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DTSTART;TZID=America/New_York:20250423T120000
DTEND;TZID=America/New_York:20250423T131500
DTSTAMP:20260403T170303
CREATED:20250404T165640Z
LAST-MODIFIED:20250404T165640Z
UID:10008353-1745409600-1745414100@seasevents.nmsdev7.com
SUMMARY:ASSET Seminar: "Fake News\, Echo Chambers\, and Algorithms: A Data Science Perspective"
DESCRIPTION:Abstract: \nAmerican democracy has been undermined by an “infodemic” of fake news\, coupled with the widespread segregation of consumers into ideologically homogenous echo chambers by inscrutable algorithms deployed by rapacious social media platforms—or so we are told. In this talk\, I will critically examine claims of this sort—made frequently by politicians\, journalists\, and public intellectuals—summarizing several recent papers that leverage large-scale representative panel data for US media consumption. Contrary to conventional wisdom\, I argue that fake news is relatively rare\, echo chambers on television are much larger and stickier than their online equivalents\, and individual preferences dominate algorithmic filtering in determining consumption patterns. I further argue that it is trivially easy to mislead people without resorting to outright falsehoods and that researchers should accordingly pay more attention to biased information\, even when it is factually accurate. I conclude by introducing the media bias detector\, a recently launched project of Penn’s Computational Social Science Lab\, that seeks to characterize and expose bias in mainstream media. \nZoom Link: https://upenn.zoom.us/j/94075987313
URL:https://seasevents.nmsdev7.com/event/asset-seminar-duncan-watts-university-of-pennsylvania/
LOCATION:Amy Gutmann Hall\, Room 414\, 3333 Chestnut Street\, Philadelphia\, 19104\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250423T104500
DTEND;TZID=America/New_York:20250423T120000
DTSTAMP:20260403T170303
CREATED:20250403T132903Z
LAST-MODIFIED:20250403T132903Z
UID:10008348-1745405100-1745409600@seasevents.nmsdev7.com
SUMMARY:ESE Guest Seminar - "Efficient Computing for AI and Robotics: From Hardware Accelerators to Algorithm Design"
DESCRIPTION:The compute demands of AI and robotics continue to rise due to the rapidly growing volume of data to be processed; the increasingly complex algorithms for higher quality of results; and the demands for energy efficiency and real-time performance. In this talk\, we will discuss the design of efficient tailored hardware accelerators and the co-design of algorithms and hardware that reduce the energy consumption while delivering swift real-time and robust performance for applications including deep neural networks\, data analytics with sparse tensor algebra\, and autonomous navigation. Throughout the talk\, we will highlight important design principles\, methodologies\, and tools that can facilitate an effective design process and various forms of co-design that can broaden the design space. \n*Distinguished lecture supported by the IEEE Solid-State Circuits Society
URL:https://seasevents.nmsdev7.com/event/ese-guest-seminar-efficient-computing-for-ai-and-robotics-from-hardware-accelerators-to-algorithm-design/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar,Distinguished Lecture,Colloquium
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250422T121500
DTEND;TZID=America/New_York:20250422T131500
DTSTAMP:20260403T170303
CREATED:20250417T140906Z
LAST-MODIFIED:20250417T140906Z
UID:10008369-1745324100-1745327700@seasevents.nmsdev7.com
SUMMARY:ESE Ph.D. Thesis Defense: "Graph Neural Networks for Communication in Multi-Agent Systems"
DESCRIPTION:Communication networks support a wide range of applications in multi-agent systems by solving core problems such as routing\, scheduling\, and resource allocation. In this thesis\, we focus on data-driven routing and scheduling strategies using local information subject to constraints using Graph Neural Networks (GNNs). First\, we study information routing in communication networks with constant channel conditions and formulate it as a constrained learning problem. We propose a novel State Augmentation strategy to achieve faster convergence and achieve decentralized implementation using GNNs. The state augmentation based optimization framework leverages graph convolutions to generate optimal routing decisions using only local information from the nearby neighbors and achieves competitive performance without the need for supervision or global knowledge. Second\, we extend the framework to opportunistic routing in wireless networks\, where we leverage the broadcast nature of wireless channels for dynamic relay node selection. We integrate state augmentation with GNN-based distributed optimization to learn efficient routing policies that maximize end-to-end throughput. The learned models can be generalized across varying network sizes and multiple flows which are very robust to network variations. Third\, we design a real-time wireless ad-hoc network testbed to validate the proposed routing strategies under realistic channel conditions. Our evaluation demonstrates that the state augmentation combined GNN framework validates the simulational algorithms in terms of queue length stability while retaining stability and transferability properties without the requirement for retraining. Overall\, this thesis presents a scalable and decentralized approach to intelligent routing and scheduling in multi-agent systems by bridging graph-based learning with network optimization\, offering practical solutions for large-scale and dynamic communication systems.
URL:https://seasevents.nmsdev7.com/event/ese-ph-d-thesis-defense-graph-neural-networks-for-communication-in-multi-agent-systems/
LOCATION:Room 313\, Singh Center for Nanotechnology\, 3205 Walnut 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:20250422T101500
DTEND;TZID=America/New_York:20250422T114500
DTSTAMP:20260403T170303
CREATED:20250421T154457Z
LAST-MODIFIED:20250421T154457Z
UID:10008375-1745316900-1745322300@seasevents.nmsdev7.com
SUMMARY:CBE Doctoral Dissertation Defense: "Designing Solvent-Based Order Parameters for Characterizing Binding of Surfaces with Different Hydrophobicity Using Molecular Dynamics Simulations" (Jun Lu)
DESCRIPTION:Abstract: \n\n\n\nLife is dependent on water: most self-assembly and binding processes of biomolecules take place in water. Water-mediated interactions are an essential driving force behind these processes\, which is largely affected by the hydrophobicity of the binding surfaces. As many biomolecular binding interfaces are amphiphilic\, the hydrophobic interactions are largely affected by polar and charged groups near hydrophobic binding domains. Therefore\, the study of interactions between hydrophobic surfaces and hydrophilic surfaces are of great importance. We find that on the two extremes: purely hydrophobic and hydrophilic systems\, traditional sampling approaches in molecular dynamics (MD) simulations become ineffective for different reasons. Here\, we study the binding of surfaces with different hydrophobicity using the MD approach with specifically designed solvent-based order parameters to control. By sensibly choosing order parameter sets from 1. Separation distance\, 2. Solvent coordinates\, and 3. Electrostatic energy to control\, sampling pathologies can be mostly solved and the thermodynamics of binding of surfaces with different hydrophobicity can be studied.
URL:https://seasevents.nmsdev7.com/event/cbe-doctoral-dissertation-defense-designing-solvent-based-order-parameters-for-characterizing-binding-of-surfaces-with-different-hydrophobicity-using-molecular-dynamics-simulations-jun-lu/
LOCATION:Greenberg Lounge (Room 114)\, Skirkanich Hall\, 210 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:20250422T101500
DTEND;TZID=America/New_York:20250422T111500
DTSTAMP:20260403T170303
CREATED:20250401T205301Z
LAST-MODIFIED:20250401T205301Z
UID:10008346-1745316900-1745320500@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Biomedical Innovations for Global Health Research and Technology (BIGHEART): NOAS\, EXODUS\, iTEARS\, and BOAS"
DESCRIPTION:This presentation will discuss the concept of BIGHEART and present various illustrative examples of this approach. The initial focus will be on NOAS (Nanoscale Optical Antennas) within the context of quantitative life sciences and transformative medicine. NOAS facilitates the visualization of quantum biological electron transfer processes occurring in mitochondria within living cells\, supports the precise release of siRNAs with accurate spatiotemporal control\, enables the detection of oscillatory communication among living bacteria via extracellular vesicles\, and innovates ultrafast photonic PCR technology for enhanced precision in preventive medicine. EXODUS (Exosome Detection via the Ultrafast-Purification System) is meticulously designed to facilitate accurate diagnostics and therapeutic applications through the utilization of exosomes. The effective purification of exosomes from patients’ liquid biopsies enables comprehensive analyses and the advancement of translational medical treatments. iTEARS (Integrated Tear Exosome Analysis via Rapid-Isolation System) allows the detection of protein and miRNA biomarkers\, thereby enhancing the diagnostic capability of various diseases through the analysis of tear samples. BOAS (Brain Organoid Analysis System)\, which integrates biosensors and EEG for real-time\, non-invasive monitoring of brainwaves and extracellular vesicles (EVs)\, was developed to explore the connections between molecular signals and neurophysiological brainwaves. This study critically analyzes and offers insights into the interrelationships among secretomes\, electrophysiological brainwaves\, and the networks generated from human brain organoids. The in vitro models of BOAS serve as valuable tools for researchers investigating neuropathogenesis\, developing treatments for neurodegenerative diseases\, and exploring preventive medical therapies by studying the interactions between EVs and brainwaves. Furthermore\, the BIGHEART initiative endeavors to create solutions for preventive and personalized medicine that contribute to affordable global healthcare.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-biomedical-innovations-for-global-health-research-and-technology-bigheart-noas-exodus-itears-and-boas/
LOCATION:Wu & Chen Auditorium
CATEGORIES:Seminar
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250422T100000
DTEND;TZID=America/New_York:20250422T110000
DTSTAMP:20260403T170303
CREATED:20250421T142135Z
LAST-MODIFIED:20250421T142135Z
UID:10008374-1745316000-1745319600@seasevents.nmsdev7.com
SUMMARY:Spring 2025 GRASP Seminar: Robin Walters\, Northeastern University\, "Pushing the Limits of Equivariant Neural Networks"
DESCRIPTION:This will be a hybrid event with in-person attendance in AGH 306 and virtual attendance on Zoom. \nABSTRACT\nDespite the success of deep learning\, there remain challenges to progress. Deep models require vast datasets to train\, can fail to generalize under surprisingly small changes in domain\, and lack guarantees on performance. Incorporating symmetry constraints into neural networks has resulted in models called equivariant neural networks (ENN) which have helped address these challenges. I will discuss several successful applications\, such as trajectory prediction\, ocean currents forecasting\, and robotic control. However\, there are also limits to the effectiveness of current ENNs.  In many applications where symmetry is only approximate or does apply across the entire input distribution\, equivariance may not be the correct inductive bias to aid learning and may even hurt model performance.  I will discuss recent work theoretically characterizing errors that can result from mismatched symmetry biases which can be used for model selection. I will also suggest different methods for relaxing symmetry constraints so that approximately equivariant models can still be used in these situations.
URL:https://seasevents.nmsdev7.com/event/spring-2025-grasp-seminar-robin-walters-northeastern-university-pushing-the-limits-of-equivariant-neural-networks/
LOCATION:Amy Gutmann Hall\, Room 306\, 3317 Chestnut 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:20250422T100000
DTEND;TZID=America/New_York:20250422T110000
DTSTAMP:20260403T170303
CREATED:20250414T200632Z
LAST-MODIFIED:20250414T200632Z
UID:10008364-1745316000-1745319600@seasevents.nmsdev7.com
SUMMARY:MEAM Master's Thesis Defense: "In Situ Additive Manufacturing of Metal-Graphene Composites by Upcycling Polymers"
DESCRIPTION:Laser powder bed fusion (LPBF) is a bourgeoning additive manufacturing technique for rapid prototyping and creating unconventional designs using metal alloys. In parallel\, graphene has garnered significant research interest since its discovery\, owing to its remarkable mechanical and transport properties. Driven by the potential advances in additive manufacturing\, this project aims to harness the intrinsically high energy densities characteristic of LPBF for in situ formation of graphene using polymers as a direct carbon source during the metal 3D printing process. This innovative approach provides a comprehensive understanding of the underlying graphene formation mechanisms and to thoroughly characterize the resulting metal-graphene systems produced via LPBF. This thesis project investigates the processing parameters and properties of these 3D metal-graphene composites in comparison to conventional metal alloys.
URL:https://seasevents.nmsdev7.com/event/meam-masters-thesis-defense-in-situ-additive-manufacturing-of-metal-graphene-composites-by-upcycling-polymers/
LOCATION:4E9\, DRLB\, 209 S. 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Dissertation or Thesis Defense,Master's
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250422T090000
DTEND;TZID=America/New_York:20250422T100000
DTSTAMP:20260403T170303
CREATED:20250414T200007Z
LAST-MODIFIED:20250414T200007Z
UID:10008363-1745312400-1745316000@seasevents.nmsdev7.com
SUMMARY:MEAM Master's Thesis Defense: "Investigating Jet Interactions in the Multi-Jet SALP Robot"
DESCRIPTION:Jet propulsion is a common locomotion strategy in nature. We developed an underwater particle image velocimetry (PIV) system to investigate the hydrodynamic effects of the SALP (Salp-inspired Approach to Low-energy Propulsion) robot\, a soft underwater robot that swims using jet propulsion. Multiple SALP units can be physically connected to form a multi-SALP system\, coordinating their jets to achieve different motions. This thesis explores jet interactions in the multi-SALP robot under various physical arrangements and propulsion modes. PIV analysis identified three types of interactions: interaction\, weak interaction\, and no interaction. Results show that\, compared to non-interacting jets\, interacting jets can exhibit up to a 25% reduction in average velocity. This research enables further exploration of flow-field-enhanced performance in multi-SALP systems.
URL:https://seasevents.nmsdev7.com/event/meam-masters-thesis-defense-investigating-jet-interactions-in-the-multi-jet-salp-robot/
LOCATION:David Rittenhouse Laboratory Building\, Room 4E19\, 209 S. 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Dissertation or Thesis Defense,Master's
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250421T134500
DTEND;TZID=America/New_York:20250421T153000
DTSTAMP:20260403T170303
CREATED:20250326T171554Z
LAST-MODIFIED:20250326T171554Z
UID:10008328-1745243100-1745249400@seasevents.nmsdev7.com
SUMMARY:CBE Doctoral Dissertation Defense: "Structure and transport properties of nanoporous polymers derived from lyotropic mesophases" (Christopher Johnson)
DESCRIPTION:Abstract: \n\n\n\nBurgeoning energy and water scarcity challenges motivate the development of new membrane materials for charge transport as well as chemical and water separations. This in turn requires an improved understanding of the physics that govern charged and uncharged solute transport in membranes\, and particularly the motion of such species in nm-scale confinement in polymeric materials. This dissertation addresses transport in porous polymers with highly ordered nm-scale constrictions made of lyotropically assembled surfactant mesophases. The primary concerns of the thesis include the extent to which one can control the bulk material properties of a polymerized lyotropic mesophase material and what affects the bulk and dynamic pictures of transport in the resulting nanoconfined spaces. The explored polymers have controlled dimensions\, curvature\, and solid volumes\, allowing for in-depth discussion and alteration of the membrane’s internal environment. Fine control over the initial lyotropic mesophase is shown through a study incorporating a spiropyran dopant into a bicontinuous gyroid\, where pore size is actively reduced by 5 % in response to stimuli\, lowering acid vapor flux by 30 %. Membrane tensile properties are improved by careful adjustment of crosslinking groups in a similar bicontinuous gyroid mesophase. By optimizing the number of -diene crosslinking groups\, improvements in tensile strength are found in comparison to the unaltered polymer blend. Anion conductivities of two differing morphologies – the Ia3d gyroid and direct hexagonal cylinders – are investigated\, with a focus on determining differences in bulk properties on the basis of morphology\, anion identity\, and external conditions such as temperature and relative humidity. Potassium ion transport through another lyotropically self-assembled mesophase is performed and deemed a suitable candidate for further study. The self-assembled materials presented in this dissertation are found to be resilient\, with higher conductivity than previously reported ordered polymeric materials. Solvent composition is found to be a first order effect on conductivity\, and anion identity shows that nanoconfinement enhances differences in diffusivity due to solvation shell depletion and condensed charge pair formation. Additional work varying relative humidity and pore size unites bulk activation energies and conductivities with short-time dynamic phenomena. These findings motivate future work in understanding the dynamics of these systems and putting these porous polymers into useful scenarios.
URL:https://seasevents.nmsdev7.com/event/cbe-doctoral-dissertation-defense-structure-and-transport-properties-of-nanoporous-polymers-derived-from-lyotropic-mesophases-christopher-johnson/
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:20250421T120000
DTEND;TZID=America/New_York:20250421T140000
DTSTAMP:20260403T170303
CREATED:20250418T134743Z
LAST-MODIFIED:20250418T134743Z
UID:10008372-1745236800-1745244000@seasevents.nmsdev7.com
SUMMARY:ESE Ph.D. Thesis Defense: "Inverse design for engineering complex light-matter interaction"
DESCRIPTION:The inverse design paradigm has emerged as a transformative approach for the synthesis of nanophotonic structures\, offering a powerful alternative to conventional intuition-driven design. By approaching photonic device design as a computational optimization problem\, inverse design enables the systematic exploration of high-dimensional parameter spaces to uncover non- intuitive structures that meet complex performance targets. This dissertation advances the application of inverse design methodologies across multiple photonic platforms\, spanning from silicon photonics to lithium niobate\, and targets critical functionalities\, including analog optical computation\, nonlinear power limiting\, compact second-harmonic generation\, efficient electro- optic modulation\, and flat antennas with desired radiation properties. \nThe first part of the thesis focuses on the design of compact\, planar silicon photonic structures for wave-based analog computation. Using topology optimization\, I designed and demonstrated a spatially patterned monolithic silicon slab structure that performs optical vector–matrix multiplication by manipulating in-plane modes. Through a collaborative effort with the Aflatouni group\, this device\, fabricated using a CMOS-compatible process\, validates the ability of inverse design to encode mathematical operators on light propagation through patterned\, compact\, and mass-producible photonic architectures. \nThe second part of the thesis introduces an inverse-designed three-port Kerr nonlinear cavity that realizes optical intensity limiting. By tailoring the nonlinear response of a high-Q cavity under increasing optical input power\, the design achieves dynamic power redistribution between ports\, suppressing excess transmission in a self-regulating manner. This structure demonstrates how inverse design can be employed to precisely engineer a cavity-modal structure and nonlinear interactions to achieve dynamic functional behavior such as optical limiting. \nBuilding upon these insights\, the third part addresses the challenge of designing efficient and compact electro-optic (EO) modulator devices based on lithium niobate on insulator (LNOI). Traditional modulators face trade-offs among voltage-length product\, footprint\, and bandwidth. Here\, I present an inverse-designed EO phase modulator cavity that substantially reduces a phase modulator footprint from traditional cm-long waveguides into a micron-scale cavity featuring similar phase modulation performance. This significant reduction in size highlights the potential of inverse design to enable scalable\, high-density modulator architectures for applications in optical computing and high-speed data transmission. \nThroughout the thesis\, I develop and employ a general framework for photonic inverse design based on the time-harmonic Maxwell equations\, adjoint sensitivity analysis\, and density-based topology optimization. This framework is adapted to accommodate platform-specific constraints\, including fabrication limitations and incorporating material nonlinearities for novel functionalities\, while enabling the design of freeform photonic structures with thousands of degrees of freedom. The designs presented in this dissertation outperform conventional counterparts and highlight inverse design’s power to fundamentally reshape how we conceive\, engineer\, and deploy photonic systems.
URL:https://seasevents.nmsdev7.com/event/ese-ph-d-thesis-defense-inverse-design-for-engineering-complex-light-matter-interaction/
LOCATION:Moore 317\, 200 S 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:20250421T120000
DTEND;TZID=America/New_York:20250421T133000
DTSTAMP:20260403T170303
CREATED:20250418T195229Z
LAST-MODIFIED:20250418T195229Z
UID:10008373-1745236800-1745242200@seasevents.nmsdev7.com
SUMMARY:ESE 5160 Special Lecture: "Taking RoboRacer Off-Road: Learning Extreme Off-Road Mobility"
DESCRIPTION:In this guest lecture\, we will cover two recent research thrusts from the RobotiXX lab in taking RoboRacer off-road: high-speed off-road navigation and wheeled mobility on vertically challenging terrain. For high-speed off-road navigation\, we will introduce a sequential line of work with every work inspired by and built upon its prior work\, ranging from inverse kinodynamics learning based on inertia and vision to forward kinodynamics to enable competence awareness. For wheeled mobility on vertically challenging terrain\, we will introduce this new problem formulation and a recently developed ecosystem around this topic\, from research infrastructure\, kinodynamics modeling and planning\, to off-road mobility learning. We will also share the latest research on combining these two thrusts\, i.e.\, making high-speed vehicles safely fly over uneven terrain through in-air maneuvers.
URL:https://seasevents.nmsdev7.com/event/ese-5160-special-lecture-taking-roboracer-off-road-learning-extreme-off-road-mobility/
LOCATION:Towne 327
CATEGORIES:Seminar,Colloquium
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250421T120000
DTEND;TZID=America/New_York:20250421T130000
DTSTAMP:20260403T170303
CREATED:20250410T132041Z
LAST-MODIFIED:20250410T132041Z
UID:10008357-1745236800-1745240400@seasevents.nmsdev7.com
SUMMARY:Confirmation Bias\, the Original Error. A master class with Prof. Konrad Kording
DESCRIPTION:RSVP at https://bit.ly/3RzmdVH  \nLearn what confirmation bias is\, how to identify it in your own research\, and acquire the skills to mitigate it. Yes\, it turns out\, we’re all biased and this can negatively impact your research. Join Professor Konrad Kording in this live training session based on the Community for Rigor’s new educational unit.  \nCommunity for Rigor is a new Center at Penn\, an NIH/NINDS-funded initiative to create a free\, online curriculum to learn\, practice\, and promote scientifi rigor.
URL:https://seasevents.nmsdev7.com/event/confirmation-bias-the-original-error-a-master-class-with-prof-konrad-kording/
ATTACH;FMTTYPE=image/png:https://seasevents.nmsdev7.com/wp-content/uploads/2025/04/C4R_IG_Post_Event-Announcement_Ex_01-2.png
ORGANIZER;CN="Community for Rigor":MAILTO:c4r@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250421T090000
DTEND;TZID=America/New_York:20250421T110000
DTSTAMP:20260403T170303
CREATED:20250415T204212Z
LAST-MODIFIED:20250415T204212Z
UID:10008368-1745226000-1745233200@seasevents.nmsdev7.com
SUMMARY:ESE Ph.D. Thesis Defense: "Machine Learning for Large-Scale Cyber-Physical Systems"
DESCRIPTION:Directly training deep learning models for applications in large-scale cyber-physical systems can be intractable due to the large number of components and decision variables. Instead\, we focus on exploiting spatial symmetries in systems by designing size-generalizable architectures. Once trained on small-scale examples\, such architectures exhibit equivalent or comparable performance on large-scale systems. The first example we consider is a fully convolutional neural network\, for which we prove a bound that guarantees generalization performance. We demonstrate generalizability empirically with applications to multi-target tracking and mobile infrastructure on demand. Next\, we introduce a novel spatial transformer architecture design with two key properties in mind: locality and shift-equivariance. The proposed architecture uses shift-equivariant positional encodings and spatially windowed attention. Our experiments in two distributed collaborative multi-robot tasks show that these design features are necessary for size generalizability. Moreover\, we demonstrate that the spatial transformer architecture is capable of decentralized execution\, robust to communication delays\, can generalize to unseen tasks\, and performs state-of-the-art graph neural networks. Finally\, we refocus on a particularly challenging optimization problem in power systems: optimal power flow (OPF). By appropriately formulating the Lagrangian dual problem\, we train graph attention networks with improved optimality and feasibility. The training performance can also be reproduced on new power systems without further hyperparameter tuning.
URL:https://seasevents.nmsdev7.com/event/ese-ph-d-thesis-defense-machine-learning-for-large-scale-cyber-physical-systems/
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:20250418T143000
DTEND;TZID=America/New_York:20250418T170000
DTSTAMP:20260403T170303
CREATED:20250411T182716Z
LAST-MODIFIED:20250411T182716Z
UID:10008362-1744986600-1744995600@seasevents.nmsdev7.com
SUMMARY:MSE PhD Defense: “Chromatin as an Active and Adaptive Material”
DESCRIPTION:The three-dimensional organization of chromatin within the cell nucleus plays a critical role in regulating gene expression\, maintaining genome stability\, and guiding cellular responses to environmental cues. Despite advances in imaging and sequencing technologies\, the fundamental principles governing chromatin architecture and dynamics\, particularly the role of associated proteins like HP1α in driving these processes\, remain poorly understood. Additionally\, although multiple studies have demonstrated the sensitivity of chromatin organization to microenvironmental cues\, the underlying physical mechanisms driving this sensitivity and its impact on transcription\, especially in response to mechanical cues\, remain largely unexplored. Therefore\, elucidating the mechanisms that regulate chromatin organization\, particularly those shaped by the interplay of molecular interactions and external mechanical forces\, is crucial for understanding how genome structure and function are interrelated and how they might be controlled for therapeutic purposes. \nTo bridge this gap\, this thesis integrates theoretical modeling\, computational simulations\, and experimental approaches to investigate the principles that regulate genome organization. To explore the molecular drivers of chromatin structure\, we developed a novel polymer-based model using kinetic data of the chromatin architectural protein HP1α\, extracted from FRAP experiments in vivo. This model was designed to predict both the structural organization and dynamic behavior of constitutive heterochromatin\, which comprises gene-poor\, transcriptionally silent regions essential for genome stability. By incorporating HP1α binding kinetics and its affinity for methylated chromatin\, the model accurately predicts heterochromatin domain sizes and sub-diffusive motion. These predictions were validated using Hi-C and high-resolution imaging data\, revealing how transient HP1α interactions contribute to heterochromatin structure and mobility. Moreover\, the model provides a mechanistic explanation for the maintenance of epigenetic memory within these regions. \nNext\, to investigate how the mechanical properties of the cellular microenvironment influence genome architecture\, we designed experiments that replicate cellular responses to changes in tissue stiffness\, guided by predictions from our stiffness-dependent polymer model. Focusing on lung fibrosis\, we used IMR90 cells cultured on synthetically engineered hydrogels with defined stiffnesses representing healthy and fibrotic (disease-like) pulmonary environments. As substrate stiffness increased from soft\, healthy-like conditions to stiff\, fibrotic-like conditions\, cells exhibited marked changes in chromatin accessibility\, nuclear epigenetic landscape\, and gene expression. These effects were characterized using RNA-seq\, ATAC-seq\, and super-resolution OligoSTORM imaging. Our findings uncovered a mechanosensitive mechanism by which chromatin reorganization mediates both transcriptional responses and epigenetic regulation\, offering insights into the gene-level consequences of microenvironmental changes during disease progression. \nTogether\, this thesis presents a multiscale integrative framework that combines computational modeling with biological experimentation to advance our understanding of genome organization. By bridging the gap between molecular-scale interactions and tissue-level mechanical cues\, it provides new insights into how chromatin architecture encodes both dynamic responsiveness and long-term cellular identity. Ultimately\, this work contributes to a broader understanding of how biophysical principles shape gene regulation\, with implications for development\, disease progression\, and the design of novel therapeutic strategies.
URL:https://seasevents.nmsdev7.com/event/mse-phd-defense-chromatin-as-an-active-and-adaptive-material/
LOCATION:CEMB Conference room\, LRSM\, 3231 Walnut Street\, Room 112-C\, Philadelphia\, PA\, 19104\, United States
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:20250418T120000
DTEND;TZID=America/New_York:20250418T140000
DTSTAMP:20260403T170303
CREATED:20250415T143630Z
LAST-MODIFIED:20250415T143630Z
UID:10008366-1744977600-1744984800@seasevents.nmsdev7.com
SUMMARY:ESE Ph.D. Thesis Defense: "Neural Compression: Estimating and Achieving the Fundamental Limits"
DESCRIPTION:Neural compression\, which pertains to compression schemes that are learned from data using neural networks\, has emerged as a powerful approach for compressing real-world data. Neural compressors often outperform classical schemes\, especially in settings where reconstructions that are perceptually similar to the source are desired. Despite their empirical success\, the fundamental principles governing how neural compressors operate\, perform\, and trade off performance with complexity are not well-understood compared to classical schemes. \nWe aim to develop some of the fundamental principles of neural compression. We first introduce neural estimation methods that can estimate the theoretical rate-distortion limits of lossy compression for high dimensional sources using techniques from generative models. These methods illustrate that recent neural compressors are sub-optimal. Next\, we build on these insights to discuss neural compressors that approach optimality yet remain low-complexity through the use of lattice coding techniques. These are shown to approach the rate-distortion limits on high-dimensional sources without incurring a significant increase in complexity. Finally\, we develop low-complexity compressors for the rate-distortion-perception setting\, where an additional perception constraint ensures the source and reconstruction distributions are close in terms of a statistical divergence. These compressors combine lattice coding with the use of shared randomness via dithering over the lattice cells\, and provably achieve the fundamental rate-distortion-perception limits on the Gaussian source.
URL:https://seasevents.nmsdev7.com/event/ese-ph-d-thesis-defense-neural-compression-estimating-and-achieving-the-fundamental-limits/
LOCATION:Amy Gutmann Hall\, Room 515\, 3317 Chestnut Street\, Philadelphia\, 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:20250417T173000
DTEND;TZID=America/New_York:20250417T190000
DTSTAMP:20260403T170303
CREATED:20250321T175357Z
LAST-MODIFIED:20250321T175357Z
UID:10008323-1744911000-1744916400@seasevents.nmsdev7.com
SUMMARY:The Future of AI: A Fireside Chat with Yann LeCun\, Chief AI Scientist at Meta
DESCRIPTION:
URL:https://seasevents.nmsdev7.com/event/the-future-of-ai-a-fireside-chat-with-yann-lecun-chief-ai-scientist-at-meta/
LOCATION:Amy Gutmann Hall\, Auditorium\, 3333 Chestnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:AI Month
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250417T153000
DTEND;TZID=America/New_York:20250417T163000
DTSTAMP:20260403T170303
CREATED:20250410T191631Z
LAST-MODIFIED:20250410T191631Z
UID:10008358-1744903800-1744907400@seasevents.nmsdev7.com
SUMMARY:CIS Seminar: "Correctness Matters: Automatic Software Engineering in the age of Generative AI"
DESCRIPTION:Software engineers never start from a blank page\, but rather from an extant and usually long-running project in need of modification (for repair\, extension\, update\, etc.). One way to view modern programming is thus as a continual process of iteratively transforming existing programs into something new\, and hopefully better. \nIn this talk\, I will discuss my work on techniques to automate a broad range of software engineering and programming tasks. I position program transformation as a search problem over a space of potential program edits; automating transformation entails careful design choices to manage and successfully traverse this trivially infinite space.  I will focus especially on the fundamental challenge of ensuring that automatically transformed code is of acceptable quality\, and ways to tackle that challenge\, especially in light of recent advances in generative AI. Throughout\, I will highlight my vision of how to develop future-generation tools to help engineers make better software\, and make existing software better\, by carefully integrating domain knowledge and semantics-based reasoning with powerful heuristic search.
URL:https://seasevents.nmsdev7.com/event/cis-seminar-correctness-matters-automatic-software-engineering-in-the-age-of-generative-ai/
LOCATION:Levine 307\, 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:20250417T120000
DTEND;TZID=America/New_York:20250417T131500
DTSTAMP:20260403T170303
CREATED:20250131T222300Z
LAST-MODIFIED:20250131T222300Z
UID:10008262-1744891200-1744895700@seasevents.nmsdev7.com
SUMMARY:IDEAS/STAT Optimization Seminar: Resilient Distributed Optimization for Cyberphysical Systems
DESCRIPTION:Zoom link: https://upenn.zoom.us/j/98220304722 \n  \nAbstract:\nThis talk considers the problem of resilient distributed multi-agent optimization for cyberphysical systems in the presence of malicious or non-cooperative agents. It is assumed that stochastic values of trust between agents are available which allows agents to learn their trustworthy neighbors simultaneously with performing updates to minimize their own local objective functions. The development of this trustworthy computational model combines the tools from statistical learning and distributed consensus-based optimization. Specifically\, we derive a unified mathematical framework to characterize convergence\, deviation of the consensus from the true consensus value\, and expected convergence rate\, when there exists additional information of trust between agents. Under certain conditions\, we show  that the consensus protocol has almost sure convergence to a common limit value is possible even when malicious agents constitute more than half of the network;  the deviation of the converged limit\, from the nominal no attack case can be bounded with probability that approaches 1 exponentially\, and that correct classification of malicious and legitimate agents can be attained in (random) finite time almost surely. Further\, the expected convergence rate decays exponentially with the quality of the trust observations between agents. We then combine the trust-learning model within a distributed gradient-based method for solving a multi-agent optimization problem and characterize its performance.
URL:https://seasevents.nmsdev7.com/event/ideas-stat-optimization-seminar-angelia-nedich/
LOCATION:Amy Gutmann Hall\, Room 414\, 3333 Chestnut Street\, Philadelphia\, 19104\, United States
CATEGORIES:Seminar,Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250417T103000
DTEND;TZID=America/New_York:20250417T120000
DTSTAMP:20260403T170303
CREATED:20250219T155903Z
LAST-MODIFIED:20250219T155903Z
UID:10008296-1744885800-1744891200@seasevents.nmsdev7.com
SUMMARY:MSE Seminar: "Foundry Enabled Chip-Scale Photonics Technology and Applications"   Shaya Fainman - University of California\, San Diego (UCSD)
DESCRIPTION:Dense photonic integration requires miniaturization of materials\, devices\, circuits and systems\, including passive components (e.g.\, engineered composite metamaterials\, filters\, etc.)\, active components (e.g.\, modulators and nonlinear wave mixers) and integrated circuits (Fourier transform spectrometer\, programmable phase modulator of free space modes\, linear algebra processors\, etc.). In this talk we will discuss recent progress in developing CMOS compatible nonlinear optical materials as well as examples of foundry enabled silicon photonic circuits and systems. Specifically\, we will review silicon photonics-based Fourier transform spectrometer (Si-FTS) that can bring broadband operation and fine resolution to the chip scale. Here we will present the modeling and experimental demonstration of a thermally tuned Si-FTS accounting for dispersion\, thermo-optic non-linearity\, and thermal expansion.  We show how these effects modify the relation between the spectrum and interferogram of a light source and we develop a quantitative correction procedure through calibration with a tunable laser. Providing design flexibility and robustness\, the Si-FTS is poised to become a fundamental building block for on-chip spectroscopy. Moreover\, taking advantage of nanofabrication we will discuss on-chip spectrometers using stratified waveguide filters and machine learning. Moving forward\, we will discuss chip-scale integrated circuit/system that will allow to realize linear algebra accelerators with superior performance in speed\, energy consumption and size compared to its electronic counterpart. Such a system can be manufactured using monolithic CMOS process and impact such applications as 5G/6G and beyond wireless MIMO systems as well as deep learning and artificial intelligence.
URL:https://seasevents.nmsdev7.com/event/mse-seminar-foundry-enabled-chip-scale-photonics-technology-and-applications-shaya-fainman-university-of-california-san-diego-ucsd/
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:20250416T153000
DTEND;TZID=America/New_York:20250416T163000
DTSTAMP:20260403T170303
CREATED:20241223T152048Z
LAST-MODIFIED:20241223T152048Z
UID:10008205-1744817400-1744821000@seasevents.nmsdev7.com
SUMMARY:CBE Seminar: "Computational Design and Simulations of Soft Matter: From Molecular Insights to Functional Materials" (Antonia Statt\, UIUC)
DESCRIPTION:Abstract: \nI will present the phase separation behavior of different sequences of a coarse-grained model for sequence defined macromolecules. They exhibit a surprisingly rich phase behavior\, and not only conventional liquid-liquid phase separation is observed\, but also reentrant phase behavior. Most sequences form open phases consisting of large\, interconnected aggregates (e.g. string-like or membrane-like clusters)\, rather than a conventional dense liquids. Minor alterations in the sequence may lead to large changes in the overall phase behavior\, a fact of significant potential relevance for biology and for designing self-assembled structures using block copolymers. I will also discuss recent results from unsupervised manifold learning (UMAP) to classify the different aggregate types and what we can learn from machine learning. Using a bidirectional Recurrent Neural Network (RNN)\, we can now predict which sequence will self-assemble into what aggregate structure. With this framework\, we can investigate the effects of dispersity and sequence errors\, which is of immediate importance for experimental investigations. \nAdditionally\, I will briefly discuss how block copolymers interact with biological lipid membranes to form hybrid membranes. Hybrid phospholipid block copolymer bilayers display many properties\, seen in biomembranes such as selective transport phenomena\, synergistic elastic properties\, and structural phase transformations. Just like in biomembranes\, these fundamental properties of hybrid bilayers are often regulated by lateral phase separation. Understanding the molecular and physical cues that determine the formation of rafts or domains in hybrid membranes\, their size\, and morphology is paramount to elucidating and programming their function. We find that at low polymer content\, a new structure develops in which the bilayer leaflets unzip (but remain continuous) to incorporate nanodomains of hydrophobic butadiene globules. Our findings offer new insights into the morphology of biomembranes upon the insertion of transmembrane proteins with bulky hydrophobic residues.
URL:https://seasevents.nmsdev7.com/event/cbe-seminar-computational-design-and-simulations-of-soft-matter-from-molecular-insights-to-functional-materials-antonia-statt-uiuc/
LOCATION:Wu & Chen Auditorium
CATEGORIES:Seminar
ORGANIZER;CN="Chemical and Biomolecular Engineering":MAILTO:cbemail@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250416T150000
DTEND;TZID=America/New_York:20250416T160000
DTSTAMP:20260403T170303
CREATED:20250411T145839Z
LAST-MODIFIED:20250411T145839Z
UID:10008361-1744815600-1744819200@seasevents.nmsdev7.com
SUMMARY:Spring 2025 GRASP SFI: Anastasia Bizyaeva\, Cornell University\, “Nonlinear dynamics of social decision-making and belief formation”
DESCRIPTION:This will be a hybrid event with in-person attendance in Levine 307 and virtual attendance on Zoom. \nABSTRACT\nMotivated by the study of complex social behavior and by the bottom-up design of collaborative autonomy\, we present and analyze a nonlinear dynamic model of social belief formation. In our framework\, belief updates of individuals are informed by the interplay of external factors\, i.e. social network effects\, and internal factors\, i.e. individual biases and interdependencies between different belief dimensions. The model accounts for networked relationships between an individual’s internal belief representations and nonlinear processing of social information. Our analysis sheds light on mechanistic principles that enable groups to make fast and flexible collective decisions\, overcoming deadlocks to form strong beliefs when it is urgent to do so. We show how the structure of a social network and of the underlying belief system graph shapes emergent social decisions in the group\, and how effects of local biases and inputs can be amplified through feedback to enable tunably sensitive informed collective decisions. This work provides novel insights into the dynamics of complex social systems and motivates a new approach for the design of distributed decision-making strategies in engineered networks of social agents.
URL:https://seasevents.nmsdev7.com/event/spring-2025-grasp-sfi-anastasia-bizyaeva/
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:20250416T143000
DTEND;TZID=America/New_York:20250416T143000
DTSTAMP:20260403T170303
CREATED:20250411T143317Z
LAST-MODIFIED:20250411T143317Z
UID:10008360-1744813800-1744813800@seasevents.nmsdev7.com
SUMMARY:ESE Ph.D. Thesis Defense: "Training Adaptive and Sample-Efficient Autonomous Agents"
DESCRIPTION:AI agents\, both in the physical and digital worlds\, should generalize from their training data to three increasingly difficult levels of deployment: training tasks and environments\, training tasks and environments with variations\, and completely new tasks and environments. Moreover\, like humans\, they are expected to learn from as little training data as possible\, especially in the physical world\, and adapt with as little adaptation data as possible. This thesis is founded around and describes work that tackles these levels of generalization with an additional emphasis on sample-efficiency. \nWe start with a focus on training data efficiency and the simplest level of generalization from training data to training tasks and environments (a.k.a.\, level 1). AI agents\, especially in the physical world\, are usually trained via one of two paradigms: imitation learning or reinforcement learning. First\, we propose a plug-in model class to improve behavior cloning with any deep neural network (DNN) backbone that is particularly effective in the low-data regime. Second\, we leverage our proposed model class to guarantee the conformance of any DNN world model to physics and medical constraints\, in a highly data-efficient manner. Third\, we improve the sample-efficiency of reinforcement learning agents\, by an order of magnitude\, by leveraging expert interventions. \nNext\, we tackle the challenge of generalization to training tasks and environments with variations as well as completely new tasks and environments (a.k.a.\, levels 2 and 3)\, keeping both training and adaptation sample-efficiency in mind. Here\, we pre-train REGENT\, a retrieval-augmented generalist agent that can adapt to unseen robotics and game-playing environments via in-context learning\, without any finetuning. REGENT outperforms state-of-the-art generalist agents after pre-training on an order-of-magnitude fewer datapoints and with up to 3x fewer parameters. We also propose a strategy\, inspired by adaptive control\, to improve the robustness of the image encoder of REGENT\, an essential component for handling environment variations. \nFinally\, we bring REGENT to the real world by converting a Vision Language Action model (VLA) to a REGENTic VLA capable of generalizing to unseen objects and tasks through retrieval-augmentation and in-context learning. Further task-specific REGENTic-tuning substantially improves reliability\, surpassing a VLA directly fine-tuned on the same data. \nWe conclude by outlining future directions to expand the envelope of tasks and environments to which a general AI agent can adapt.
URL:https://seasevents.nmsdev7.com/event/ese-ph-d-thesis-defense-training-adaptive-and-sample-efficient-autonomous-agents/
LOCATION:Room 512\, Levine Hall\, 3330 Walnut 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:20250416T120000
DTEND;TZID=America/New_York:20250416T131500
DTSTAMP:20260403T170303
CREATED:20250404T165546Z
LAST-MODIFIED:20250404T165546Z
UID:10008352-1744804800-1744809300@seasevents.nmsdev7.com
SUMMARY:ASSET Seminar: "Learning Reliable and Robust Generative Intelligence"
DESCRIPTION:Abstract: \nRobust simulation and precise modeling of physical dynamics are essential for advancing perception\, planning\, and control in the development of generalist physical agents. In this talk\, I will present my research on building generative models that combine physical realism with scalability in high-dimensional environments. The presentation delves into both the theoretical foundations and practical implementations of our methods\, including the incorporation of 3D constraints into video diffusion models and the integration of autoregressive structures into continuous generative modeling to better handle complex data. By combining these techniques\, the models replicate intricate interactions in dynamic scenes and demonstrate their potential to support efficient\, data-driven learning across a broad range of applications. \nZoom Link: https://upenn.zoom.us/j/92594955973 \n 
URL:https://seasevents.nmsdev7.com/event/asset-seminar-jiatao-gu-university-of-pennsylvania/
LOCATION:Amy Gutmann Hall\, Room 414\, 3333 Chestnut Street\, Philadelphia\, 19104\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250415T153000
DTEND;TZID=America/New_York:20250415T173000
DTSTAMP:20260403T170303
CREATED:20250403T205157Z
LAST-MODIFIED:20250403T205157Z
UID:10008349-1744731000-1744738200@seasevents.nmsdev7.com
SUMMARY:ESE Ph.D. Thesis Defense: ''Manifold Filters and Neural Networks: Geometric Graph Signal Processing in the Limit''
DESCRIPTION:Graph Neural Networks (GNNs) are the tool of choice for scalable and stable learning in graph-structured data applications involving geometric information. My research addresses the fundamental questions of how GNNs can generalize across different graph scales and how they can remain stable on large-scale graphs. I do so by considering manifolds as graph limit models. In this talk\, we will explain how to build manifold convolutional filters and manifold neural networks (MNNs) as the limit objects of graph convolutional filters and GNNs when the graphs are sampled from manifolds. Using the Laplace-Beltrami operator exponentials to define manifold convolutions\, we demonstrate their algebraic equivalence to both graph convolutions and standard time convolutions in nodal and spectral domains. This equivalence provides a unifying framework to analyze key theoretical properties of GNNs: i) Convergence of GNNs to MNNs allows the scalability of GNNs on graphs across scales. ii) The stability of MNNs to deformations indicates the stability of large-scale GNNs. These findings offer practical guidelines for designing GNN architectures\, particularly by imposing constraints on the spectral properties of filter functions. Theoretical results are verified in real-world scenarios\, including point cloud analysis\, wireless resource allocation\, and wind field studies on vector fields.
URL:https://seasevents.nmsdev7.com/event/ese-ph-d-thesis-defense-manifold-filters-and-neural-networks-geometric-graph-signal-processing-in-the-limit/
LOCATION:Amy Gutmann Hall\, Room 515\, 3317 Chestnut Street\, Philadelphia\, 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:20250415T140000
DTEND;TZID=America/New_York:20250415T160000
DTSTAMP:20260403T170303
CREATED:20250328T185934Z
LAST-MODIFIED:20250328T185934Z
UID:10008341-1744725600-1744732800@seasevents.nmsdev7.com
SUMMARY:AI Month Alumni Panel
DESCRIPTION:Penn Engineering alumni working in AI will share industry insights in a panel discussion\, followed by student networking sessions. \nSchedule: \n2–3 p.m.  Panel Discussion and Q&A\n3:15–4 p.m.  Breakout Networking Sessions \nPanelists include: \nSara Dwyer (ENG’19)\nFounder & CEO at Parambil \nDavid Q. Sun (GEE’14\, GR’20)\nSenior Engineering Manager\, Siri & Information Intelligence\, AIML at Apple \nArchana Vemulapalli (GEN’01)\nCorporate Vice President\, Global Commercial Sales at AMD \nModerated by\nGeorge Pappas\nUPS Foundation Professor of Transportation in Computer and Information Science\, in Electrical and Systems Engineering and in Mechanical Engineering and Applied Mechanics
URL:https://seasevents.nmsdev7.com/event/ai-month-alumni-panel/
LOCATION:Berger Auditorium (Room 13)\, Skirkanich Hall\, 210 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Alumni,AI Month
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250415T120000
DTEND;TZID=America/New_York:20250415T133000
DTSTAMP:20260403T170303
CREATED:20250328T185355Z
LAST-MODIFIED:20250328T185355Z
UID:10008340-1744718400-1744723800@seasevents.nmsdev7.com
SUMMARY:What Does AI Tell Us About What It Means to Be Human
DESCRIPTION:Please RSVP here. \nWe are living in an age where capabilities previously thought to be hallmarks of human intelligence are increasingly being replicated\, or at least mimicked\, in artificial systems. \nTasks involving language\, reasoning\, perception and even interaction with the real world have all been demonstrated in silico. What does this fact tell us about the nature of human intelligence? In what ways is human intelligence similar to or different from machine intelligence? How have philosophers and other thinkers conceptualized issues related to thought and agency over the centuries and which of these ideas are relevant to today’s context? \nJoin us for a panel discussion where faculty members from Philosophy\, Cognitive Science and Computer Science will exchange perspectives on these issues. \nCamillo Jose Taylor (Moderator)\nRaymond S. Markowitz President’s Distinguished Professor\nComputer and Information Science \nChris Callison Burch\nProfessor of Computer and Information Science\nProgram Director of Online Master of Science in Engineering in Artificial Intelligence \nCharles Yang\nProfessor of Linguistics and Computer Science\nDirector\, Program in Cognitive Science \nCarlos Gray Santana\nAssociate Professor of Philosophy
URL:https://seasevents.nmsdev7.com/event/what-does-ai-tell-us-about-what-it-means-to-be-human/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:AI Month
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250415T101500
DTEND;TZID=America/New_York:20250415T111500
DTSTAMP:20260403T170303
CREATED:20241126T202355Z
LAST-MODIFIED:20241126T202355Z
UID:10008187-1744712100-1744715700@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Microscopic Mayhem: Cancer in Three-Dimensions"
DESCRIPTION:Critical to advancing immunotherapy and cell therapy in cancer is developing a deeper understanding pf the dynamics of immune cell-mediated cytotoxicity. The results from the multidisciplinary effort reported here include numerous measurements and movies of immune cell-mediated cytotoxicity with striking examples of serial killing\, foraging\, path-tracking\, cytokine gradients at tumor margins\, and killing dynamics\, in some cases revealing peak apoptotic signatures just minutes after T Cell engagement. \nIn vitro studies of immune cell killing are traditionally performed using time-lapse imaging and biochemical assays\, but these methods are often limited by spatial and temporal resolution\, throughput\, and the ability to extract the dynamics of cellular interactions. This study integrates high-resolution and high-speed laser scanning confocal microscopy with artificial intelligence (AI)\, and machine learning (ML) approaches to provide a high-resolution data-driven analysis of immune cell killing dynamics in vitro. \nWe have engineered a perfusion-enabled 3D culture system integrated microscopy to assess cellular dynamics for extended periods of time. Perfusion culture maintains the interstitial flow of liquid culture media\, clearing the microenvironment of toxic metabolites and reactive oxygen species. This platform uses a Liquid-Like Solids (LLS) to mimic the transport dynamics of a capillary bed. Integrated microscopy allows in situ quantification of spatiotemporal cytokine concentrations\, immune cell tracking\, immune cell killing dynamics\, and invasion dynamics.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-microscopic-mayhem-cancer-in-three-dimensions/
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:20250414T150000
DTEND;TZID=America/New_York:20250414T160000
DTSTAMP:20260403T170303
CREATED:20250409T190428Z
LAST-MODIFIED:20250409T190428Z
UID:10008356-1744642800-1744646400@seasevents.nmsdev7.com
SUMMARY:Spring 2025 GRASP Seminar: Sebastian Scherer\, Carnegie Mellon University\, "Resilient Autonomy for Extreme and Uncertain Environments"
DESCRIPTION:This will be an IN-PERSON event ONLY with in-person attendance in AGH 306. \nABSTRACT\nRobots show great promise if they can get out of the lab into the field and go beyond a single-operator per robot paradigm. However\, the unstructured nature of the real-world requires nuanced decision making of the robot. In this talk I will outline some of our approaches\, progress\, and results on multi-modal sensing\, providing nuanced perception inputs\, as well as navigation in difficult terrain\, and extensions to multi-robot teams\, and future directions of our research.
URL:https://seasevents.nmsdev7.com/event/spring-2025-grasp-seminar-sebastian-scherer-carnegie-mellon-university-resilient-autonomy-for-extreme-and-uncertain-environments/
LOCATION:Amy Gutmann Hall\, Room 306\, 3317 Chestnut 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;VALUE=DATE:20250414
DTEND;VALUE=DATE:20250416
DTSTAMP:20260403T170303
CREATED:20250410T192829Z
LAST-MODIFIED:20250410T192829Z
UID:10008359-1744588800-1744761599@seasevents.nmsdev7.com
SUMMARY:AI Infrastructure: Foundations for Energy Efficiency and Scalability
DESCRIPTION:Click here for more details. \nThe workshop will explore the state of the art in sustainable computing and share recent research at the intersection of technology\, economics\, and policy. Through invited talks\, panel discussions\, and breakout sessions\, participants will help shape a research agenda for the field. The workshop aims to produce a white paper and publish a research perspective in a major journal or media outlet. \nOrganization. The workshop is organized by Carbon Connect (https://carbonconnect.eco/)\, a team of researchers who have been awarded an Expedition in Computing by the National Science Foundation. It is co-hosted by Penn Engineering and Wharton School. \nThe workshop program agenda is available here.
URL:https://seasevents.nmsdev7.com/event/ai-infrastructure-foundations-for-energy-efficiency-and-scalability/
LOCATION:Jon M. Huntsman Hall\, 3730 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Conference,AI Month
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250411T140000
DTEND;TZID=America/New_York:20250411T150000
DTSTAMP:20260403T170303
CREATED:20250130T153134Z
LAST-MODIFIED:20250130T153134Z
UID:10008249-1744380000-1744383600@seasevents.nmsdev7.com
SUMMARY:PICS Colloquium: Multiscale simulations of soft matter: from block copolymers to biomolecular condensates
DESCRIPTION:Polymers are ubiquitous in both synthetic and biological materials and underlie technologies as diverse as surfactants\, adhesives\, proteins and DNA. One of the defining features of all polymeric materials is that they are characterized by a wide range of length scales\, often involving phenomena that span nanometers to microns. This hierarchy of length scales presents significant challenges for polymeric simulations. Existing models that can resolve phenomena at monomeric length scales (e.g. Atomistic simulations) are far too expensive to access mesoscopic processes\, whereas coarse-grained models that can access these large length scales must necessarily omit chemical details\, which often enter calculations through phenomenological parameters. As such\, there is a significant need for new computational techniques that can accurately and efficiently predict how chemical changes at the smallest length scales will propagate up to the largest length scales in a material. \n  \nIn this talk\, I describe recent efforts by my group to develop multiscale simulations of polymeric materials that can link monomeric to mesoscopic length scales. In the first part of the talk\, I describe a new “multi-representation” simulation method where particle-based and field-theoretic simulations are linked together into a unified framework. This approach can accelerate polymer simulations by several orders of magnitude and can rapidly equilibrate mesoscopic length scales while preserving monomer-scale details and dynamics. Notably\, this multi-representation approach leverages the formal equivalence between particle and field-based models and involves no approximation. The utility of this approach is illustrated by examining the self-assembly of complex sphere phases in block copolymer melts. In the second part of the talk\, I demonstrate how these multi-representation simulations can be extended to explain the phase separation of biomolecular condensates. Our approach can model proteins at amino acid resolution yet is efficient enough to access the long length and time scales that characterize condensate formation. Our approach can recapitulate recent experimental data on prion-like domains and can resolve how subtle modifications to the amino acid sequence can modulate their phase behavior. Finally\, we examine the phase separation of chromatin and how post-translational modifications to histone proteins can lead to transitions between liquid-like and solid-like behavior. Taken together\, this work demonstrates that multi-representation simulations that combine particle and field-theoretic simulations can unlock new insights into the multiscale physics that characterize synthetic and biological polymers.
URL:https://seasevents.nmsdev7.com/event/pics-colloquium-with-josh-lequieu/
LOCATION:PICS Conference Room 534 – A Wing \, 5th Floor\, 3401 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Colloquium
ATTACH;FMTTYPE=image/jpeg:https://seasevents.nmsdev7.com/wp-content/uploads/2025/01/lequieu.jpg
ORGANIZER;CN="Penn Institute for Computational Science (PICS)":MAILTO:dkparks@seas.upenn.edu
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