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DTSTART;TZID=America/New_York:20240422T120000
DTEND;TZID=America/New_York:20240422T130000
DTSTAMP:20260403T153915
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SUMMARY:MEAM Master's Thesis Defense: "Optical Analysis of Buckling-Induced Micro-Robotic Membranes"
DESCRIPTION:In recent years\, micro-robotic membranes have attracted increasing interest due to their unique properties and potential applications in various fields. The optical properties of these membranes have been playing a crucial role in the design and development of optical devices such as reflective displays with customizable colors. The primary challenge to understanding the mechanical-spectral interaction is the limitation of conventional microscopic techniques. The AFM cannot be employed when voltage is applied. Conversely\, hyperspectral imaging offers insights into the spectral response but lacks the capacity to infer topological characteristics directly. In this research\, I build an optical model that stands on the theoretical foundation laid by Maxwell’s equations\, Fresnel equations\, and the Transfer Matrix Method (TMM). By feeding the hyperspectral imaging\, the model can reconstruct the 3D topologies of buckling membranes. This is achieved through least-square regressions to accurately predict height data across various points. Through this methodology\, this research offers a novel framework for understanding the complex interplay between mechanical deformation and optical phenomena.
URL:https://seasevents.nmsdev7.com/event/meam-masters-thesis-defense-optical-analysis-of-buckling-induced-micro-robotic-membranes/
LOCATION:Levine 307\, 3330 Walnut 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:20240423T100000
DTEND;TZID=America/New_York:20240423T113000
DTSTAMP:20260403T153915
CREATED:20240408T175418Z
LAST-MODIFIED:20240408T175418Z
UID:10007938-1713866400-1713871800@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Flows About Superhydrophobic Surfaces"
DESCRIPTION:Superhydrophobic surfaces\, formed by air entrapment within the cavities of hydrophobic solid substrates\, offer a promising potential for hydrodynamic drag reduction. In several of the prototypical surface geometries the flows are two-dimensional\, governed by Laplace’s equation in the longitudinal problem and the biharmonic equation in the transverse problem. Moreover\, low-drag configurations are typically associated with singular limits. Accordingly\, the analysis of liquid slippage past superhydrophobic surfaces naturally invites the use of both singular-perturbation methods and conformal-mapping techniques. I will discuss the combined application of these methodologies to several emerging problems in the field.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-flows-about-superhydrophobic-surfaces/
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:20240423T110000
DTEND;TZID=America/New_York:20240423T140000
DTSTAMP:20260403T153915
CREATED:20240408T140915Z
LAST-MODIFIED:20240408T140915Z
UID:10007935-1713870000-1713880800@seasevents.nmsdev7.com
SUMMARY:Sustainable Catering - Earth Week 2024
DESCRIPTION:Learn about sustainable products and practices your caterer can implement to reduce waste\, minimize plastic and lower carbon footprint. Planet-friendly menu Plastic-alternative packaging and utensils Nutrition label for customized eating preferences Vendor engagement beyond delivery.
URL:https://seasevents.nmsdev7.com/event/sustainable-catering-earth-week-2024/
LOCATION:Lobby and Mezzanine\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Student,Staff
ATTACH;FMTTYPE=image/jpeg:https://seasevents.nmsdev7.com/wp-content/uploads/2024/04/Earth-Week-2024-PosterHorizontal-scaled-1.jpg
ORGANIZER;CN="SEAS Green Team":MAILTO:dianepa@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240423T120000
DTEND;TZID=America/New_York:20240423T130000
DTSTAMP:20260403T153915
CREATED:20240404T170254Z
LAST-MODIFIED:20240404T170254Z
UID:10007928-1713873600-1713877200@seasevents.nmsdev7.com
SUMMARY:MEAM Master's Thesis Defense: "Gaussian Process-Based Active Exploration Strategies in Vision and Touch"
DESCRIPTION:Robots struggle to understand object properties like shape\, material\, and semantics due to limited prior knowledge\, hindering manipulation in unstructured environments. In contrast\, humans learn these properties through interactive multi-sensor exploration. This work proposes fusing visual and tactile observations into a unified Gaussian Process Distance Field (GPDF) representation for active perception of object properties. While primarily focusing on geometry\, this approach also demonstrates potential for modeling surface properties beyond geometry. \nThe GPDF encodes signed distance\, gradients\, and uncertainty estimates. Starting with an initial visual shape estimate\, the framework iteratively refines the geometry by integrating dense vision measurements using differentiable rendering and tactile measurements at uncertain regions. By quantifying multi-sensor uncertainties\, it plans exploratory motions to maximize information gain for recovering precise 3D structures. To improve scalability\, it investigates approximation methods like inducing point parameterization for Gaussian Processes. This probabilistic multi-modal fusion enables active exploration and mapping of complex object geometries.
URL:https://seasevents.nmsdev7.com/event/meam-masters-thesis-defense-gaussian-process-based-active-exploration-strategies-in-vision-and-touch/
LOCATION:Meyerson Hall\, Room B2\, 210 S. 34th 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:20240424T100000
DTEND;TZID=America/New_York:20240424T110000
DTSTAMP:20260403T153915
CREATED:20240415T203631Z
LAST-MODIFIED:20240415T203631Z
UID:10007950-1713952800-1713956400@seasevents.nmsdev7.com
SUMMARY:PICS Colloquium: "Exploiting time-domain parallelism to accelerate neural network training and PDE constrained optimization"
DESCRIPTION:This talk will explore methods for accelerating numerical optimization constrained by transient problems using parallelism. Two types of transient problems will be considered. In the first case training algorithms for Neural ODEs will be discussed. Neural ODEs are a class of neural network architecture where the depth of the neural network (the layers) is modeled as a continuous time domain. For the second case\, transient PDE-constrained optimization problems will be described. In either case\, simulation-based optimization requires repeated executions of the simulator’s forward and backward (adjoint) time integration schemes. Consequently\, the arrow of time creates a major sequential bottleneck in the optimization process. Second\, for performance these methods rely strongly on the available parallelization for the forward and adjoint solves. Thus\, when forward and adjoint solvers are already operating at the limit of strong scaling and hardware utilization\, the arrow-of-time bottleneck cannot be overcome by additional parallelization across the spatial grid or network layers.  \nDeep neural networks are a powerful machine learning tool with the capacity to‚ learn complex nonlinear relationships described by large data sets. Despite their success training these models remains a challenging and computationally intensive undertaking. We will present a layer-parallel training algorithm that exploits a multigrid scheme to accelerate both forward and backward propagation. Introducing a parallel decomposition between layers requires inexact propagation of the neural network. The multigrid method used in this approach stitches these subdomains together with sufficient accuracy to ensure rapid convergence. We demonstrate an order of magnitude wall-clock time speedup over the serial approach\, opening a new avenue for parallelism that is complementary to existing approaches. We also discuss applying the layer-parallel methodology to recurrent neural networks and transformer architectures.  \nThe second half of this talk focuses on PDE-constrained optimization formulations. Solving optimization problems with transient PDE-constraints is computationally costly due to the number of nonlinear iterations and the cost of solving large-scale KKT matrices. These matrices scale with the size of the spatial discretization times the number of time steps. We propose a new 2-level domain decomposition preconditioner to solve these linear systems when constrained by the heat equation. Our approach leverages the observation that the Schur-complement is elliptic in time\, and thus amenable to classical domain decomposition methods. Further\, the application of the preconditioner uses existing time integration routines to facilitate implementation and maximize software reuse. The performance of the preconditioner is examined in an empirical study demonstrating the approach is scalable with respect to the number of time steps and subdomains.
URL:https://seasevents.nmsdev7.com/event/pics-colloquium-exploiting-time-domain-parallelism-to-accelerate-neural-network-training-and-pde-constrained-optimization/
LOCATION:PICS Conference Room 534 – A Wing \, 5th Floor\, 3401 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Colloquium
ORGANIZER;CN="Penn Institute for Computational Science (PICS)":MAILTO:dkparks@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240424T120000
DTEND;TZID=America/New_York:20240424T131500
DTSTAMP:20260403T153915
CREATED:20240104T163727Z
LAST-MODIFIED:20240104T163727Z
UID:10007790-1713960000-1713964500@seasevents.nmsdev7.com
SUMMARY:ASSET Seminar: "Statistical Methods for Trustworthy Language Modeling" (Tatsu Hashimoto\, Stanford University)
DESCRIPTION:ABSTRACT: \nLanguage models work well\, but they are far from trustworthy. Major open questions remain on high-stakes issues such as detecting benchmark contamination\, identifying LM-generated text\, and reliably generating factually correct outputs. Addressing these challenges will require us to build more precise\, reliable algorithms and evaluations that provide guarantees that we can trust. \nDespite the complexity of these problems and the black-box nature of modern LLMs\, I will discuss how in all three problems — benchmark contamination\, watermarking\, and factual correctness — there are surprising connections between classic statistical techniques and language modeling problems that lead to precise guarantees for identifying contamination\, watermarking LM-generated text\, and ensuring the correctness of LM outputs. \n  \nZOOM LINK (if unable to attend in-person): https://upenn.zoom.us/j/94597712175
URL:https://seasevents.nmsdev7.com/event/asset-seminar-tatsu-hashimoto-stanford-university/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240424T150000
DTEND;TZID=America/New_York:20240424T160000
DTSTAMP:20260403T153915
CREATED:20240408T195558Z
LAST-MODIFIED:20240408T195558Z
UID:10007940-1713970800-1713974400@seasevents.nmsdev7.com
SUMMARY:Spring 2024 GRASP SFI: Harish Ravichandar\, Georgia Institute of Technology\, "New Wine in an Old Bottle: A Structured Approach to Democratize Robot Learning"
DESCRIPTION:This will be a hybrid event with in-person attendance in Levine 307 and virtual attendance on Zoom. \nABSTRACT\nDecades of rigorous research in dynamical systems and control helped us integrate robots into a wide variety of domains\, ranging from factory floors to the moon. Today\, it would appear that deep learning has taken over the torch and will bring robots to our homes\, freeing us all from banal chores. In this utopian vision\, learning-based approaches tend to replace analytical methods. Moving away from handcrafted bespoke solutions to generalist robots that can operate in unstructured environments. But one can instead view learning-based and analytical approaches as two ends of a broad spectrum\, with one end optimizing for reliability (at the cost of human effort) and the other for emergent intelligence (at the cost of data and computation). In this talk\, I will argue why it is better for robots to be in the middle of this broad spectrum. Using manipulation as a case study\, I will discuss how our lab combines ideas from dynamical systems and machine learning to overcome three often-overlooked issues with contemporary methods: i) high barrier to entry due to demands for expensive computational resources and annotated data\, ii) inability to handle new tasks without relying on significant user expertise (e.g.\, for reward or controller design\, hyperparameter tuning\, data collection and curation)\, and iii) unreliable behaviors due to inscrutable and unpredictable learned policies. Addressing these issues will enable robot learning to escape the confines of well-resourced research labs and positively impact the larger society.
URL:https://seasevents.nmsdev7.com/event/spring-2024-grasp-sfi-harish-ravichandar/
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:20240424T153000
DTEND;TZID=America/New_York:20240424T163000
DTSTAMP:20260403T153915
CREATED:20240116T183524Z
LAST-MODIFIED:20240116T183524Z
UID:10007814-1713972600-1713976200@seasevents.nmsdev7.com
SUMMARY:John A. Quinn Distinguished Lecture in Chemical Engineering: "Exploring the Physics\, Materials Science\, and Biological Implications of Polyelectrolyte Complexation" (Matthew Tirrell\, University of Chicago)
DESCRIPTION:Abstract\nThe richness of liquid-liquid phase separation behavior in mixtures of oppositely-charged polyelectrolyte has been greatly illuminated recently in the polymer physics literature. Precise determinations of phase diagrams\, measurements of interfacial tension\, scattering measurements of chain configurations\, and increasingly insightful theory are all producing a clearer understanding of these phenomena. In parallel\, physics is also being brought to bear on manifestations of these behaviors in biology. Diverse biological examples related to liquid0liquid phase separation of polyelectrolyte complexes include membraneless organelles\, biological condensates that enhance transcription or protect from stress shock\, and other biological functions. This talk will spell out current understanding of the various contributions to the phase behavior\, including the role of various entropic contributions\, as well as the effects of charge density of the macromolecules. New\nresults on asymmetric mixtures will be presented\, which are more the norm in nature than the perfectly symmetrical mixtures in polymer physics studies.
URL:https://seasevents.nmsdev7.com/event/john-a-quinn-distinguished-lecture-in-chemical-engineering-exploring-the-physics-materials-science-and-biological-implications-of-polyelectrolyte-complexation-matthew-tirrell-university-of-c/
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:20240424T180000
DTEND;TZID=America/New_York:20240424T200000
DTSTAMP:20260403T153915
CREATED:20240405T155605Z
LAST-MODIFIED:20240405T155605Z
UID:10007932-1713981600-1713988800@seasevents.nmsdev7.com
SUMMARY:Tech Talks: Bridging Academia and Innovation in Philadelphia's Tech Hub
DESCRIPTION:Join us at the University of Pennsylvania for an event co-organized by the Mack Institute for Innovation Management and the City of Philadelphia Department of Commerce. This event is set to explore and expand collaboration opportunities that are remaking Philadelphia’s tech industry into a diverse and dynamic hub of innovation. Hear from city and commonwealth officials about their take on Philadelphia’s tech ecosystem and how the University of Pennsylvania can help solidify the city’s future as a leader in innovation. We’ll also hear from Penn entrepreneurs about their contribution to the local ecosystem and the benefits of starting their venture in Philadelphia. \nConfirmed Panelists: \n\nTempest Carter\, Director of Strategic Tech Initiatives at the City of Philadelphia\nJohn Swartley\, Chief Innovation Officer\, University of Pennsylvania\nJen Gilburg\, Deputy Secretary for Technology and Entrepreneurship for the Commonwealth of PA\, Department of\nCommunity and Economic Development\, Commonwealth of PA\nSteven Nichtberger\, MD; CEO\, Cabaletta Bio; Adjunct Professor\, Healthcare Management; Senior Fellow\, Vagelos LSM Program\, University of Pennsylvania\n\nModerator: \n\nDr. Valery Yakubovich\, Executive Director\, Mack Institute of Innovation Management\n\nAbout the Organizers:\nDepartment of Commerce for the City of Philadelphia champions the innovation economy\, with the Philadelphia Most Diverse Tech Hub (MDTH) initiative leading the charge in making the city a top\, inclusive tech destination. Launched by the Department in August 2023\, Tech Talks offer a platform to share resources\, discuss innovative ideas\, and foster community connections. \nMack Institute for Innovation Management at the Wharton School is a premier hub for innovation research and practice\, driving forward the integration of academic insights with real-world application. \nREGISTER HERE \n 
URL:https://seasevents.nmsdev7.com/event/tech-talks-bridging-academia-and-innovation-in-philadelphias-tech-hub/
LOCATION:Jon M. Huntsman Hall\, 3730 Walnut Street\, Philadelphia\, PA\, 19104\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240425T103000
DTEND;TZID=America/New_York:20240425T120000
DTSTAMP:20260403T153915
CREATED:20240421T211723Z
LAST-MODIFIED:20240421T211723Z
UID:10007955-1714041000-1714046400@seasevents.nmsdev7.com
SUMMARY:MSE Seminar: "Probabilistic Digital Twins for Structure Preserving Simulation and Scientific Discovery"
DESCRIPTION:Despite the recent flurry of work employing machine learning to develop surrogate models to accelerate scientific computation\, the “black-box” underpinnings of current techniques fail to provide the verification and validation guarantees provided by modern finite element methods. In this talk we present a data-driven finite element exterior calculus for building accelerated reduced-order models of multiphysics systems when the governing equations are either unknown or require closure. Key to the framework is a fully differentiable partition of unity which provides a machine learnable alternative to a traditional computational mesh\, upon which we simultaneously learn physical relevant control volumes alongside corresponding integral balance laws. We demonstrate that resulting models may realize speedup of over 1000x over traditional finite element simulations\, while guaranteeing the exact treatment of physical constraints and numerical stability. We then briefly summarize recent work developing Bayesian underpinnings for these models\, providing characterization of epistemic uncertainty which may be used to drive active learning tasks. \nWith tools for building probabilistic digital twins in hand\, we then turn to our work integrating physical models into high-throughput material discovery experiments to characterize process-structure-property relationships. In material science\, datasets are comparatively small relative to the combinatorially massive space of potential designs. We combat this by fusing information spanning multimodal characterization (e.g. XRD\,TEM\,SEM\,EBSD) of differing fidelity and throughput and incorporating data-driven models. We end by summarizing some campaigns conducted at Sandia National Laboratories applying these tools to physical vapor deposition\, metal additive manufacturing\, and electrodeposition.
URL:https://seasevents.nmsdev7.com/event/mse-seminar-probabilistic-digital-twins-for-structure-preserving-simulation-and-scientific-discovery/
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:20240425T140000
DTEND;TZID=America/New_York:20240425T150000
DTSTAMP:20260403T153915
CREATED:20240417T181645Z
LAST-MODIFIED:20240417T181645Z
UID:10007953-1714053600-1714057200@seasevents.nmsdev7.com
SUMMARY:PRECISE Seminar: Optical Coherence Tomography - From Conception to Current Frontiers
DESCRIPTION:Optical coherence tomography (OCT) is a technology invented in 1991 to image small critical tissue structures throughout the body with micrometer resolution. It is widely used in the management of eye and coronary heart diseases. In 2023\, OCT received broad attention when its inventors received the prestigious Lasker-DeBakey Clinic Medical Research Award and the National Medal of Technology and Innovation from President Biden. For me\, it was the culmination of 3 decades of work as an engineer\, clinician\, and translational researcher\, as well as an even longer journey as an immigrant who tapped into the potential of America’s great research universities. \nI will present OCT from an inventor’s perspective. The physical principles will be explained with illustrations on measuring the time-of-flight of light with interferometry. I will tell the story of the aha moment when the idea of OCT came to my mind\, as well as the rapid pace of development that made OCT a clinical reality. The biggest applications of OCT in the management of eye diseases will be shown. Recent advances made at OHSU that enable OCT to advance beyond the imaging of tissue structure to the detection of blood flow and photoreceptor function will be described. \nOCT is still a rapidly developing technology. The technical capabilities have improved in many aspects\, but the most astounding has been the continual improvement in imaging speed\, which has doubled approximately every 2.5 years over the past 3 decades. The technological advances have made more and more clinical applications feasible. I will present a vision for the broader applications of OCT\, which includes imaging the eye to assess brain and cardiovascular diseases\, as well as direct OCT imaging of other target organs such as the skin\, digestive tract\, brain\, inner/middle ear\, and teeth.
URL:https://seasevents.nmsdev7.com/event/precise-seminar-optical-coherence-tomography-from-conception-to-current-frontiers/
LOCATION:Room 307\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="PRECISE":MAILTO:wng@cis.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240426T103000
DTEND;TZID=America/New_York:20240426T114500
DTSTAMP:20260403T153915
CREATED:20240329T152051Z
LAST-MODIFIED:20240329T152051Z
UID:10007911-1714127400-1714131900@seasevents.nmsdev7.com
SUMMARY:Spring 2024 GRASP on Robotics: Marco Pavone\, Stanford University & NVIDIA\, "Rethinking AV Development with AV Foundation Models"
DESCRIPTION:This is a hybrid event with in-person attendance in Wu and Chen and virtual attendance on Zoom. \nABSTRACT\nFoundation models\, trained on vast and diverse data encompassing the human experience\, are at the heart of the ongoing AI revolution influencing the way we create\, problem solve\, and work. These models\, and the lessons learned from their construction\, can also be applied to the way we develop a similarly transformative technology\, autonomous vehicles. In this talk\, I will highlight recent research efforts towards rethinking elements of an AV program both in the vehicle and in the data center\, with an emphasis on (1) composing ingredients for universal and controllable end-to-end simulation\, (2) architecting autonomy stacks that leverage foundation models to generalize to long-tail events\, and (3) ensuring safety with foundation models in the loop.
URL:https://seasevents.nmsdev7.com/event/spring-2024-grasp-on-robotics-marco-pavone-stanford-university-nvidia-rethinking-av-development-with-av-foundation-models/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="General Robotics%2C Automation%2C Sensing and Perception (GRASP) Lab":MAILTO:grasplab@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240426T120000
DTEND;TZID=America/New_York:20240426T130000
DTSTAMP:20260403T153915
CREATED:20240404T171111Z
LAST-MODIFIED:20240404T171111Z
UID:10007929-1714132800-1714136400@seasevents.nmsdev7.com
SUMMARY:MEAM Master's Thesis Defense: "The Rheology and Microphysics of Monodisperse Synthetic Mucin"
DESCRIPTION:Mucus\, a complex fluid produced by every living organism\, has multiple essential functions including acting as an effective barrier layer in various bodily processes\, many of which involve important rheological (flow) and tribological (adhesive\, lubricative) functions. The primary component of mucuses are mucins – highly glycosylated\, linear polypeptides. Understanding how the structure and properties of mucins control the overall behavior of mucus is thus of substantial interest. However\, natural mucus is often contaminated with non-mucin constituents that affect the rheological response\, and purifying mucus without damaging the mucins themselves is difficult. Here\, fully synthetic\, monodisperse mucins have been prepared by a collaborator. Aqueous solutions of these mucins have been studied for comparison to the rheological response shown by natural mucin solutions. The purity and structural control of these synthetic mucins provide a model system where the mechanistic sources of the mucus’ rheological response can be isolated and identified. Experimental bulk rheometry demonstrates a shear-thinning behavior with a yield-stress fluid response. This behavior is attributed to associations between mucin molecules localized to the liquid-air interface\, which contradicts previous literature on natural mucins. This conclusion is supported by interfacial rheology measurements and by a mathematical model encapsulating the dynamics of a thin mucin layer under shear. This work furthers the understanding of the dynamics of mucin solutions and the qualitative microphysics surrounding their dynamics.
URL:https://seasevents.nmsdev7.com/event/meam-masters-thesis-defense-the-rheology-and-microphysics-of-monodisperse-synthetic-mucin/
LOCATION:Room 2C8\, David Rittenhouse Laboratory Building\, 209 South 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:20240426T140000
DTEND;TZID=America/New_York:20240426T150000
DTSTAMP:20260403T153915
CREATED:20240401T175825Z
LAST-MODIFIED:20240401T175825Z
UID:10007926-1714140000-1714143600@seasevents.nmsdev7.com
SUMMARY:PICS Colloquium: "Representations Learnt from Synthetic Volumes Enable Training-free Medical Image Analysis"
DESCRIPTION:Current medical image analysis projects involve months to years of data annotation and custom technical development. This talk introduces methods to train networks that generalize out-of-the-box to new modalities\, anatomies\, and datasets all without retraining for the specific use case. Our key contributions include (A) generative models driven by biomedical shape priors that synthesize wildly variable training data\, and (B) a multi-scale dense representation learning algorithm that leverages the synthetic data to learn contrast-invariant representations. We will show that a single U-Net pretrained in this manner can then extract features that enable state-of-the-art 3D multimodality image registration and can also serve as a general-purpose foundation model for few-shot segmentation across arbitrary biomedical datasets. We will also briefly demonstrate translational applications of the proposed methods to ongoing studies of disordered pregnancies in fetal and maternal MRI.
URL:https://seasevents.nmsdev7.com/event/pics-colloquium-representations-learnt-from-synthetic-volumes-enable-training-free-medical-image-analysis/
LOCATION:PICS Conference Room 534 – A Wing \, 5th Floor\, 3401 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Colloquium
ORGANIZER;CN="Penn Institute for Computational Science (PICS)":MAILTO:dkparks@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240426T140000
DTEND;TZID=America/New_York:20240426T160000
DTSTAMP:20260403T153915
CREATED:20240411T185142Z
LAST-MODIFIED:20240411T185142Z
UID:10007946-1714140000-1714147200@seasevents.nmsdev7.com
SUMMARY:Entrepreneurship Seminar Series: Pathways to Impact
DESCRIPTION:Entrepreneurship Seminar Series: Pathways to Impact \nThis session brings together a panel of current and former faculty and PhDs that have brought their technology to market and have worked in both academia and commerce. Panelists will discuss the opportunities and approaches they took to create companies\, leverage experience in academia\, and drive research into commercial success. \nRefreshments will be provided. \nWho: Faculty\, Post-Docs\, & PhD students \nWhere: Towne 327 \nWhen: 2-4:00 pm\, Friday April 26\, 2024 \nAdd to calendar: Apple  Google  Office 365  Outlook  Outlook.com  Yahoo \nRSVP Here (Not required)
URL:https://seasevents.nmsdev7.com/event/entrepreneurship-seminar-series-pathways-to-impact/
LOCATION:Towne 327
CATEGORIES:Faculty,Doctoral,Graduate,Panel Discussion,Postdoctoral
ORGANIZER;CN="Penn Engineering Entrepreneurship":MAILTO:sevile@seas.upenn.edu
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