BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Penn Engineering Events - ECPv6.15.18//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Penn Engineering Events
X-ORIGINAL-URL:https://seasevents.nmsdev7.com
X-WR-CALDESC:Events for Penn Engineering Events
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20230312T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20231105T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20240310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20241103T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20250309T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20251102T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240207T153000
DTEND;TZID=America/New_York:20240207T163000
DTSTAMP:20260404T034844
CREATED:20240116T175613Z
LAST-MODIFIED:20240116T175613Z
UID:10007806-1707319800-1707323400@seasevents.nmsdev7.com
SUMMARY:CBE Seminar: "Molecular Microscopy with Single Cell Transcriptomic Data Resolves RNA Liquid Biopsies" (Sevahn Vorperian\, Stanford)
DESCRIPTION:Abstract\nInvasive biopsy is the gold standard for diagnosing several diseases; however\, these procedures offer a limited\, localized view of the disease pathology to the physician and are not risk-free to the patient. Cell-free RNA (cfRNA) in blood plasma reflects dynamic gene expression changes and can facilitate early disease diagnosis\, yet current cfRNA assays fall short of the cellular resolution afforded by an invasive biopsy. In this talk\, I will first resolve plasma cfRNA at cell type resolution using single cell transcriptomic data alongside approaches from machine learning and data science\, which enable a systems-view into the underlying molecular patterns within these high-dimensional biological data. I will then describe how this molecular microscope can noninvasively reflect changes observed in invasive biopsy across various diseases and facilitate the study of biofluids beyond the blood. These findings expand the achievable resolution within the RNA liquid biopsy biomolecular repertoire and broaden opportunities in precision medicine for complex diseases in hard-to-biopsy tissues.
URL:https://seasevents.nmsdev7.com/event/cbe-seminar-molecular-microscopy-with-single-cell-transcriptomic-data-resolves-rna-liquid-biopsies-sevahn-vorperian-stanford/
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:20240208T150000
DTEND;TZID=America/New_York:20240208T170000
DTSTAMP:20260404T034844
CREATED:20240201T155129Z
LAST-MODIFIED:20240201T155129Z
UID:10007845-1707404400-1707411600@seasevents.nmsdev7.com
SUMMARY:Evolution of Data Storytelling: Women in Data Science x Penn Museum Tour + Workshop
DESCRIPTION:Join us for an exciting kick-off event at the Penn Museum as part of the Women in Data Science (WiDS) @ Penn conference\, where the past meets the future in a guided tour and storytelling workshop.
URL:https://seasevents.nmsdev7.com/event/evolution-of-data-storytelling-women-in-data-science-x-penn-museum-tour-workshop/
LOCATION:The Penn Museum\, 3260 South St\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240208T153000
DTEND;TZID=America/New_York:20240208T163000
DTSTAMP:20260404T034844
CREATED:20240116T154201Z
LAST-MODIFIED:20240116T154201Z
UID:10007803-1707406200-1707409800@seasevents.nmsdev7.com
SUMMARY:BE Seminar: "Mapping and engineering gene expression with chemical and spatial lenses" (Hailing Shi\, Broad Institute & MIT)
DESCRIPTION:Precise RNA expression\, tailored to specific brain regions\, cell types\, and subcellular compartments\, is pivotal for orchestrating complex brain functions. In the first part of my talk\, I will introduce a confocal imaging-based spatial transcriptomics platform\, STARmap\, that seamlessly combines in situ hybridization\, hydrogel tissue chemistry\, and in situ sequencing technologies. Leveraging scalable experimental and computational pipelines\, we have constructed a comprehensive spatial cell atlas of the mouse brain\, revealing subregion-specific cell types\, previously undiscovered tissue architectures\, and viral tropisms. The second part of my talk transits to chemical modifications on messenger RNAs (mRNAs) and their far-reaching implications in gene expression regulation and the development of RNA therapeutics. I will discuss the messenger-oligonucleotide conjugate RNAs (mocRNAs) design\, showcasing how we can harness chemical modifications for engineering enhanced gene delivery vectors. Looking into the future\, I aim to innovate and integrate chemical and spatial profiling approaches to understand tissue health and disease in-depth.
URL:https://seasevents.nmsdev7.com/event/be-seminar-mapping-and-engineering-gene-expression-with-chemical-and-spatial-lenses-hailing-shi-broad-institute-mit/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Bioengineering":MAILTO:be@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240208T153000
DTEND;TZID=America/New_York:20240208T163000
DTSTAMP:20260404T034844
CREATED:20240129T163131Z
LAST-MODIFIED:20240129T163131Z
UID:10007832-1707406200-1707409800@seasevents.nmsdev7.com
SUMMARY:CIS Seminar: "Rethinking Data Use in Large Language Models"
DESCRIPTION:Large language models (LMs) such as ChatGPT have revolutionized natural language processing and artificial intelligence more broadly. In this talk\, I will discuss my research on understanding and advancing these models\, centered around how they use the very large text corpora they are trained on. First\, I will describe our efforts to understand how these models learn to perform new tasks after training\, demonstrating that their so-called in context learning capabilities are almost entirely determined by what they learn from the training data. Next\, I will introduce a new class of LMs—nonparametric LMs—that repurpose this training data as a data store from which they retrieve information for improved accuracy and updatability. I will describe my work on establishing the foundations of such models\, including one of the first broadly used neural retrieval models and an approach that simplifies a traditional\, two-stage pipeline into one. I will also discuss how nonparametric models open up new avenues for responsible data use\, e.g.\, by segregating permissive and copyrighted text and using them differently. Finally\, I will envision the next generation of LMs we should build\, focusing on efficient scaling\, improving factuality\, and decentralization.
URL:https://seasevents.nmsdev7.com/event/cis-seminar-rethinking-data-use-in-large-language-models/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="Computer and Information Science":MAILTO:cherylh@cis.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240209T083000
DTEND;TZID=America/New_York:20240209T150000
DTSTAMP:20260404T034844
CREATED:20240201T155601Z
LAST-MODIFIED:20240201T155601Z
UID:10007844-1707467400-1707490800@seasevents.nmsdev7.com
SUMMARY:Women in Data Science @ Penn
DESCRIPTION:The Wharton School and Penn Engineering are proud to host the fifth annual Women in Data Science (WiDS) @ Penn Conference on February 8-9\, 2024\, on the University of Pennsylvania’s campus. A celebrated interdisciplinary event\, WiDS @ Penn welcomes academic\, industry\, and student speakers from across the data science landscape to celebrate its diversity\, both in subject matter and personnel.
URL:https://seasevents.nmsdev7.com/event/women-in-data-science-penn/
LOCATION:Jon M. Huntsman Hall\, 3730 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Conference
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240209T103000
DTEND;TZID=America/New_York:20240209T114500
DTSTAMP:20260404T034844
CREATED:20231120T162119Z
LAST-MODIFIED:20231120T162119Z
UID:10007762-1707474600-1707479100@seasevents.nmsdev7.com
SUMMARY:Spring 2024 GRASP on Robotics: Qixing Huang\, University of Texas at Austin\, "Geometric Regularizations for 3D Shape Generation"
DESCRIPTION:This is a hybrid event with in-person attendance in Wu and Chen and virtual attendance on Zoom. \nABSTRACT\nGenerative models\, which map a latent parameter space to instances in an ambient space\, enjoy various applications in 3D Vision and related domains. A standard scheme of these models is probabilistic\, which aligns the induced ambient distribution of a generative model from a prior distribution of the latent space with the empirical ambient distribution of training instances. While this paradigm has proven to be quite successful on images\, its current applications in 3D generation encounter fundamental challenges in the limited training data and generalization behavior. The key difference between image generation and shape generation is that 3D shapes possess various priors in geometry\, topology\, and physical properties. Existing probabilistic 3D generative approaches do not preserve these desired properties\, resulting in synthesized shapes with various types of distortions. In this talk\, I will discuss recent work that seeks to establish a novel geometric framework for learning shape generators. The key idea is to model various geometric\, physical\, and topological priors of 3D shapes as suitable regularization losses by developing computational tools in differential geometry and computational topology. We will discuss the applications in deformable shape generation\, latent space design\, joint shape matching\, and 3D man-made shape generation.
URL:https://seasevents.nmsdev7.com/event/spring-2024-grasp-on-robotics-qixing-huang-university-of-texas-at-austin-geometric-regularizations-for-3d-shape-generation/
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:20240209T140000
DTEND;TZID=America/New_York:20240209T150000
DTSTAMP:20260404T034844
CREATED:20240130T141449Z
LAST-MODIFIED:20240130T141449Z
UID:10007835-1707487200-1707490800@seasevents.nmsdev7.com
SUMMARY:PICS Colloquium: “Wall-models of turbulent flows via scientific multi-agent reinforcement learning”
DESCRIPTION:The predictive capabilities of turbulent flow simulations\, critical for aerodynamic design and weather prediction\, hinge on the choice of turbulence models. The abundance of data from experiments and simulations and the advent of machine learning have provided a boost to turbulence modeling efforts. However\, simulations of turbulent flows remain hindered by the inability of heuristics and supervised learning to model the near-wall dynamics. We address this challenge by introducing scientific multi-agent reinforcement learning (SciMARL) for the discovery of wall models for large-eddy simulations (LES). In SciMARL\, discretization points act also as cooperating agents that learn to supply the LES closure model. The agents self-learn using limited data and generalize to higher Reynolds numbers in reproducing key flow quantities. We test the discovered wall model to canonical flat plate boundary layers\, which shows good predictable capabilities outside the Reynolds numbers used to train the model. We will discuss extensions to this model for flows with pressure-gradient effects.
URL:https://seasevents.nmsdev7.com/event/pics-colloquium-wall-models-of-turbulent-flows-via-scientific-multi-agent-reinforcement-learning/
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:20240212T110000
DTEND;TZID=America/New_York:20240212T120000
DTSTAMP:20260404T034844
CREATED:20240201T134202Z
LAST-MODIFIED:20240201T134202Z
UID:10007836-1707735600-1707739200@seasevents.nmsdev7.com
SUMMARY:ESE Spring Seminar - "Physics-inspired Machine Learning"
DESCRIPTION:Combining physics with machine learning is a rapidly growing field of research. Thereby\, most work focuses on leveraging machine learning methods to solve problems in physics. Here\, however\, we focus on the converse\, i.e.\, physics-inspired machine learning\, which can be described as incorporating structure from physical systems into machine learning methods to obtain models with better inductive biases. More concretely\, we propose several physics-inspired deep learning architectures for sequence modelling based on nonlinear coupled oscillators\, Hamiltonian systems and multi-scale dynamical systems. The proposed architectures tackle central problems in the field of recurrent sequence modeling\, namely the vanishing and exploding gradients problem as well as the issue of insufficient expressive power. Moreover\, we discuss physics-inspired learning on graphs\, wherein the dynamics of the message-passing propagation are derived from physical systems. We further prove that these methods mitigate the over-smoothing issue\, thereby enabling the construction of deep graph neural networks (GNNs). We extensively test all proposed methods on a variety of versatile synthetic and real-world datasets\, ranging from image recognition\, speech recognition\, natural language processing (NLP)\, medical applications\, and scientific computing for sequence models\, to citation networks\, computational chemistry applications\, and networks of articles and websites for graph learning models. Finally\, we show how to leverage physics-based inductive biases of physics-inspired machine learning methods to solve problems in the physical sciences.
URL:https://seasevents.nmsdev7.com/event/ese-spring-seminar-tbd/
LOCATION:Glandt Forum\, Singh Center for Nanotechnology\, 3205 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Colloquium
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240212T130000
DTEND;TZID=America/New_York:20240212T150000
DTSTAMP:20260404T034844
CREATED:20240112T192252Z
LAST-MODIFIED:20240112T192252Z
UID:10007801-1707742800-1707750000@seasevents.nmsdev7.com
SUMMARY:BE Doctoral Dissertation Defense: "Computational imaging and multiomic biomarkers for precision medicine: characterizing heterogeneity in lung cancer" (Apurva Singh)
DESCRIPTION:The Department of Bioengineering at the University of Pennsylvania and Dr. Despina Kontos are pleased to announce the Doctoral Dissertation Defense of Apurva Singh.\n\nTitle:  “Computational imaging and multiomic biomarkers for precision medicine: characterizing heterogeneity in lung cancer”\nDate: February 12\, 2024\nTime: 1:00 PM\nLocation:  John Morgan Building\, Class of 62 auditorium\n\nZoom link\n\nThe public is welcome to attend.
URL:https://seasevents.nmsdev7.com/event/be-doctoral-dissertation-defense-computational-imaging-and-multiomic-biomarkers-for-precision-medicine-characterizing-heterogeneity-in-lung-cancer-apurva-singh/
LOCATION:Class of 62 Auditorium\, John Morgan Building\, 3620 Hamilton Walk\, Philadelphia\, PA\, 19104
CATEGORIES:Doctoral,Graduate,Student,Dissertation or Thesis Defense
ORGANIZER;CN="Bioengineering":MAILTO:be@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240213T100000
DTEND;TZID=America/New_York:20240213T113000
DTSTAMP:20260404T034844
CREATED:20240207T163149Z
LAST-MODIFIED:20240207T163149Z
UID:10007848-1707818400-1707823800@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "AI for Antibiotic Discovery"
DESCRIPTION:Computers can be programmed for superhuman pattern recognition of images and text; however\, their application in biology and medicine is still in its infancy. In this talk\, I will discuss our advances over the past half-decade\, which are accelerating discoveries in the crucial and underinvested area of antibiotic discovery. We developed the first antibiotic designed by a computer with proven efficacy in preclinical animal models\, demonstrating that machines and artificial intelligence (AI) could be used to design therapeutic molecules. Our algorithms have accelerated antibiotic discovery\, and for the first time\, we successfully mined the human proteome for antibiotics. Recently\, we expanded our proteome-mining efforts to explore the proteomes of extinct species. Using AI\, my lab discovered the first therapeutic molecules in extinct organisms\, including Neanderthals and Denisovans\, launching the field of molecular de-extinction. Collectively\, our efforts have dramatically reduced the time needed to discover preclinical antibiotic candidates from years to hours. I believe we are on the cusp of a new era in science where advances enabled by AI will help control antibiotic resistance\, infectious disease outbreaks\, and pandemics.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-ai-for-antibiotic-discovery/
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:20240213T110000
DTEND;TZID=America/New_York:20240213T120000
DTSTAMP:20260404T034844
CREATED:20240201T134437Z
LAST-MODIFIED:20240201T134437Z
UID:10007837-1707822000-1707825600@seasevents.nmsdev7.com
SUMMARY:ESE & CIS Spring Seminar - "Beyond the black box: characterizing and improving how neural networks learn"
DESCRIPTION:The predominant paradigm in deep learning practice treats neural networks as “black boxes”. This leads to economic and environmental costs as brute-force scaling remains the performance driver\, and to safety issues as robust reasoning and alignment remain challenging. My research opens up the neural network black box with mathematical and statistical analyses of how networks learn\, and yields engineering insights that improve the efficiency and transparency of these models. In this talk I will present characterizations of (1) how large language models can learn to reason with abstract symbols\, and (2) how hierarchical structure in data guides deep learning\, and will conclude with (3) new tools to distill trained neural networks into lightweight and transparent models.
URL:https://seasevents.nmsdev7.com/event/ese-spring-seminar-tbd-2/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Colloquium
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240214T120000
DTEND;TZID=America/New_York:20240214T131500
DTSTAMP:20260404T034844
CREATED:20230928T142325Z
LAST-MODIFIED:20230928T142325Z
UID:10007713-1707912000-1707916500@seasevents.nmsdev7.com
SUMMARY:ASSET Seminar: "Enforcing Right to Explanation: Algorithmic Challenges and Opportunities" (Himabindu Lakkaraju\, Harvard University)
DESCRIPTION:ABSTRACT: \nAs predictive and generative models are increasingly being deployed in various high-stakes applications in critical domains including healthcare\, law\, policy and finance\, it becomes important to ensure that relevant stakeholders understand the behaviors and outputs of these models so that they can determine if and when to intervene. To this end\, several techniques have been proposed in recent literature to explain these models. In addition\, multiple regulatory frameworks (e.g.\, GDPR\, CCPA) introduced in recent years also emphasized the importance of enforcing the key principle of “Right to Explanation” to ensure that individuals who are adversely impacted by algorithmic outcomes are provided with an actionable explanation. In this talk\, I will discuss the gaps that exist between regulations and state-of-the-art technical solutions when it comes to explainability of predictive and generative models. I will then present some of our latest research that attempts to address some of these gaps. I will conclude the talk by discussing bigger challenges that arise as we think about enforcing right to explanation in the context of large language models and other large generative models. \nZOOM LINK (if unable to attend in-person): https://upenn.zoom.us/j/94929617939
URL:https://seasevents.nmsdev7.com/event/asset-seminar-himabindu-lakkaraju-harvard-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:20240214T150000
DTEND;TZID=America/New_York:20240214T160000
DTSTAMP:20260404T034844
CREATED:20240124T151237Z
LAST-MODIFIED:20240124T151237Z
UID:10007822-1707922800-1707926400@seasevents.nmsdev7.com
SUMMARY:Spring 2024 GRASP SFI: Fei Miao\, University of Connecticut\, “Learning and Control for Safety\, Efficiency\, and Resiliency of Embodied AI”
DESCRIPTION:This will be a hybrid event with in-person attendance in Levine 307 and virtual attendance on Zoom. \nABSTRACT\nWith rapid evolution of sensing\, communication\, and computation\, integrating learning and control presents significant Embodied AI opportunities. However\, current decision-making frameworks lack comprehensive understanding of the tridirectional relationship among communication\, learning and control\, posing challenges for multi-agent systems in complex environments. In the first part of the talk\, we focus on learning and control with communication capabilities. We design an uncertainty quantification method for collaborative perception in connected autonomous vehicles (CAVs). Our findings demonstrate that communication among multiple agents can enhance object detection accuracy and reduce uncertainty. Building upon this\, we develop a safe and scalable deep multi-agent reinforcement learning (MARL) framework that leverages shared information among agents to improve system safety and efficiency. We validate the benefits of communication in MARL\, particularly in the context of CAVs in challenging mixed traffic scenarios. We incentivize agents to communicate and coordinate with a novel reward reallocation scheme based on Shapley value for MARL. Additionally\, we present our theoretical analysis of robust MARL methods under state uncertainties\, such as uncertainty quantification in the perception modules or worst-case adversarial state perturbations. In the second part of the talk\, we briefly outline our research contributions on robust MARL and data-driven robust optimization for sustainable mobility. We also highlight our research results concerning CPS security. Through our findings\, we aim to advance Embodied AI and CPS for safety\, efficiency\, and resiliency in dynamic environments.
URL:https://seasevents.nmsdev7.com/event/spring-2024-grasp-sfi-fei-miao/
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:20240214T153000
DTEND;TZID=America/New_York:20240214T163000
DTSTAMP:20260404T034844
CREATED:20240116T180348Z
LAST-MODIFIED:20240116T180348Z
UID:10007807-1707924600-1707928200@seasevents.nmsdev7.com
SUMMARY:CBE Seminar: "Systems Engineering for Addressing Critical Challenges in Viral Vector Manufacturing" (Francesco Destro\, MIT)
DESCRIPTION:Abstract\nThe demand for viral vectors is poised to soon exceed current production capacities\, driven by the surging number of clinical trials for gene and cell therapies. Unfortunately\, current manufacturing processes for viral vectors have high costs and low titers. This talk will demonstrate how process systems engineering tools can be leveraged for addressing the most critical challenges in the manufacturing process for recombinant adeno-associated virus (rAAV)\, the most widely used viral vector in commercial gene therapies. FDA recently approved the first rAAV-based gene therapies manufactured in the Sf9/baculovirus expression vector system (BEVS). Within the BEVS\, Sf9 cells produce rAAV as a result of infection with recombinant baculoviruses that carry the genetic blueprint for vector production. A mechanistic model is developed to identify the bottlenecks to full capsid formation in the intracellular pathway for rAAV production in Sf9 cells. The model indicates genetic modifications to the baculovirus vectors that can enhance the productivity of the platform. Further\, the optimal process conditions to establish continuous rAAV manufacturing in the BEVS are identified through a novel numerical method for solving systems of partial differential equations. Finally\, a powerful machine learning model is introduced for real-time prediction of rAAV titers based on single-cell biophysical signatures.
URL:https://seasevents.nmsdev7.com/event/cbe-seminar-systems-engineering-for-addressing-critical-challenges-in-viral-vector-manufacturing-francesco-destro-mit/
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:20240215T110000
DTEND;TZID=America/New_York:20240215T120000
DTSTAMP:20260404T034844
CREATED:20240201T134931Z
LAST-MODIFIED:20240201T134931Z
UID:10007838-1707994800-1707998400@seasevents.nmsdev7.com
SUMMARY:ESE Spring Seminar - "White-Box Computational Imaging: Measurements to Images to Insights"
DESCRIPTION:Computation and machine learning hold tremendous potential to improve the quality and capabilities of imaging methods used across science\, medicine\, engineering\, and art. Despite their impressive performance on benchmark datasets\, however\, deep learning methods are known to behave unpredictably on some real-world data\, which limits their trusted adoption in safety-critical domains. Accordingly\, in this talk I will describe white-box\, interpretable methods for photorealistic volumetric reconstruction that match or exceed the performance of black-box neural alternatives. I will also present recent theoretical results that guarantee correct and efficient reconstruction using our white-box approach in nonlinear computed tomography.
URL:https://seasevents.nmsdev7.com/event/ese-spring-seminar-white-box-computational-imaging-measurements-to-images-to-insights/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Colloquium
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240215T153000
DTEND;TZID=America/New_York:20240215T163000
DTSTAMP:20260404T034844
CREATED:20240108T171337Z
LAST-MODIFIED:20240108T171337Z
UID:10007793-1708011000-1708014600@seasevents.nmsdev7.com
SUMMARY:BE Seminar: "Where Do Therapeutic Antibodies Go?: A First-In-Human Journey" (Guolan Lu\, Stanford)
DESCRIPTION:Dr. Lu will introduce a fluorescence molecular imaging method to track therapeutic antibody delivery from cancer patients in vivo\, down to single cells\, through first-in-human clinical trials. She will present a new experimental and AI-powered analytical framework that integrates single-cell drug imaging with spatial omics to decipher drug-target-microenvironment in situ. This work establishes a foundational framework for studying drug pharmacology in the context of tissue biology in serious diseases including cancer\, autoimmunity\, and chronic inflammation.
URL:https://seasevents.nmsdev7.com/event/be-seminar-guolan-lu-stanford/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Student
ORGANIZER;CN="Bioengineering":MAILTO:be@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240215T153000
DTEND;TZID=America/New_York:20240215T163000
DTSTAMP:20260404T034844
CREATED:20240129T171413Z
LAST-MODIFIED:20240129T171413Z
UID:10007833-1708011000-1708014600@seasevents.nmsdev7.com
SUMMARY:CIS Seminar: "Accessible Foundation Models: Systems\, Algorithms\, and Science"
DESCRIPTION:The ever-increasing scale of foundation models\, such as ChatGPT and AlphaFold\, has revolutionized AI and science more generally. However\, increasing scale also steadily raises computational barriers\, blocking almost everyone from studying\, adapting\, or otherwise using these models for anything beyond static API queries. In this talk\, I will present research that significantly lowers these barriers for a wide range of use cases\, including inference algorithms that are used to make predictions after training\, finetuning approaches that adapt a trained model to new data\, and finally\, full training of foundation models from scratch.  For inference\, I will describe our LLM.int8() algorithm\, which showed how to enable high-precision 8-bit matrix multiplication that is both fast and memory efficient. LLM.int8() is based on the discovery and characterization of sparse outlier sub-networks that only emerge at large model scales but are crucial for effective Int8 quantization. For finetuning\, I will introduce the QLoRA algorithm\, which pushes such quantization much further to unlock finetuning of very large models on a single GPU by only updating a small set of the parameters while keeping most of the network in a new information-theoretically optimal 4-bit representation. For full training\, I will present SWARM parallelism\, which allows collaborative training of foundation models across continents on standard internet infrastructure while still being 80% as effective as the prohibitively expensive supercomputers that are currently used. Finally\, I will close by outlining my plans to make foundation models 100x more accessible\, which will be needed to maintain truly open AI-based scientific innovation as models continue to scale.
URL:https://seasevents.nmsdev7.com/event/cis-seminar-accessible-foundation-models-systems-algorithms-and-science/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="Computer and Information Science":MAILTO:cherylh@cis.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240216T103000
DTEND;TZID=America/New_York:20240216T114500
DTSTAMP:20260404T034844
CREATED:20240208T161800Z
LAST-MODIFIED:20240208T161800Z
UID:10007851-1708079400-1708083900@seasevents.nmsdev7.com
SUMMARY:Spring 2024 GRASP on Robotics: Guillaume Sartoretti\, National University of Singapore\, “Towards Learned Cooperation at Scale in Robotic Multi-Agent Systems”
DESCRIPTION:This is a hybrid event with in-person attendance in Wu and Chen and virtual attendance on Zoom. \nABSTRACT\nWith the recent advances in sensing\, actuation\, computation\, and communication\, the deployment of large numbers of robots is becoming a promising avenue to enable or speed up complex tasks in areas such as manufacturing\, last-mile delivery\, search-and-rescue\, or autonomous inspection. My group strives to push the boundaries of multi-agent scalability by understanding and eliciting emergent coordination/cooperation in multi-robot systems as well as in articulated robots (where agents are individual joints). Our work mainly relies on distributed (multi-agent) reinforcement learning\, where we focus on endowing agents with novel information and mechanisms that can help them align their decentralized policies towards team-level cooperation. In this talk\, I will first summarize my early work in independent learning\, before discussing my group’s recent advances in convention\, communication\, and context-based learning. I will discuss these techniques within a wide variety of robotic applications\, such as multi-agent path finding\, autonomous exploration/search\, task allocation\, and legged locomotion. Finally\, I will also touch on our recent incursion into the next frontier for multi-robot systems: cooperation learning for heterogeneous multi-robot teams. Throughout this journey\, I will highlight the key challenges surrounding learning representations\, policy space exploration\, and scalability of the learned policies\, and outline some of the open avenues for research in this exciting area of robotics.
URL:https://seasevents.nmsdev7.com/event/spring-2024-grasp-on-robotics-guillaume-sartoretti-national-university-of-singapore-towards-learned-cooperation-at-scale-in-robotic-multi-agent-systems/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="General Robotics%2C Automation%2C Sensing and Perception (GRASP) Lab":MAILTO:grasplab@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240219T140000
DTEND;TZID=America/New_York:20240219T160000
DTSTAMP:20260404T034844
CREATED:20240206T155231Z
LAST-MODIFIED:20240206T155231Z
UID:10007847-1708351200-1708358400@seasevents.nmsdev7.com
SUMMARY:CBE Doctoral Dissertation Defense: "Controlled Deposition of Cargo-Carrying Colloids from Dispersed Liquid to Solid Surfaces" (Paradorn Rummaneethorn)
DESCRIPTION:Abstract: \nGreat advances have been made in encapsulation of (biological) analytes at extremely high throughput via techniques such as microfluidics and/or conjugation. In parallel\, analytical techniques such as mass spectrometry have advanced to analyze biochemical components of complex mixtures with high resolutions. Both capabilities are essential for enabling biology at subcellular scales; rather than engineering a new integrated system possessing both capabilities\, the path of lower resistance may be to bridge such high-throughput encapsulation to high-resolution analytical platforms. However\, existing encapsulation techniques yield colloids in dispersions\, whereas analytical techniques require sample preparation on surfaces. \nIn this work\, we addressed two types of colloids dispersed in liquid medium – liquid droplets and solid particles. For droplets\, we employed a charge injection technique to study the reversible wetting state modulation of water droplets on hydrophobic polydimethylsiloxane (PDMS) surfaces. The system exhibits a high range of wetting modulation (from nonwetting to 20°)\, and we were able to demonstrate two-way cargo transfer between droplet and surface. For dispersed particles\, we employed two techniques to array particles in a patterned microwell array: capillary assembly and dielectrophoretic assembly. For capillary assembly\, we studied the effects of coating speed\, coating passes\, particle concentration\, surface temperature\, and presence of surfactants to optimize yield (% of occupied wells) and selectivity (% of particles inside microwells) of particle arraying. As for dielectrophoretic (DEP) assembly\, we studied the number of particles deposited as a function of peak-to-peak voltage (DEP force) and alternating current frequency (DEP polarity) to the arraying of carboxylate-conjugated polystyrene particles. \nThe physical nature of these technologies enables robustness against combinations of colloid-surface chemical characteristics\, with a tunable parameter space that empowers broad use cases involving different colloid-surface combinations. Beyond the colloid deposition use case described here\, the technologies studied here can also be applied to separations\, heterogeneous reaction engineering\, and fundamental colloid-surface studies. When colloids and surfaces come together\, possibilities are imagination-limited.
URL:https://seasevents.nmsdev7.com/event/cbe-doctoral-dissertation-defense-controlled-deposition-of-cargo-carrying-colloids-from-dispersed-liquid-to-solid-surfaces-paradorn-rummaneethorn/
LOCATION:Towne 337
CATEGORIES:Doctoral,Graduate,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:20240220T100000
DTEND;TZID=America/New_York:20240220T113000
DTSTAMP:20260404T034844
CREATED:20240208T165355Z
LAST-MODIFIED:20240208T165355Z
UID:10007852-1708423200-1708428600@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Precision Engineering in Health and Medicine via Acoustics"
DESCRIPTION:Precision engineering stands at the forefront of catalyzing transformative advancements in health and medicine. Among various precise techniques utilizing phototactic\, electric\, and magnetic mechanisms\, acoustic devices have captured heightened attention for their capability to facilitate contactless\, label-free\, and biocompatible manipulation of cells\, extracellular vesicles\, and organisms. Demonstrating proficiency in manipulating micro/nano-objects across a diverse spectrum\, acoustic platforms have evolved to facilitate cell patterning\, separation\, and sorting for single-cell analysis\, isolate extracellular vesicles for diagnosing cancer and neurodegenerative diseases\, and assemble single cells for tissue engineering. As acoustic research and technologies continue to advance\, acoustic devices emerge as a linchpin\, seamlessly bridging the realms of engineering and medicine. This integration propels the frontier of personalized medicine and advanced manufacturing\, showcasing the transformative potential of precision engineering in shaping the future of healthcare. \nIn this presentation\, I will explore the dynamic application of acoustics to advance precision engineering in health and medicine\, spanning a range from nanometer to millimeter scales. I will showcase a series of noteworthy examples\, including (1) the assembly and dynamic control of colloids\, droplets\, and living cells; (2) the precise separation of extracellular vesicles for disease diagnostics; and (3) the engineering of 3D tissues for therapeutic purposes. The distinctive attributes of acoustic platforms\, such as precision\, biocompatibility\, and versatility\, endow them with immense potential to serve as pioneering technologies\, translating innovations in mechanical engineering into advancements in materials\, biology\, and medicine. Additionally\, I will touch upon my past and ongoing endeavors\, covering topics such as sensors and actuators\, nanofabrication\, and advanced packaging\, showcasing broad applications in the fields of semiconductors\, micro/nanorobotics\, and biodevices.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-precision-engineering-in-health-and-medicine-via-acoustics/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240220T153000
DTEND;TZID=America/New_York:20240220T163000
DTSTAMP:20260404T034844
CREATED:20240209T133752Z
LAST-MODIFIED:20240209T133752Z
UID:10007853-1708443000-1708446600@seasevents.nmsdev7.com
SUMMARY:CIS Seminar: "Towards Flexible\, Scalable\, and Knowledgeable Generative Intelligence"
DESCRIPTION:From language modeling to 3D vision\, generative AI has revolutionized nearly every aspect of machine learning. In this talk\, I will examine the limitations of the foundation behind many generative AI techniques–autoregressive models. Despite their impressive successes\, these token-by-token models face various challenges\, including 1). non-flexible computation during generation\, 2). lack of rich inner structures for scalable modeling\, and 3). limited understanding of the real world. \nTo address these three issues\, I propose to strategically predict “latents” for the design of new generative models\, where latents refer to the model’s intermediate representations during the generation process. First\, I will demonstrate how integrating latents allows flexible architecture designs to enhance both efficiency and adaptability \,such as in the first non-autoregressive model for sequence generation. Next\, I will show how to use latents to incorporate useful data structures for improved model scalability\, especially in high-resolution images and videos. Moreover\, I will demonstrate how to use latents to infuse world knowledge such as 3D for tasks like consistent view synthesis. Throughout the talk\, I will cover various modalities\, including text\, images\, and 3D. Finally\, I will conclude with a discussion about the prevailing challenges and envision future paths that could lead to more flexible\, scalable \,a nd knowledgeable next-generation generative models.
URL:https://seasevents.nmsdev7.com/event/cis-seminar-towards-flexible-scalable-and-knowledgeable-generative-intelligence/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="Computer and Information Science":MAILTO:cherylh@cis.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240221T120000
DTEND;TZID=America/New_York:20240221T133000
DTSTAMP:20260404T034844
CREATED:20240125T161410Z
LAST-MODIFIED:20240125T161410Z
UID:10007827-1708516800-1708522200@seasevents.nmsdev7.com
SUMMARY:ASSET Seminar: “Mathematical Foundations for Physical Agents” (Max Simchowitz\, Massachusetts Institute of Technology\, CSAIL)
DESCRIPTION:ABSTRACT: \nFrom robotics to autonomous vehicles\, machine learning agents deployed in the physical world (“physical agents”) promise to revolutionize endeavors ranging from manufacturing to agriculture to domestic labor. In this talk\, we will develop mathematical foundations\, from the ground up\, for how to carry out this vision. We will begin our investigation by examining linear dynamical systems\, a simple and fundamental model of the interaction between a physical agent and its environment. We prove mathematically that simple exploration attains optimal performance for some of both the simplest and the most complex learning problems in this class. The above finding\, while powerful\, strongly motivates moving past linear dynamics as a mathematical testbed for understanding learning with physical agents. \nHence\, we turn to providing mathematical guarantees for a setting of real-world importance that does not fit the linear mold: behavior cloning. Behavior cloning — teaching a robot to imitate from example demonstrations — lies at the heart of many of today’s most promising robot learning endeavors due to its intuitive data collection and simplicity. Though it can work incredibly well\, we still do not have a clear understanding of what circumstances ensure its success. Bringing together the flexibility of generative models with key intuitions arising from the study of linear control\,  we introduce a framework for behavior cloning that enables an agent to imitate nearly arbitrary behavior with provable guarantees\, even when the dynamics governing the agent and environments interaction are nonlinear. We conclude by outlining ongoing work and future steps towards building out the mathematical and conceptual tooling for understanding the next steps towards general\, capable and flexible physical agents. \n  \nZOOM LINK (if unable to attend in-person): https://upenn.zoom.us/j/99732583896 \n 
URL:https://seasevents.nmsdev7.com/event/asset-seminar-mathematical-foundations-for-physical-agents-max-simchowitz-massachusetts-institute-of-technology-csail/
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:20240221T150000
DTEND;TZID=America/New_York:20240221T163000
DTSTAMP:20260404T034844
CREATED:20240214T200630Z
LAST-MODIFIED:20240214T200630Z
UID:10007860-1708527600-1708533000@seasevents.nmsdev7.com
SUMMARY:Spring 2024 GRASP SFI: Erik Bekkers\, University of Amsterdam\, "Fast\, Expressive SE(n) Equivariant Networks through Weight-Sharing in Position-Orientation Space"
DESCRIPTION:This will be a hybrid event with in-person attendance in Levine 307 and virtual attendance on Zoom. This week’s speaker will be virtual. There will be an extended Q&A/discussion after the seminar from 4:00 PM to 4:30 PM.  \nABSTRACT\nBased on the theory of homogeneous spaces we derive \textit{geometrically optimal edge attributes} to be used within the flexible message passing framework. We formalize the notion of weight sharing in convolutional networks as the sharing of message functions over point-pairs that should be treated equally. We define equivalence classes of point-pairs that are identical up to a transformation in the group and derive attributes that uniquely identify these classes. Weight sharing is then obtained by conditioning message functions on these attributes. As an application of the theory\, we develop an efficient equivariant group convolutional network for processing 3D point clouds. The theory of homogeneous spaces tells us how to do group convolutions with feature maps over the homogeneous space of positions ℝ3\, position and orientations ℝ3×S2\, and the group SE(3) itself. Among these\, ℝ3×S2 is an optimal choice due to the ability to represent directional information\, which ℝ3 methods cannot\, and it significantly enhances computational efficiency compared to indexing features on the full SE(3) group. We empirically support this claim by reaching state-of-the-art results — in accuracy and speed — on three different benchmarks: interatomic potential energy prediction\, trajectory forecasting in N-body systems\, and generating molecules via equivariant diffusion models.
URL:https://seasevents.nmsdev7.com/event/spring-2024-grasp-sfi-erik-bekkers/
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:20240221T153000
DTEND;TZID=America/New_York:20240221T163000
DTSTAMP:20260404T034844
CREATED:20240116T180710Z
LAST-MODIFIED:20240116T180710Z
UID:10007808-1708529400-1708533000@seasevents.nmsdev7.com
SUMMARY:CBE Seminar: "Minimally Invasive Neuroelectronics" (Anqi Zhang\, Stanford)
DESCRIPTION:Abstract\nNeuroelectronic interfaces have enabled significant advances in both fundamental neuroscience research and the treatment of neurological disorders. However\, current neuroelectronic devices have a clear trade-off between invasiveness and spatial resolution\, and are unable to achieve seamless integration into the nervous system with cell-type specificity. In this talk\, I will first introduce an ultra-small and flexible endovascular neural probe that can be implanted into sub-100-micron scale blood vessels in the brains of rodents without damaging the brain or vasculature. Second\, I will describe a biochemically functionalized electronic probe that enables cell type- and neuron subtype-specific targeting and recording in the brain. Third\, I will present a bottom-up approach for constructing neural interfaces from the cell surface\, where neurons are genetically programmed to express membrane-localized enzymes that catalyze in situ assembly of functional materials. Finally\, I will discuss future advances toward clinical translation of minimally invasive neuroelectronic interfaces capable of long-term monitoring and treatment of neurological disorders.
URL:https://seasevents.nmsdev7.com/event/cbe-seminar-minimally-invasive-neuroelectronics-anqi-zhang-stanford/
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:20240222T110000
DTEND;TZID=America/New_York:20240222T120000
DTSTAMP:20260404T034844
CREATED:20240201T135527Z
LAST-MODIFIED:20240201T135527Z
UID:10007839-1708599600-1708603200@seasevents.nmsdev7.com
SUMMARY:ESE & BE Spring Seminar - "Ultra-high-throughput computational imaging: towards a trillion voxels per second"
DESCRIPTION:Traditional biomedical imaging techniques face throughput bottlenecks that limit our ability to study complex dynamic samples like cells\, organoids\, tissues\, and organisms. In particular\, hardware-only systems have inherent physical limitations preventing the simultaneous improvement of resolution\, field of view\, and frame rate. In this seminar\, I propose that large-scale\, machine learning-accelerated computational imaging will be the key to overcoming these throughput bottlenecks. I demonstrate a variety of examples from my research\, ranging from resolution-enhanced\, speckle-free tissue imaging with optical coherence refraction tomography\, to camera array-based gigapixel microscopy and 4D fluorescence tomography of freely-behaving zebrafish and fruit flies. Critical to the computational scalability is the integration of physics-supervised deep learning into my reconstruction algorithms. This approach is inherently robust to generalization errors and does not require labeled data\, as it uses the differentiable physical model as the only supervision mechanism. Combined with scalable hardware designs\, these high-performance computational imaging systems will continue the trend of my research towards ultra-high imaging throughputs\, even approaching 1 trillion voxels per second\, which will accelerate scientific discovery\, big data generation\, and tool development across a broad range of biomedical applications.
URL:https://seasevents.nmsdev7.com/event/ese-spring-seminar-tbd-3/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Colloquium
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240222T153000
DTEND;TZID=America/New_York:20240222T163000
DTSTAMP:20260404T034844
CREATED:20240108T171614Z
LAST-MODIFIED:20240108T171614Z
UID:10007794-1708615800-1708619400@seasevents.nmsdev7.com
SUMMARY:BE Seminar: "Endothelial cells and the promise of regeneration on demand" (Brisa Palikuqi\, UCSF)
DESCRIPTION:In this seminar\, I explore the pivotal role of tissue stem cells and their microenvironment\, known as the niche\, in regeneration. With a focus on the endothelial cell niche\, my work introduces an innovative in vitro vascularized perfusable model tailored for human tissue explants and organoids. My findings also highlight the crucial contribution of paracrine factors derived from lymphatic endothelial cells in facilitating intestinal regeneration and repair in vivo. I will also outline future research directions for my independent laboratory\, where I aim to unravel the complexities of endothelial cell interactions and paracrine signaling in tissue regeneration. The overarching goal is to leverage this knowledge for engineering vascularized and physiologically relevant organoids\, advancing our understanding of regenerative medicine and stem cell biology.
URL:https://seasevents.nmsdev7.com/event/be-seminar-brisa-palikuqi-ucsf/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Bioengineering":MAILTO:be@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240223T103000
DTEND;TZID=America/New_York:20240223T114500
DTSTAMP:20260404T034844
CREATED:20231212T190328Z
LAST-MODIFIED:20231212T190328Z
UID:10007782-1708684200-1708688700@seasevents.nmsdev7.com
SUMMARY:Spring 2024 GRASP on Robotics: Pietro Valdastri\, University of Leeds\, "Magnetic Surgical Robots: A “Fantastic Voyage” deep inside the human body"
DESCRIPTION:This is a hybrid event with in-person attendance in Wu and Chen and virtual attendance on Zoom. \nABSTRACT\nMagnetic fields offer the possibility of manipulating objects from a distance and are ideal for medical applications\, as they penetrate human tissue without inflicting any harm on the patient. Magnetic fields can be harnessed to actuate surgical robots\, enhancing the capabilities of surgeons in reaching deep into the human anatomy through complex winding pathways\, thus providing minimally invasive access to organs that are out of reach with current technologies. In this talk\, we will explore various robotic architectures based on magnetic control\, specifically designed for lifesaving clinical applications. These architectures include a magnetic flexible endoscope for painless colonoscopy\, soft magnetic tentacles personalized for reaching peripheral areas of the lung and navigating the pancreatic duct\, and magnetic “fusilli” robots designed for collaborative bimanual tasks in a confined workspace. We will also discuss enabling technologies\, intelligent control\, potential levels of computer assistance\, the path to first-in-human trials\, and highlight the future challenges associated with this ongoing Fantastic Voyage.
URL:https://seasevents.nmsdev7.com/event/spring-2024-grasp-on-robotics-pietro-valdastri-university-of-leeds-magnetic-surgical-robots-a-fantastic-voyage-deep-inside-the-human-body/
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:20240223T140000
DTEND;TZID=America/New_York:20240223T150000
DTSTAMP:20260404T034844
CREATED:20240212T153325Z
LAST-MODIFIED:20240212T153325Z
UID:10007856-1708696800-1708700400@seasevents.nmsdev7.com
SUMMARY:PICS Colloquium: "Genetic testing and adverse selection"
DESCRIPTION:Technology is dramatically driving down the cost of sequencing genetic data and increasing the quality of predictions made with this data. A standard concern is that these predictions could impair the functioning of insurance markets\, either because insurers would abuse genetic information or because of adverse selection. \nWe make three contributions. First\, we develop a methodology to measure how much selection would be created by genetic information in the market for an insurance product. Second\, we extend the methodology to measure the amount of selection with future prediction technology\, by combining information from heritability studies and empirical regularities from genetic epidemiology. Third\, we apply the methodology to critical illness insurance using data from the UK biobank\, including genetic information and National Health Service records for 800\,000 UK citizens. \n The main substantive finding is that selection would make many of these markets untenable unless insurers are allowed to underwrite based on genetic information. However\, there are important differences in how the results vary by the type of illness and market.
URL:https://seasevents.nmsdev7.com/event/pics-colloquium-genetic-testing-and-adverse-selection/
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:20240226T140000
DTEND;TZID=America/New_York:20240226T160000
DTSTAMP:20260404T034844
CREATED:20240219T164613Z
LAST-MODIFIED:20240219T164613Z
UID:10007865-1708956000-1708963200@seasevents.nmsdev7.com
SUMMARY:CBE Doctoral Dissertation Defense: "Synthesis of Enzyme-Powered Motors using Microfluidics" (Jessica O'Callaghan)
DESCRIPTION:Abstract: \n\n\n\nThis thesis addresses the fundamental questions surrounding the design and functional capabilities of enzyme-powered micromotors synthesized using microfluidic techniques. The research focuses on the development of these motors\, made from artificial cell (protocell) scaffolds\, and which seek to replicate the motion behavior of biological cells\, investigating their propulsion mechanisms\, motion directionality\, and collective behavior. The thesis first describes the development of a microfluidic platform for the synthesis of polymer and polymer-protein-based protocells. This platform enables precise control over the size\, composition\, and functional properties of the protocells\, demonstrating the versatility of microfluidics in the fabrication of complex microstructures. Next\, a novel approach to creating urease-powered micromotors using double emulsion-templated microcapsules is presented. The study explores how surfactants used in the emulsion assembly step that integrate themselves into the microcapsule structure can reliably lead to autonomous motion\, providing insights into the design principles that govern the efficiency of enzyme-powered motors prepared by droplet microfluidics. The thesis next investigates the directed motion of urease-powered motors in gradients of urea\, revealing how these motors can be directed away from high concentrations of substrate\, providing insights into how to control their motion in complex fluids. Finally\, the thesis explores interactions between enzyme-powered (active) and passive particles\, demonstrating how active particles influence the motion of passive ones. The findings of this dissertation significantly advance our understanding of enzyme-powered motors\, offering new strategies for their design and application. The use of microfluidic technology for the synthesis of these motors opens up new possibilities for the precise control of their properties\, paving the way for their use in a wide range of scientific and technological applications.
URL:https://seasevents.nmsdev7.com/event/cbe-doctoral-dissertation-defense-synthesis-of-enzyme-powered-motors-using-microfluidics-jessica-ocallaghan/
LOCATION:Levine 307\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Doctoral,Graduate,Dissertation or Thesis Defense
ORGANIZER;CN="Chemical and Biomolecular Engineering":MAILTO:cbemail@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240226T180000
DTEND;TZID=America/New_York:20240226T190000
DTSTAMP:20260404T034844
CREATED:20240223T205636Z
LAST-MODIFIED:20240223T205636Z
UID:10007872-1708970400-1708974000@seasevents.nmsdev7.com
SUMMARY:PRECISE Seminar: Image Curation for AI in Ophthalmology
DESCRIPTION:This presentation will explore the critical process of curating medical imaging data for AI algorithm development in ophthalmology\, highlighting the challenges and current limitations in data curation. It will discuss benchmark datasets\, reference standards for FDA validation\, and innovative strategies to enhance data availability. Attendees will gain insights into best practices and future directions in image curation for advancing AI applications in eye care.
URL:https://seasevents.nmsdev7.com/event/precise-seminar-image-curation-for-ai-in-ophthalmology/
LOCATION:https://upenn.zoom.us/j/93008201901
ORGANIZER;CN="PRECISE":MAILTO:wng@cis.upenn.edu
END:VEVENT
END:VCALENDAR