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:20240205T153000
DTEND;TZID=America/New_York:20240205T163000
DTSTAMP:20260403T203930
CREATED:20231219T204537Z
LAST-MODIFIED:20231219T204537Z
UID:10007786-1707147000-1707150600@seasevents.nmsdev7.com
SUMMARY:Penn Engineering 2023-24 Pender Award Lecture: Shafi Goldwasser
DESCRIPTION:
URL:https://seasevents.nmsdev7.com/event/penn-engineering-2023-24-pender-award-lecture-shafi-goldwasser/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar,Distinguished Lecture,Faculty
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240206T100000
DTEND;TZID=America/New_York:20240206T113000
DTSTAMP:20260403T203930
CREATED:20240116T230557Z
LAST-MODIFIED:20240116T230557Z
UID:10007816-1707213600-1707219000@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: “Towards the Discovery of Trustworthy and Interpretable ML-enabled Constitutive Laws for Solids from Low- and Limited-data”
DESCRIPTION:Machine learning techniques are gearing up to play a significant role in the field of computational solid mechanics and multiphysics\, enabling the integration of experimental data and physical constraints towards data-driven constitutive laws\, acceleration of computational techniques for multi-scale modeling\, and new paradigms for the solution of forward and inverse problems\, to name a few. This talk will cover recent advancements in the area of ML-enabled constitutive modeling I) A physics-informed data-driven constitutive modeling approach for isotropic and anisotropic hyperelastic materials is developed using tensor representation theorems. The trained surrogates (using GPR and NNs) are able to respect physical principles such as material frame indifference\, material symmetry\, and the local balance of angular momentum. Overall\, the presented approach is tested on synthetic data from isotropic and anisotropic constitutive laws and shows surprising accuracy even far beyond the limits of the training domain\, indicating that the resulting surrogates can efficiently generalize as they incorporate knowledge about the underlying physics. Additionally it is shown that the inherent material symmetries can be discovered directly from data. II) Finally\, we proceed to tackle elastoplasticity in a modular framework. Employing convexity for the yield functions we recover textrure-parametrized yield functions using input convex (IC)NNs and propose a hybrid model-data-driven framework to recover yield functions with tension compression asymmetries in the low data regime. Additionally by employing thermodynamic requirements in an NN-based framework we learn hardening laws from limited experimental data. III) The extension of the above approaches for both hyperelasticity and elastoplasticity to enable interpretable discovery of constitutive models without the use of model libraries will be discussed. IV) Finally ML-hybrid approaches for the solution of PDEs will be introduced\, focusing on applications in solid and structural mechanics towards high dimensional optimization problems requiring robust and trustworthy solvers.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-towards-the-discovery-of-trustworthy-and-interpretable-ml-enabled-constitutive-laws-for-solids-from-low-and-limited-data/
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:20240206T153000
DTEND;TZID=America/New_York:20240206T163000
DTSTAMP:20260403T203930
CREATED:20240124T182229Z
LAST-MODIFIED:20240124T182229Z
UID:10007824-1707233400-1707237000@seasevents.nmsdev7.com
SUMMARY:CIS Seminar: " How Algorithms Can Support Deliberative Democracy"
DESCRIPTION:Academics and political practitioners around the world are experimenting with a class of democratic innovations called deliberative mini-publics (DMs). In a DM\, a panel of constituents convenes to deliberate about specific issues and make policy recommendations to traditional political decision-makers (e.g.\, legislators). Nearly all DMs rely on sortition – random selection – to choose the panelists. Sortition is often thought of as a simple lottery that chooses all constituents with equal probability. In practice\, however\, simple random selection fails to yield representative panels due to selection bias in who accepts invitations to participate. Many practitioners of DMs therefore sacrifice the pure equality embodied by a simple lottery\, instead imposing quotas on socially salient groups and then “randomizing” within those constraints. \nEngineering this randomization within user-specified quotas turns out to be technically demanding. My talk covers our algorithmic solution to this problem: a framework of optimization-based algorithms which\, subject to such quotas\, ensure individuals’ selection probabilities are as equal as possible\, as measured by any convex function measuring equality (Fair Algorithms for Selecting Citizens’ Assemblies\, Nature\, ‘21). \nAfter presenting our approach to this technical problem\, I discuss my follow-up work demonstrating how the notion of equality we choose to optimize within this framework has implications for normative goals like fairness\, transparency\, and resistance to subversion. This includes a discussion of Leximin\, the original instantiation of our framework\, which has been adopted widely in practice and is available for public use at Panelot.org.
URL:https://seasevents.nmsdev7.com/event/cis-seminar-how-algorithms-can-support-deliberative-democracy/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240207T103000
DTEND;TZID=America/New_York:20240207T113000
DTSTAMP:20260403T203930
CREATED:20240202T144841Z
LAST-MODIFIED:20240202T144841Z
UID:10007846-1707301800-1707305400@seasevents.nmsdev7.com
SUMMARY:Spring 2024 GRASP SFI: Andrew Owens\, University of Michigan\, "Multimodal Learning from the Bottom Up"
DESCRIPTION:This will be a hybrid event with in-person attendance in Levine 307 and virtual attendance on Zoom. \nABSTRACT\nToday’s machine perception systems rely extensively on supervision provided by humans\, such as natural language. I will talk about our efforts to make systems that\, instead\, learn from two ubiquitous sources of unlabeled sensory data: visual motion and cross-modal associations between senses. First\, I will discuss our work on creating unified self-supervised motion analysis methods that can address both object tracking and optical flow tasks. I will then discuss how these same techniques can be applied to localizing sound sources in video\, and how tactile sensing data can be used to train multimodal  visual-tactile models. Finally\, I will talk about our recent work on subverting visual perception systems\, by creating “multi-view” optical illusions: images that change their appearance under a transformation\, such as a flip or rotation.
URL:https://seasevents.nmsdev7.com/event/spring-2024-grasp-sfi-andrew-owens/
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:20240207T120000
DTEND;TZID=America/New_York:20240207T133000
DTSTAMP:20260403T203930
CREATED:20240125T211658Z
LAST-MODIFIED:20240125T211658Z
UID:10007828-1707307200-1707312600@seasevents.nmsdev7.com
SUMMARY:ASSET Seminar: "Paths to AI Accountability" (Sarah Cen\, Massachusetts Institute of Technology)
DESCRIPTION:ABSTRACT: \nIn the past decade\, we have begun grappling with difficult questions related to the rise of AI\, including: What rights do individuals have in the age of AI? When should we regulate AI and when should we abstain? What degree of transparency is needed to monitor AI systems? These questions are all concerned with AI accountability: determining who owes responsibility and to whom in the age of AI. In this talk\, I will discuss the two main components of AI accountability\, then illustrate them through a case study on social media. Within the context of social media\, I will focus on how social media platforms filter (or curate) the content that users see. I will review several methods for auditing social media\, drawing from concepts and tools in hypothesis testing\, causal inference\, and LLMs. \n  \nZOOM LINK (if unable to attend in-person): https://upenn.zoom.us/j/96315573573 \n 
URL:https://seasevents.nmsdev7.com/event/asset-seminar-sarah-cen-massachusetts-institute-of-technology/
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:20240207T153000
DTEND;TZID=America/New_York:20240207T163000
DTSTAMP:20260403T203930
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:20260403T203930
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:20260403T203930
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:20260403T203930
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:20260403T203930
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:20260403T203930
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:20260403T203930
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
END:VCALENDAR