learning that makes no assumptions on the differentiability of the underlying optimizer. I will also discuss different feature representations for extracting salient information from tactile image observations. We will evaluate these approaches on a real-world application of tactile perception for robot manipulation where we demonstrate reliable object tracking in hundreds of trials across planar pushing and in-hand manipulation tasks. URL:https://seasevents.nmsdev7.com/event/spring-2022-grasp-sfi-paloma-sodhi-learning-in-factor-graphs-for-tactile-perception/ 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 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20220126T153000 DTEND;TZID=America/New_York:20220126T163000 DTSTAMP:20260406T072545 CREATED:20220110T225701Z LAST-MODIFIED:20220110T225701Z UID:10007006-1643211000-1643214600@seasevents.nmsdev7.com SUMMARY:CBE Seminar: "Revealing the Unknown Dynamics of High-Energy Density Lithium-Metal Batteries" DESCRIPTION:Abstract \nHigh-energy density batteries will play a remarkable role in hurdling global climate change. My research focuses on the fundamental understandings of their electrochemical reaction mechanisms and the design of materials\, protocols\, and characterization tools to enable their safe operations over long-term use. First\, I will discuss about the previously overlooked dynamics of detached lithium metal filaments during battery operations. This discovery leads to the recovery of lost capacities in lithium-metal batteries and enables fast charging in lithium-ion batteries. Next\, I will introduce a characterization tool for the on-board monitoring of battery health based on pressure evolutions. In addition to capturing the early signs of battery failure\, this pressure sensing system offers new insights into the battery degradation process. Overall\, the combination of fundamental study and the rational design of materials/protocols/characterization tools opens broad opportunities toward a clean energy future. URL:https://seasevents.nmsdev7.com/event/cbe-seminar-revealing-the-unknown-dynamics-of-high-energy-density-lithium-metal-batteries/ 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:20220127T103000 DTEND;TZID=America/New_York:20220127T113000 DTSTAMP:20260406T072545 CREATED:20220118T175658Z LAST-MODIFIED:20220118T175658Z UID:10007021-1643279400-1643283000@seasevents.nmsdev7.com SUMMARY:MSE Seminar: "Engineering Organoid Models for Understanding Human Neurodevelopment and neurological disorders" DESCRIPTION:Human Induced pluripotent stem cells (hiPSCs) has the potential to generate all cell types of a human body under 2D culture conditions or form organ like structures-organoids\, under 3D culture conditions. Brain organoid cultures from human iPSCs have been recently developed to recapitulate the cellular composition and the cytoarchitecture of the developing brain. These hiPSC based organoid model systems offer unique advantages in understanding molecular and cellular mechanisms governing embryonic neural development and in modeling neurodevelopmental and neurological disorders. I will discuss our recent work in developing brain region specific organoid systems and apply them to understand human brain development and neurotropism of SARS-CoV-2. URL:https://seasevents.nmsdev7.com/event/mse-seminar-engineering-organoid-models-for-understanding-human-neurodevelopment-and-neurological-disorders/ LOCATION:https://upenn.zoom.us/j/96715197752 ORGANIZER;CN="Materials Science and Engineering":MAILTO:johnruss@seas.upenn.edu END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20220127T110000 DTEND;TZID=America/New_York:20220127T120000 DTSTAMP:20260406T072545 CREATED:20220121T154409Z LAST-MODIFIED:20220121T154409Z UID:10007030-1643281200-1643284800@seasevents.nmsdev7.com SUMMARY:ESE Spring Colloquium - "The One Learning Algorithm Hypothesis-- Towards Universal Machine Learning Models and Architectures" DESCRIPTION:We revisit the “One Learning Algorithm Hypothesis” of Andrew Ng (Google Brain) according to which the brain of higher-level animals and of humans processes and perceives sensory data (vision\, sound\, haptics) with the same abstract algorithmic architecture. We develop models\, based on our earlier work on automatic target recognition with radar and other sensors\, face recognition and image classification\, which employ a multi-resolution preprocessor\, followed by a group-invariance based feature extractor\, followed by a machine learning (ML) module that employs the two fundamental algorithms of Kohonen Learning Vector Quantization (LVQ)\, for supervised learning\, and Self-Organizing Map (SOM)\, for unsupervised learning. In addition the model and algorithms utilize a “global” feedback from the output of the overall system back to the feature extractor and to the multiresolution preprocessor. We first summarize briefly our older results with such algorithms and their remarkable\, domain agnostic\, performance on various applications. We then provide our recent results on the mathematical analysis of the resulting Tree Structure Learning Vector Quantization (TSLVQ) ML architecture and algorithms. We introduce and integrate Deterministic Annealing (DA) with our older architecture and demonstrate the resulting tremendous reduction in data required for learning and application. The new algorithms allow even on-line progressive learning. We utilize Bregman divergences as dissimilarity measures\, which allows us to provide direct transition from “dissimilarity distance” to probability of error\, which cannot be achieved with the commonly used metric-based dissimilarity measures. We show that many deep learning network architectures can be mapped to this “universal” architecture. We show that the integrated algorithm converges to the true Bayes decision surface\, albeit with variable resolution at various parts of it\, as required. The latter brings a tight connection to integrated hypothesis testing with compressed data. We demonstrate the results in various applications and close with future directions and extensions. URL:https://seasevents.nmsdev7.com/event/ese-spring-colloquium-the-one-learning-algorithm-hypothesis-towards-universal-machine-learning-models-and-architectures/ LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States CATEGORIES:Seminar,Colloquium ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20220127T153000 DTEND;TZID=America/New_York:20220127T163000 DTSTAMP:20260406T072545 CREATED:20220121T221023Z LAST-MODIFIED:20220121T221023Z UID:10007031-1643297400-1643301000@seasevents.nmsdev7.com SUMMARY:BE Seminar: "Orchestrating Cellular Regeneration at Organ Scale" (Yvon Woappi) DESCRIPTION:Large scale tissue damage\, such as organ failure and burn injury\, is a leading cause of morbidity and death. However\, the mechanisms underlying full regeneration of organs remain poorly understood. As the largest organ system in the body\, the integumentary system is a composite tissue assembly evolutionarily adapted for healing. Consequently\, its complex physiology requires multifaceted cooperation between several distinct cell populations and cell lineages of embryologically distinct origins. Equally integrated within this dynamic process is local immune response that produces mitogenic and inhibitory signals throughout the restoration procedure. There remains a significant gap in understanding how these processes are orchestrated\, and how various skin cell populations from distinct developmental lineages functionally cooperate to regenerate tissue at organ scale. My research seeks to characterize the molecular language of tissue healing and to harness this malleable dialect for the regeneration of mammalian tissues. Through the development of organoid models of wound regeneration\, and the coupling of these systems with novel gene-editing approaches\, my work is enabling the functional understanding of the multifaceted cellular events executed throughout restorative healing. This seminar will describe these high throughput technologies and will illustrate their utility in identifying novel regulators of tissue healing. URL:https://seasevents.nmsdev7.com/event/be-seminar-orchestrating-cellular-regeneration-at-organ-scale-yvon-woappi/ LOCATION:PA CATEGORIES:Seminar ORGANIZER;CN="Bioengineering":MAILTO:be@seas.upenn.edu END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20220127T153000 DTEND;TZID=America/New_York:20220127T163000 DTSTAMP:20260406T072545 CREATED:20220125T182832Z LAST-MODIFIED:20220125T182832Z UID:10007036-1643297400-1643301000@seasevents.nmsdev7.com SUMMARY:CIS Seminar: "Social Reinforcement Learning" DESCRIPTION:Social learning helps humans and animals rapidly adapt to new circumstances\, coordinate with others\, and drives the emergence of complex learned behaviors. What if it could do the same for AI? This talk describes how Social Reinforcement Learning in multi-agent and human-AI interactions can improve coordination\, learning\, generalization\, and lead to the development of agents better able to anticipate and serve human needs. I propose a unified method for improving coordination and communication based on causal social influence. Beyond coordination\, I demonstrate how multi-agent training can be a useful tool for improving learning and generalization even in the single-agent setting. I present PAIRED\, in which an adversary learns to construct training environments to maximize regret between a pair of learners\, leading to the generation of a complex curriculum of environments that improve both learning and zero-shot generalization. Ultimately\, the goal of my research is to create intelligent agents that can assist humans with everyday tasks; this means interacting effectively with humans\, and learning from human-AI interactions. I show that learning from human social and affective cues scales more effectively than learning from manual feedback. However\, it depends on accurate recognition of such cues. Therefore I will discuss how to dramatically enhance the accuracy of affect detection models using personalized multi-task learning to account for inter-individual variability. Together\, this work argues that Social RL is a valuable approach for developing more general\, sophisticated\, and cooperative AI\, which is ultimately better able to serve human needs. URL:https://seasevents.nmsdev7.com/event/cis-seminar-social-reinforcement-learning/ 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:20220128T103000 DTEND;TZID=America/New_York:20220128T114500 DTSTAMP:20260406T072545 CREATED:20220119T193218Z LAST-MODIFIED:20220119T193218Z UID:10007025-1643365800-1643370300@seasevents.nmsdev7.com SUMMARY:GRASP on Robotics: Andreas Malikopoulos\, University of Delaware\, “Separation of Learning and Control for Cyber-Physical Systems” DESCRIPTION:*This will be a VIRTUAL Event with attendance via Zoom Webinar here.  \nCyber-physical systems (CPS)\, in most instances\, represent systems of subsystems with an informationally decentralized structure. To derive optimal control strategies for such systems\, we typically assume an ideal model\, e.g.\, controlled transition kernel. Such model-based control approaches cannot effectively facilitate optimal solutions with performance guarantees due to the discrepancy between the model and the actual CPS. On the other hand\, in most CPS there is a large volume of data with a dynamic nature which is added to the system gradually in real time and not altogether in advance. Thus\, traditional supervised learning approaches cannot always facilitate robust solutions using data derived offline. By contrast\, applying reinforcement learning approaches directly to the actual CPS might impose significant implications on safety and robust operation of the system. In this talk\, I will present a theoretical framework founded at the intersection of control theory and learning that circumvents these challenges in deriving optimal strategies for CPS. In this framework\, we aim at identifying a sufficient information state for the CPS that takes values in a time-invariant space\, and use this information state to derive separated control strategies. Separated control strategies are related to the concept of separation between the estimation of the information state and control of the system. By establishing separated control strategies\, we can derive offline the optimal control strategy of the system with respect to the information state\, which might not be precisely known due to model uncertainties or complexity of the system\, and then use learning methods to learn the information state online while data are added gradually to the system in real time. This approach could effectively facilitate optimal solutions with performance guarantees in a wide range of CPS applications such as emerging mobility systems\, networked control systems\, smart power grids\, cooperative cyber-physical networks\, cooperation of robots\, and internet of things. URL:https://seasevents.nmsdev7.com/event/grasp-on-robotics-separation-of-learning-and-control-for-cyber-physical-systems/ 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 END:VCALENDAR