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DTSTART;TZID=America/New_York:20240402T100000
DTEND;TZID=America/New_York:20240402T113000
DTSTAMP:20260403T134731
CREATED:20240322T175702Z
LAST-MODIFIED:20240322T175702Z
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SUMMARY:Tedori-Callinan Distinguished Lecture: "Origami"
DESCRIPTION:We study the geometric mechanics of origami assemblages and investigate how geometry affects behavior and properties. Understanding origami from a structural standpoint allows for conceptualizing and designing feasible applications across scales and disciplines of engineering. We review the basic mathematical rules of origami and use 3D-printed origami legos to illustrate those concepts. We then present a reduced-order-model\, which consists of an improved bar-and-hinge model\, to simulate origami assemblages. We explore the stiffness of tubular origami and kirigami structures based on the Miura-ori folding pattern. A unique orientation for zipper coupling of rigidly foldable origami tubes substantially increases stiffness in higher order modes and permits only one flexible motion through which the structure can deploy. We couple compatible origami tubes into a variety of cellular assemblages that enhances mechanical characteristics and geometric versatility\, leading to the design of structures and configurational metamaterials that can be deployed\, stiffened\, and tuned. We have designed\, fabricated (using DLW\, direct laser writing)\, and tested (in-situ SEM) this metamaterial at the micron-scale. This resulted not only in the smallest scale origami assembly\, but also in a metamaterial with intriguing mechanical properties\, such as anisotropy\, reversible auxeticity\, and large degree of shape recoverability. The presentation concludes with a vision toward the field of origami engineering\, including origami robots with distributed actuation\, allowing for on-the-fly programmability\, and other interdisciplinary applications.
URL:https://seasevents.nmsdev7.com/event/tedori-callinan-distinguished-lecture-origami/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Distinguished Lecture
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240402T110000
DTEND;TZID=America/New_York:20240402T120000
DTSTAMP:20260403T134731
CREATED:20240223T172250Z
LAST-MODIFIED:20240223T172250Z
UID:10007871-1712055600-1712059200@seasevents.nmsdev7.com
SUMMARY:ESE Spring Seminar - "Developing next-generation wireless\, bioelectronic cellular medicine"
DESCRIPTION:Recent advances in engineering science have led to new classes of medical devices with emergent mechanical\, electrical\, and thermal properties that offer new opportunities for interfacing with living cells. I will discuss conceptual advances in microfabrication\, device physics\, power transfer and microscale transport phenomena that enable novel biosensors and cell delivery systems\, with an emphasis on two recent examples from my work: (i) Soft\, skin-interfacing wearable flow sensors for novel neurosurgical diagnostics; (ii) Battery-free bioelectronic systems for “living drug factories” that combine inorganic device elements with living cells for long-term\, functional cures for a range of diseases with an emphasis on oxygenation strategies and immune-isolation. I will illustrate the utility of the latter platform with examples of specific cell and disease models. Finally\, I will present a vision for how these types of technologies could lead to both fundamental scientific discoveries and next generation bioelectronic cell therapy platforms for the treatment and sensing of chronic disease.
URL:https://seasevents.nmsdev7.com/event/ese-spring-seminar-developing-next-generation-wireless-bioelectronic-cellular-medicine/
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:20240402T153000
DTEND;TZID=America/New_York:20240402T163000
DTSTAMP:20260403T134731
CREATED:20240220T194100Z
LAST-MODIFIED:20240220T194100Z
UID:10007869-1712071800-1712075400@seasevents.nmsdev7.com
SUMMARY:CIS/IDEAS Seminar: "Flow matching and optimal transport with applications to cell trajectories and protein design"
DESCRIPTION:ABSTRACT: \nFlow matching models learn a (possibly stochastic) mapping between source and target distributions. Common paradigms include diffusion models\, score matching models\, and continuous normalizing flows. In this talk I will first present methods for improved training of flow matching models using ideas from optimal transport. I will then show how these improved methods can be applied to the tasks of (1) modeling  cell dynamics\, which allow us to better understand disease programs – leading to a new potential therapeutic pathway for triple-negative breast cancer and (2) generative protein design\, with applications to biologic drug discovery. \n  \nZOOM LINK: https://upenn.zoom.us/j/94916924500
URL:https://seasevents.nmsdev7.com/event/cis-ideas-seminar-alexander-tong-mila-quebec-ai-institute-universite-de-montreal/
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:20240403T120000
DTEND;TZID=America/New_York:20240403T133000
DTSTAMP:20260403T134731
CREATED:20240220T194210Z
LAST-MODIFIED:20240220T194210Z
UID:10007870-1712145600-1712151000@seasevents.nmsdev7.com
SUMMARY:ASSET Seminar: "Building a Foundation for Trustworthy Machine Learning" (Elan Rosenfeld\, Carnegie Mellon University)
DESCRIPTION:ABSTRACT: \n\n\nArtificial Intelligence is being increasingly relied on in safety-critical domains. But the predictive models underlying these systems are notoriously brittle\, and trustworthy deployment remains a significant challenge. In this talk\, I give an overview of my work towards a rigorous foundation for robust machine learning (ML).\n\n\nUsing a case study of invariant prediction\, we first highlight the importance of formally specifying the space of adverse events we’d like to handle at deployment time. This provides a mathematical framework for analyzing\, comparing\, and improving the robustness of ML algorithms. Then\, we explore how careful experimental probing of these methods’ failures leads to a deeper understanding of the underlying causes\, and how these insights can inform the design of new methods with more reliable real-world behavior. We conclude with a brief summary of other past and ongoing works towards provably secure ML\, including a scalable framework which enables certified robustness to adversarial train- and test-time attacks. \n\nZOOM LINK (if unable to attend in-person): https://upenn.zoom.us/j/95678270617
URL:https://seasevents.nmsdev7.com/event/asset-seminar-elan-rosenfeld-carnegie-mellon-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:20240403T150000
DTEND;TZID=America/New_York:20240403T160000
DTSTAMP:20260403T134731
CREATED:20240308T161940Z
LAST-MODIFIED:20240308T161940Z
UID:10007893-1712156400-1712160000@seasevents.nmsdev7.com
SUMMARY:Spring 2024 GRASP SFI: Madhur Behl\, University of Virginia\, "Bringing AI Up To Speed"
DESCRIPTION:This will be a hybrid event with in-person attendance in Levine 307 and virtual attendance on Zoom. \nABSTRACT\nWhy has autonomous driving\, a task demanding significant intelligence\, not met the high expectations set by many? Which hurdles have turned out to be more formidable than expected\, and how can we refine our testing methodologies for autonomous vehicles (AVs) to address these problems more efficiently? In this talk\, I will discuss the targeted research initiatives we have engaged in to overcome these challenges. Leveraging more than a decade of experience from high-speed autonomous racing\, particularly with the full-scale Cavalier Autonomous Racing Indy car and the F1Tenth platform\, I will demonstrate how racing at speeds exceeding 150 mph (240 kmph) while in close quarters with other vehicles presents unique robotics challenges and offers deep insights into the limits of perception\, multi-agent prediction and planning\, dynamics modeling\, and control. I will recount our journey from algorithms to accelerations\, the rigorous engineering required to develop an autonomous racing car from scratch\, and how this fast moving field is becoming accessible to researchers and professionals alike. Despite progress\, autonomous racing has yet to match expert racing drivers’ skills or navigate the chaos of dense\, multi-car racing in the real world; indicating that several more laps are needed on our journey towards artificial general “driving” intelligence.
URL:https://seasevents.nmsdev7.com/event/spring-2024-grasp-sfi-madhur-behl/
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:20240403T153000
DTEND;TZID=America/New_York:20240403T163000
DTSTAMP:20260403T134731
CREATED:20240116T181810Z
LAST-MODIFIED:20240116T181810Z
UID:10007810-1712158200-1712161800@seasevents.nmsdev7.com
SUMMARY:CBE Seminar: "Dynamics and Reactivity of Supported Catalysts in the Subnanometer Regime" (Ayman Karim\, Virginia Tech)
DESCRIPTION:Abstract\nSupported noble metal catalysts are extensively used in industry and their catalytic performance is strongly affected by particle size and shape. In the last decade\, supported single atoms and subnanometer clusters have attracted a lot of interest since they maximize the metal utilization and have shown extraordinary catalytic properties for many reactions. In this talk\, I will present my group’s work using detailed kinetics\, in-situ and in-operando infrared and x-ray absorption spectroscopies to understand the dynamics\, both structural and ligand\, of supported metal single atoms and small clusters which result in complex reaction mechanisms. The differences between supported single atoms\, small clusters and extended metal surfaces as well as their potential advantages and limitations will be discussed.
URL:https://seasevents.nmsdev7.com/event/cbe-seminar-dynamics-and-reactivity-of-supported-catalysts-in-the-subnanometer-regime-ayman-karim-virginia-tech/
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:20240403T153000
DTEND;TZID=America/New_York:20240403T170000
DTSTAMP:20260403T134731
CREATED:20240329T174454Z
LAST-MODIFIED:20240329T174454Z
UID:10007922-1712158200-1712163600@seasevents.nmsdev7.com
SUMMARY:Condensed and Living Matter Seminar Series - "Optical Neural Networks for Faster AI and Superresolution Imaging"
DESCRIPTION:Although machine intelligence is taking over the world\, its current digital electronic platform is very inefficient in terms of energy consumption. Switching to analogue computation\, which function more like human brains than digital computers\, will allow enhancing the energy efficiency by several orders of magnitude. Optics presents a particularly promising platform for analogue AI; however\, significant challenges – particularly in the domain of neural network training – must be overcome before it can compete with its digital counterpart. A likely upcoming range of applications of optical neuron networks is in computer vision\, as they will allow eliminating the bottleneck associated with back-and forth conversion of data between optical and electronic formats. A further benefit of optical processing is enhancing the quality of imaging. For example\, it allows reaching the quantum frontier of imaging resolution beyond Rayleigh’s diffractive limit which applies to most of the modern classical imaging technology.
URL:https://seasevents.nmsdev7.com/event/condensed-and-living-matter-ese-seminar-optical-neural-networks-for-faster-ai-and-superresolution-imaging/
LOCATION:DRL A8\, 209 S. 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:20240404T103000
DTEND;TZID=America/New_York:20240404T120000
DTSTAMP:20260403T134731
CREATED:20240216T140134Z
LAST-MODIFIED:20240216T140134Z
UID:10007862-1712226600-1712232000@seasevents.nmsdev7.com
SUMMARY:MSE Seminar: "The Surface Dynamics of the Initial Stages of CU Oxidation"
DESCRIPTION:Much is known about oxygen interaction with metal surfaces and about the macroscopic growth of thermodynamically stable oxides. At present\, however\, the transient stages of oxidation – from nucleation of the metal oxide to formation of the thermodynamically stable oxide – represent a scientifically challenging and technologically important terra incognito. These issues can only be understood through a detailed study of the relevant microscopic processes at the nanoscale in situ. We have previously demonstrated via in situ transmission electron microscopy (TEM) that the formation of epitaxial Cu2O islands during the transient oxidation of Cu(100)\, (110)\, and (111) films bear a striking resemblance to heteroepitaxy\, where the initial stages of growth are dominated by oxygen surface diffusion and strain impacts the evolution of the oxide morphologies. To deepen our understanding of the atomic-scale dynamic processes of Cu2O island formation on Cu during oxidation in situ\, we are presently using correlated in situ environmental high-resolution TEM (ETEM) and atomistic simulations. As an example of this approach\, preferential monolayer-by-monolayer growth along Cu2O (110) planes\, instead of along Cu2O (100) planes\, was noted. Correlated Density Functional Theory (DFT) simulations on the surface and diffusion energies during Cu2O growth on various Cu2O surface orientations and terminations were carried out. Our DFT results show that the monolayer formation of Cu2O along Cu2O(110) was both thermodynamically and kinetically preferred over that of Cu2O(100) during Cu2O growth\, which explains the observed phenomenon.
URL:https://seasevents.nmsdev7.com/event/mse-seminar-the-surface-dynamics-of-the-initial-stages-of-cu-oxidation/
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:20240404T110000
DTEND;TZID=America/New_York:20240404T120000
DTSTAMP:20260403T134731
CREATED:20240214T212552Z
LAST-MODIFIED:20240214T212552Z
UID:10007861-1712228400-1712232000@seasevents.nmsdev7.com
SUMMARY:ESE Spring Seminar - "Neural Mechatronics and Mixed Reality for Patient Care"
DESCRIPTION:The rich set of mechanoreceptors found in human skin offers a versatile engineering interface for transmitting information and eliciting perceptions\, potentially serving a broad range of applications in patient care and other important industries. Targeted multisensory engagement of these afferent units\, however\, faces persistent challenges\, especially for wearable\, programmable systems that need to operate adaptively across the body. I present a miniaturized electromechanical structure that\, when combined with skin as an elastic\, energy storing element\, supports bistable\, self-sensing modes of deformation. Targeting specific classes of mechanoreceptors as the basis for distinct\, programmed sensory responses\, this haptic unit can deliver both dynamic and static stimuli\, directed as either normal or shear forces. Systematic experimental and theoretical studies establish foundational principles and practical criteria for low-energy operation across natural anatomical variations in the mechanical properties of human skin. A wireless\, skin-conformable haptic interface\, integrating an array of these bistable transducers\, serves as a high-density channel capable of rendering input from smartphone-based 3D scanning and inertial sensors. Demonstrations of this system include sensory substitution designed to improve the quality of life for patients in clinical trials of stroke and spinal cord injury.
URL:https://seasevents.nmsdev7.com/event/ese-spring-seminar-neural-mechatronics-and-mixed-reality-for-patient-care/
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:20240404T153000
DTEND;TZID=America/New_York:20240404T163000
DTSTAMP:20260403T134731
CREATED:20240311T173357Z
LAST-MODIFIED:20240311T173357Z
UID:10007896-1712244600-1712248200@seasevents.nmsdev7.com
SUMMARY:BE Seminar: "A Task-Optimized Approach to Systems Neuroscience" (Aran Nayebi\, MIT)
DESCRIPTION:Note that this seminar will be held in Wu & Chen Auditorium (Levine 101).\n\nHumans and animals exhibit a range of interesting behaviors in complex environments\, and it is unclear how the brain reformats dense sensory information to enable these behaviors. To gain traction on this problem\, new recording paradigms now facilitate the ability to record and manipulate hundreds to thousands of neurons in awake\, behaving animals. Consequently\, a pressing need arises to distill these data into interpretable insights about how neural circuits give rise to intelligent behaviors.\n\nTo engage with these issues\, I take a computational approach\, known as “task-optimized modeling”\, that leverages recent advancements in artificial intelligence (AI) to express hypotheses for the evolutionary constraints of neural circuits. These constraints guide the iterative optimization of artificial neural networks to achieve a specific behavior (“task”). By carefully analyzing the factors that contribute to model fidelity in predicting large-scale neural response patterns\, we can gain insight into why certain brain areas respond as they do\, and what selective pressures over evolutionary and developmental timescales give rise to the diversity of observed neural responses.\n\nIn this talk\, I apply this approach to examine the functional constraints of brain areas involved in the perception-action loop across multiple timescales: 1. the role of recurrent processing in rapid object recognition (within 250 ms)\, and 2. visually-grounded mental simulation of future environmental states (within several seconds). Finally\, I conclude with future directions towards closing the perception-action loop by extending task-optimized modeling to build integrative\, embodied agents to gain a systems-level understanding of an organism’s brain. These agents would serve as normative accounts of how brain areas collaborate to enable meaningful actions in the physical world. Their design will elucidate the algorithmic principles of natural intelligence conserved across species\, and yield safer\, more grounded embodied AI algorithms.
URL:https://seasevents.nmsdev7.com/event/be-seminar-a-task-optimized-approach-to-systems-neuroscience-aran-nayebi-mit/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut 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:20240405T093000
DTEND;TZID=America/New_York:20240405T103000
DTSTAMP:20260403T134731
CREATED:20240325T133006Z
LAST-MODIFIED:20240325T133006Z
UID:10007912-1712309400-1712313000@seasevents.nmsdev7.com
SUMMARY:MEAM Ph.D. Thesis Defense: "Computational Study on Rough Wall-Bounded Flows and their Effects at Low and Very-High Reynolds Numbers"
DESCRIPTION:Many relevant engineering fluid dynamics problems\, such as turbulent flow over an airplane or transport processes in geophysical flows\, contain wall-bounded regions that form boundary layers. Oftentimes\, numerical and experimental studies are simplified by using smooth surfaces.  This simplification has allowed us to gain a greater understanding of near-wall processes for many flows of interest\, yet in actuality\, most surfaces are inherently rough. In many cases\, especially at higher Reynolds numbers where boundary layers are relatively thin\, roughness elements protrude far enough into the flow to disrupt it. This roughness can induce form drag\, reducing efficiency for ships\, planes\, and turbines\, or alter transport of heat\, contaminants\, and sediment in atmospheric flows. However\, at lower Reynolds numbers\, roughness may produce the opposite effect\, reducing drag or enhancing lift capabilities\, such as in the case of dimples on a golf ball. Here\, I use large-eddy simulations to discern the physics governing rough\, wall-bounded flows at the two extremes of the Reynolds number range. I first investigate the aerodynamic enhancement provided by surface roughness on a micro-propeller operating at a Reynolds number ~ O(10^3). Physical mechanisms of the improvement in thrust are found and disclosed. Next\, I investigate changes to an atmospheric boundary layer at a Reynolds number ~ O(10^6)\, encountering a step change in roughness. I examine the real roughness transition that occurs at White Sands National Park in New Mexico\, USA\, where an Alkali Flat develops into a spatially heterogeneous dune field. A new mesoscopic length-scale is established\, as the study reveals a self-similarity of turbulence within the internal boundary layer\, displaying its importance to the large-scales of the flow. I extend this investigation using quadrant analysis to determine the role of the developing internal boundary layer in modifying turbulence producing events.
URL:https://seasevents.nmsdev7.com/event/meam-ph-d-thesis-defense-computational-study-on-rough-wall-bounded-flows-and-their-effects-at-low-and-very-high-reynolds-numbers/
LOCATION:Towne 313\, 220 S. 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Doctoral,Dissertation or Thesis Defense
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240405T103000
DTEND;TZID=America/New_York:20240405T114500
DTSTAMP:20260403T134731
CREATED:20231220T154633Z
LAST-MODIFIED:20231220T154633Z
UID:10007788-1712313000-1712317500@seasevents.nmsdev7.com
SUMMARY:Spring 2024 GRASP on Robotics: Jessy Grizzle\, University of Michigan\, "Michigan’s Robotics Department and Undergrad Curriculum - 'Non c'è scommessa più persa di quella che non giocherò' (There’s no bet more lost than the one I won’t even play)" -- Ora by Jovanotti
DESCRIPTION:This is a hybrid event with in-person attendance in Wu and Chen and virtual attendance on Zoom. \nABSTRACT\nAfter 39 years as a faculty member with continuous NSF support\, the speaker has graduated his last PhD students\, closed his lab\, and turned 100% to teaching. From June 2016 through June 2021\, he led Michigan’s Robotics Institute (aka\, he took that bet) and transformed it into a full-fledged department with UG\, MS\, and PhD degrees. The talk will focus on the undergraduate program and its highly innovative math sequence: ROB 101 Computational Linear Algebra has already taught over 800 Michigan engineers\, 90 HBCU students\, and a handful of HS students;  and ROB 201 Calculus for the Modern Engineer\, which will be piloted in Fall 2024. Both courses are focused on equity because\, while Talent is Uniformly Distributed\, Opportunity is Not!
URL:https://seasevents.nmsdev7.com/event/spring-2024-grasp-on-robotics-jessy-grizzle-university-of-michigan-non-ce-scommessa-piu-persa-di-quella-che-non-giochero-theres-no-bet-more-lost-than-the-one-i-wont-ev/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="General Robotics%2C Automation%2C Sensing and Perception (GRASP) Lab":MAILTO:grasplab@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240408T153000
DTEND;TZID=America/New_York:20240408T163000
DTSTAMP:20260403T134731
CREATED:20240315T151323Z
LAST-MODIFIED:20240315T151323Z
UID:10007902-1712590200-1712593800@seasevents.nmsdev7.com
SUMMARY:Penn Engineering 2023-24 Heilmeier Faculty Award Lecture: Arjun Raj
DESCRIPTION:“Can a cell learn?”\nEver since the genetic code was deciphered\, we have increasingly come to view cellular control through the lens of genetic determinism. In this paradigm\, a cell’s fate is already written into its DNA\, which is in turn shaped by Darwinian evolution over the course of many generations. At the same time\, an essential part of our experience as human beings is our ability to learn: our past shapes our present in a multitude of ways\, all within a single lifetime. Is it possible that cells can adapt to their environment by learning\, thereby overcoming their genetic destiny? We explore this possibility by tracing the life history of individual cells. In the context of drug resistance in cancer\, we show that there is a special subset of cells that can store memories of past events. These memories allow cells to rewire themselves at the molecular level to adapt to challenges that evolution may have never encountered. We posit that cellular learning may be occurring across many biological systems\, affording new opportunities for the engineering of cellular behavior.
URL:https://seasevents.nmsdev7.com/event/penn-engineering-2023-24-george-h-heilmeier-faculty-award-lecture-arjun-raj/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Distinguished Lecture
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240410T120000
DTEND;TZID=America/New_York:20240410T133000
DTSTAMP:20260403T134731
CREATED:20240401T152812Z
LAST-MODIFIED:20240401T152812Z
UID:10007924-1712750400-1712755800@seasevents.nmsdev7.com
SUMMARY:ASSET Seminar: "What Should We “Trust” in Trustworthy Machine Learning?" (Aaron Roth\, University of Pennsylvania)
DESCRIPTION:ABSTRACT: \n\n\n“Trustworthy Machine Learning” has become a buzz-word in recent years. But what exactly are the semantics of the promise that we are supposed to trust? In this talk we will make a proposal\, through the lens of downstream decision makers using machine learning predictions of payoff relevant states: Predictions are “Trustworthy” if it is in the interests of the downstream decision makers to act as if the predictions are correct\, as opposed to gaming the system in some way. We will find that this is a fruitful idea. For many kinds of downstream tasks\, predictions of the payoff relevant state that are statistically unbiased\, subject to a modest number of conditioning events\, suffice to give downstream decision makers strong guarantees when acting optimally as if the predictions were correct — and it is possible to efficiently produce predictions (even in adversarial environments!) that satisfy these bias properties. This methodology also gives an algorithm design principle that turns out to give new\, efficient algorithms for a variety of adversarial learning problems\, including obtaining subsequence regret in online combinatorial optimization problems and extensive form games\, and for obtaining sequential prediction sets for multiclass classification problems that have strong\, conditional coverage guarantees — directly from a black box prediction technology\, avoiding the need to choose a “score function” as in conformal prediction. \n  \nThis is joint work with Georgy Noarov\, Ramya Ramalingam\, and Stephan Xie \n\n\n\nZOOM LINK (if unable to attend in-person): https://upenn.zoom.us/j/96814843409
URL:https://seasevents.nmsdev7.com/event/asset-seminar-what-should-we-trust-in-trustworthy-machine-learning-aaron-roth-university-of-pennsylvania/
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:20240410T130000
DTEND;TZID=America/New_York:20240410T140000
DTSTAMP:20260403T134731
CREATED:20240328T183658Z
LAST-MODIFIED:20240328T183658Z
UID:10007920-1712754000-1712757600@seasevents.nmsdev7.com
SUMMARY:MEAM Ph.D. Thesis Defense: "Implementation and Performance of Wall Models for Large Eddy Simulation of Non-equilibrium Turbulent Boundary Layers"
DESCRIPTION:Accurate prediction of high-Reynolds-number wall-bounded turbulent flows is essential for the understanding and flow control of many engineering applications such as aircraft\, turbomachinery\, and marine vehicles. Additionally\, most practical flows exhibit nonequilibrium effects such as pressure gradient\, flow separation\, and mean three-dimensionality. However\, the direct numerical simulation (DNS) of high-Reynolds-number wall-bounded turbulent flows is not feasible owing to the prohibitive computational cost of resolving small-scale eddies near the wall. Wall-modeled large-eddy simulation (WMLES) presents an affordable predictive alternative to the DNS via the approximate modeling of flow physics near the wall (through a wall model) while resolving the outer (larger) scales directly on the computational grid. In this work\, we focus on two aspects of wall models\, (i) development and implementation of new/existing wall models\, and (ii) application and comparison of different wall models in various nonequilibrium turbulent boundary layers. In the first part\, we develop a novel spectral formulation for the ODE equilibrium wall model\, showing its superior efficiency to the traditional approach. Furthermore\, we extend the integral nonequilibrium wall model to an unstructured-grid LES solver. In the second part\, we explore three wall models with varying degrees of computational complexity and physical fidelity\, to assess their performance in two controlled but reasonably realistic nonequilibrium flows over a flat plate. The first flow features a turbulent boundary layer undergoing a series of complex pressure gradient effects\, while the second exhibits turbulent flow separation induced by suction and blowing. While in the latter case\, the more complex model clearly produces a superior prediction of the wall shear stress\, the same is not necessarily true in the former case\, highlighting that there still exists the need to adapt the existing wall models to different flow physics by modifying their underlying formulation or assumptions. Finally\, a physic-based decomposition of skin friction\, that shows separable contributions from various physical processes in the flow\, is employed to explain the differing mechanisms of success/failure of wall models in different flows.
URL:https://seasevents.nmsdev7.com/event/meam-ph-d-thesis-defense-implementation-and-performance-of-wall-models-for-large-eddy-simulation-of-non-equilibrium-turbulent-boundary-layers/
LOCATION:Room B13\, Chemistry Building\, 231 S. 34th Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Doctoral,Dissertation or Thesis Defense
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240410T133000
DTEND;TZID=America/New_York:20240410T133000
DTSTAMP:20260403T134731
CREATED:20240408T141733Z
LAST-MODIFIED:20240408T141733Z
UID:10007936-1712755800-1712755800@seasevents.nmsdev7.com
SUMMARY:ESE PhD Thesis Defense: "Scalable and Risk-Aware Verification of Learning Enabled Autonomous Systems"
DESCRIPTION:As autonomous systems become more prevalent\, ensuring their safety will become more and more important. However\, deriving guarantees for these systems is becoming increasingly difficult due to the use of black box\, learning enabled components and the growing range of operating domains in which they are deployed. The complexity of the learning-enabled components greatly increases the computational complexity of the verification problem. Additionally\, the safety predictions from verifying these systems must be conservative. This thesis explores two high-level methods for verifying autonomous systems: probabilistic model checking and statistical model checking. Probabilistic model checking methods exhaustively analyze a model of the system to reason about its properties. These methods generally suffer from scalability issues\, but if the abstraction is built correctly then the results will be provably conservative. On the other hand\, statistical model checking methods draw traces from the system to reason about its properties. These methods don’t suffer the scalability drawback of probabilistic model checking\, but their guarantees are weaker and may not even be conservative. This thesis introduces methods for improving the scalability of verifying autonomous systems with probabilistic model checking methods and incorporating notions of conservatism into statistical model checking. \nOn the probabilistic model checking side\, this thesis first explores using engineering intuitions about systems to reduce probabilistic model checking complexity while preserving conservatism. Next\, standard conservative probabilistic model checking techniques are used to synthesize runtime monitors that are conservative and lightweight. Finally\, this thesis presents a run-time method for composing monitors of verification assumptions. Verification assumptions are critical for simplifying verification problems so that they become computationally feasible. \nFor statistical model checking\, this thesis first leverages a method called conformal prediction to bound the errors of trajectory predictors\, which enables safe (i.e. conservative) planning in dynamic environments. Additionally\, a method for producing less conservative conformal prediction regions in time series settings is developed. Then a method called risk verification is developed\, which uses statistical methods to bound risk metrics of a system’s performance. Risk metrics\, which capture tail events of the system’s performance\, offer a statistical equivalent of worst case analysis.
URL:https://seasevents.nmsdev7.com/event/ese-phd-thesis-defense-scalable-and-risk-aware-verification-of-learning-enabled-autonomous-systems/
LOCATION:Moore 317\, 200 S 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Dissertation or Thesis Defense
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240410T133000
DTEND;TZID=America/New_York:20240410T143000
DTSTAMP:20260403T134731
CREATED:20240401T152100Z
LAST-MODIFIED:20240401T152100Z
UID:10007923-1712755800-1712759400@seasevents.nmsdev7.com
SUMMARY:ESE Spring Seminar - "Solving Inverse Problems with Generative Priors: From Low-rank to Diffusion Models"
DESCRIPTION:: Generative priors are effective countermeasures to combat the curse of dimensionality\, and enable efficient learning and inversion that otherwise are ill-posed\, in data science. This talk begins with the classical low-rank prior\, and introduces scaled gradient descent (ScaledGD)\, a simple iterative approach to directly recover the low-rank factors for a wide range of matrix and tensor estimation tasks. ScaledGD provably converges linearly at a constant rate independent of the condition number at near-optimal sample complexities\, while maintaining the low per-iteration cost of vanilla gradient descent\, even when the rank is overspecified and the initialization is random. Going beyond low rank\, the talk discusses diffusion models as an expressive data prior in inverse problems\, and introduces a plug-and-play posterior sampling method (Diffusion PnP) that alternatively calls two samplers\, a proximal consistency sampler solely based on the forward model\, and a denoising diffusion sampler solely based on the score functions of data prior. Performance guarantees and numerical examples will be demonstrated to illustrate the promise.
URL:https://seasevents.nmsdev7.com/event/ese-spring-seminar-solving-inverse-problems-with-generative-priors-from-low-rank-to-diffusion-models/
LOCATION:Towne 337
CATEGORIES:Colloquium
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240410T150000
DTEND;TZID=America/New_York:20240410T160000
DTSTAMP:20260403T134731
CREATED:20240327T182337Z
LAST-MODIFIED:20240327T182337Z
UID:10007919-1712761200-1712764800@seasevents.nmsdev7.com
SUMMARY:Spring 2024 GRASP SFI: Michel Hidalgo\, Ekumen\, "Doing robotics in digital labs: Or how simulations fuel robotics development"
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. \nSeminar attendees are also invited to a group discussion with Michel Hidalgo on Thursday\, April 11th from 3:00 PM to 4:00 PM on this link. \nABSTRACT\nHow do you do robotics without robots? Ekumen has been profitably providing software consulting services to robotics companies for over a decade\, 10000 miles from relevant technological hubs. While multi-causal\, a non-negligible factor in the company’s success were the advancements in multi-body dynamics simulation. From prototyping through validation and testing\, the robotics industry relies on these simulations to speed up and scale its development workflows. As a byproduct\, this tendency has alleviated the need for locality with hardware assets. I will discuss present-day technology in multi-body dynamics simulation\, some of our past experience with it in real-world applications\, and what we have learned from practice along the way.
URL:https://seasevents.nmsdev7.com/event/spring-2024-grasp-sfi-michel-hidalgo/
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:20240410T153000
DTEND;TZID=America/New_York:20240410T163000
DTSTAMP:20260403T134731
CREATED:20240116T182536Z
LAST-MODIFIED:20240116T182536Z
UID:10007812-1712763000-1712766600@seasevents.nmsdev7.com
SUMMARY:CBE Seminar: "Role of Water in Underwater Adhesion" (Ali Dhinojwala\, University of Akron)
DESCRIPTION:Abstract\nRoughness and wetness can disrupt interfacial bonding and reduce adhesion\, and this phenomenon is of relevance for many biological and engineering applications. I will discuss how roughness affects both dry and wet adhesion as well as provide an overview of our current theoretical understanding in this area. My specific interest is in underwater adhesion\, focusing on overcoming the challenges for achieving adhesion in confined water\, which reduces molecular contact (particularly when in contact with rough surfaces). The trapping of confined water is a function of roughness\, surface chemistry\, and kinetics\, making this a difficult problem to explain using theoretical models. Interestingly\, nature has developed a wide range of strategies that enable organisms to stick to rough and wet surfaces. For example\, geckos and insects use fibrillar structures to create molecular contact and to improve water drainage\, spiders use hygroscopic salts to reduce interfacial water next to hydrophilic surfaces\, and mussels use specific chemical groups (catechol) to bind to polar surfaces. I will share new strategies inspired by these natural systems for improving adhesion and discuss how they are applied to biomedical and engineering applications that require adhesion to wet and rough surfaces.
URL:https://seasevents.nmsdev7.com/event/cbe-seminar-role-of-water-in-underwater-adhesion-ali-dhinojwala-university-of-akron/
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:20240411T103000
DTEND;TZID=America/New_York:20240411T120000
DTSTAMP:20260403T134731
CREATED:20240405T135838Z
LAST-MODIFIED:20240405T135838Z
UID:10007931-1712831400-1712836800@seasevents.nmsdev7.com
SUMMARY:MSE Seminar: "Thermal Architecture"
DESCRIPTION:Air conditioning accounts for nearly 20% of the total electricity used in buildings globally and cooling energy demand is predicted to significantly increase over the next decades due to urbanization\, population growth\, and global warming. Heat stress is a major environmental justice concern\, disproportionally impacting disadvantaged communities. What are the paths to reduce the massive energy consumption of the building sector and at the same time still provide people with healthy living environments? With COVID-19 and extreme heat stress events placing a strong focus on the precarious relationship between indoor spaces and human well-being\, we need a new paradigm for environmental control in buildings. \nThe Thermal Architecture Lab seeks to find sustainable and equitable cooling alternatives to replace current building practices. Working at the intersection of heat transfer\, architectural design\, and material science\, we develop novel technologies and design strategies to simultaneously reduce buildings’ energy demand and provide thermal shelter to people in a warming world. In this lecture\, a series of projects will be presented\, exploring various methods to provide both ventilation and climatic adaptation to interior spaces across different climatic zones.
URL:https://seasevents.nmsdev7.com/event/mse-seminar-thermal-architecture/
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:20240412T093000
DTEND;TZID=America/New_York:20240412T103000
DTSTAMP:20260403T134731
CREATED:20240401T170733Z
LAST-MODIFIED:20240401T170733Z
UID:10007925-1712914200-1712917800@seasevents.nmsdev7.com
SUMMARY:MEAM Ph.D. Thesis Defense: "Controlling Fracture Behavior Through Architecture"
DESCRIPTION:Many natural materials achieve excellent combinations of mechanical properties through their micro- and nano-scale structures\, which leverage a level of complexity currently unmatched in engineering design. Recent advances in digital manufacturing have enabled the introduction of these fine-scale architectures to improve the mechanical properties of materials\, but their intricacy still lags far behind that of natural materials. In particular\, the potential of these structures to create materials with enhanced fracture resistance has remained limited\, primarily due to a narrow design focus on simple\, repetitive structures optimized for idealized materials. Improving the damage-tolerance of materials is critical to the mechanical performance of structures and interfaces\, as cracks and defects often lead to failure at far-field loads that are significantly lower than the theoretical strength of the system. This thesis will demonstrate how leveraging disordered structures and considering material behavior beyond the idealized elastic-brittle regime can significantly enhance the fracture resistance of architected interfaces. Specifically\, three key aspects influencing the failure of architected interfaces are examined: the effects of plasticity\, the advantages of disordered structures\, and the impacts of stochastic material failure. Through a synthesis of mechanics frameworks\, computational modeling\, and experimental mechanics including full-field analyses using digital image correlation and photoelasticity\, it is shown that properly designed architectures lead to tunable and enhanced fracture resistance. These architectures enlarge the region of damage around the crack tip\, delocalizing stresses and increasing the resistance to crack propagation\, while also revealing novel properties such as the decoupling of toughness and strength.
URL:https://seasevents.nmsdev7.com/event/meam-ph-d-thesis-defense-controlling-fracture-behavior-through-architecture/
LOCATION:DRL A8\, 209 S. 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Doctoral,Dissertation or Thesis Defense
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240412T103000
DTEND;TZID=America/New_York:20240412T114500
DTSTAMP:20260403T134731
CREATED:20231220T164100Z
LAST-MODIFIED:20231220T164100Z
UID:10007789-1712917800-1712922300@seasevents.nmsdev7.com
SUMMARY:Spring 2024 GRASP on Robotics: David Fridovich-Keil\, University of Texas at Austin\, "Information-Aware Algorithms for Smooth Dynamic Games"
DESCRIPTION:This is a hybrid event with in-person attendance in Wu and Chen and virtual attendance on Zoom. \nABSTRACT\nThis talk introduces dynamic game theory as a natural modeling tool for multi-agent interactions ranging from large\, abstract systems such as ride-hailing networks to more concrete\, physically-embodied robotic settings such as collision-avoidance in traffic. We present the key theoretical underpinnings of dynamic game models for these varied situations and draw attention to the subtleties of information structure\, i.e.\, what information is implicitly made available to each agent in a game. Thus equipped\, the talk presents a state-of-the-art technique for solving several variants of these games\, as well as a set of “dual” techniques for the inverse problem of identifying players’ objectives and other structures based on observations of strategic behavior.
URL:https://seasevents.nmsdev7.com/event/spring-2024-grasp-on-robotics-david-fridovich-keil-university-of-texas-at-austin-information-aware-algorithms-for-smooth-dynamic-games/
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:20240412T140000
DTEND;TZID=America/New_York:20240412T150000
DTSTAMP:20260403T134731
CREATED:20240312T191603Z
LAST-MODIFIED:20240312T191603Z
UID:10007898-1712930400-1712934000@seasevents.nmsdev7.com
SUMMARY:PICS Colloquium: "Modeling Lone Pair Dynamics in Materials"
DESCRIPTION:Materials properties are governed by the structure and dynamics of the bonds between their constituent atoms. In addition to covalent\, metallic\, and ionic interactions that we typically think about\, lone pair electrons can result in non-trivial directional interactions in materials. I will discuss molecular interactions involving lone pairs in materials\, focusing on results from molecular simulations of the electronic structure and dynamics of model halide perovskites and solid-state ionic conductors of interest for applications in energy science. After a brief discussion of lone pair-driven interactions in materials\, I will present recent results predicting the existence of electronic plastic crystals\, crystalline solids that exhibit dynamic rotational disorder of lone pairs. We predict that electronic plastic crystals are found in a wide range of molecular and ionic materials\, including halide perovskites\, where we anticipate that dynamic lone pair disorder plays an important role in photophysical processes. I will then present recent results from our ongoing investigations into electron pair dynamics in solid-state electrolytes for energy storage applications. We predict that rotational motion of electron pairs is coupled to translational dynamics of conducting ions\, forming electronic paddle-wheels in solid-state electrolytes. Finally\, I will discuss our ongoing efforts to reach the length and time scales necessary to model electrochemical processes in these and related systems by developing machine learning-based models that simultaneously describe electronic effects and coupling to long-range electrostatic fields. By focusing on the correct physics\, the resulting models are partially transferable and can describe electronic and nuclear response to external fields. I will then demonstrate the accuracy and transferability of this neural network approach – the self-consistent field neural network (SCFNN) – on model aqueous systems before closing with a discussion of implications of our results for the development of machine learning models.
URL:https://seasevents.nmsdev7.com/event/pics-colloquium-modeling-lone-pair-dynamics-in-materials/
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:20240412T150000
DTEND;TZID=America/New_York:20240412T163000
DTSTAMP:20260403T134731
CREATED:20240328T204435Z
LAST-MODIFIED:20240328T204435Z
UID:10007921-1712934000-1712939400@seasevents.nmsdev7.com
SUMMARY:Career Steps Before Graduation Seminar
DESCRIPTION:On Friday\, April 12\,2024\, the Department of Materials Science and Engineering is hosting an information session dedicated to equipping you with insights on the essential steps to secure a career after graduation\, with staff from UPenn Career Services and ISSS (International Student and Scholar Services) lending their expertise on prevailing job trends and providing guidance on navigating the intricacies of securing employment. \nWhat sets this presentation apart is the tailored approach.  Rather than offering generic advice\, our goal is to tailor the presentation to address YOUR questions and concerns. That’s why we’re asking you to register (which is required) and submit any questions that you have in advance. Whether you’re curious about visa regulations\, resume tips\, using OPT\, or hiring trends\, we want to provide as much support as possible for your career goals. \nThis opportunity is extended to all SEAS students who are approaching graduation. Therefore\, we ask you to secure your spot at your earliest convenience\, ensuring that you don’t miss out on this opportunity.
URL:https://seasevents.nmsdev7.com/event/career-steps-before-graduation-seminar/
LOCATION:Auditorium\, LRSM Building\, 3231 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:20240412T150000
DTEND;TZID=America/New_York:20240412T170000
DTSTAMP:20260403T134731
CREATED:20240326T152430Z
LAST-MODIFIED:20240326T152430Z
UID:10007915-1712934000-1712941200@seasevents.nmsdev7.com
SUMMARY:CBE Doctoral Dissertation Defense: "A Multifaceted Approach to CO2 Emissions Reductions and Removals" (Maxwell Pisciotta)
DESCRIPTION:Abstract:\nThe scientific consensus is that climate change is not only actively occurring\, but that it is irrevocably due to human activities associated with greenhouse gas emissions. Greenhouse gas emissions have been accumulating in the atmosphere since the beginning of the industrial revolution. This thesis specifically focuses on one greenhouse gas in particular\, CO 2 . The continued CO 2 emissions from human activity can be quantified with the atmospheric concentration\, which amounts to upwards of 420 ppm today. To mitigate the harmful impacts of climate change\, these CO 2 emissions must be mitigated\, through pathways such as reducing their initial generation\, capturing them when they are unable to be avoided\, and removing them from the atmosphere when they cannot be captured at the source. This thesis investigates different technologies that fit into these broad categories\, notably\, deploying carbon capture technologies on natural gas combined cycle power plants\, decarbonizing industrial sectors\, and pairing direct air capture technologies to geothermal energy. To readily address the CO 2 emissions from natural gas combined cycle power plants\, a novel approach of using thermal energy storage was developed and evaluated to ensure its technological performance and economic viability. By integrating natural gas combined cycle power plants with carbon capture and storage (CCS) and thermal energy storage opportunities\, the economic viability of these plants improve. This was measured using the net present value of each of the configurations assessed over real-world locational marginal pricing (LMP) signals from NYISO and CAISO. Of the thermal energy storage options\, eight of the 19 thermal energy storage configurations led to an increased net present value on 11.5% – 98% of the LMP signals. Additionally\, a framework was developed and used to identify opportunities to integrate direct air capture (DAC) systems with geothermal energy resources to maximize the CO 2 abatement potential. The Geothermal-Framework can be used with various geothermal resources ranging from 86ºC – 225ºC\, using various working fluids\, and brine salinity ranging from 0-6%. When the integration of geothermal energy and DAC systems are compared to geothermal energy being used to generate low-carbon electricity\, the CO 2 abatement potential is increase by 105% to 452% when geothermal energy is integrated with DAC systems. This illustrates beneficial synergies between the two technologies\, namely being able to use geothermal energy as thermal energy rather than solely converting it to electricity. Lastly\, the Geothermal-DAC Framework was used to showcase opportunities for integrating DAC with the geothermal resources near Gerlach\, NV\, in preparation for a community meeting. The community feedback was then incorporated\, facilitating updates to the Geothermal-DAC Framework to account for community needs\, illustrating that engineering can be community-centered from the start of the project. All the approaches explored in this thesis highlight the need for a diverse portfolio of solutions to address the ongoing CO 2 emissions and abatement required to avoid the most harmful impacts of climate change. Furthermore\, the efforts of researchers\, scientists\, policymakers and frontline communities will be needed in concert to deploy a portfolio that meets the needs to address climate change and protect against further environmental injustices.
URL:https://seasevents.nmsdev7.com/event/cbe-doctoral-dissertation-defense-a-multifaceted-approach-to-co2-emissions-reductions-and-removals-maxwell-pisciotta/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Doctoral,Student,Dissertation or Thesis Defense
ORGANIZER;CN="Chemical and Biomolecular Engineering":MAILTO:cbemail@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240416T090000
DTEND;TZID=America/New_York:20240416T110000
DTSTAMP:20260403T134731
CREATED:20240410T132128Z
LAST-MODIFIED:20240410T132128Z
UID:10007942-1713258000-1713265200@seasevents.nmsdev7.com
SUMMARY:BE Doctoral Dissertation Defense: "The Origin and Factors Affecting Differentiation of Progenitor Cells in Tendon-to-Bone Integration" (Tim Kamalitdinov)
DESCRIPTION:The Department of Bioengineering at the University of Pennsylvania and Dr. Nat Dyment are pleased to announce the Doctoral Dissertation Defense of Tim Kamalitdinov.\n\n\nTitle: The Origin and Factors Affecting Differentiation of Progenitor Cells in Tendon-to-Bone Integration\nDate: April 16\, 2024\nTime: 9:00 AM\nLocation: SCTR (Smilow Center for Translational Research) 12-146AB\n\nThe public is welcome to attend.
URL:https://seasevents.nmsdev7.com/event/be-doctoral-dissertation-defense-the-origin-and-factors-affecting-differentiation-of-progenitor-cells-in-tendon-to-bone-integration/
LOCATION:Smilow Center for Translational Research in SCTR 11-146AB
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:20240416T090000
DTEND;TZID=America/New_York:20240416T110000
DTSTAMP:20260403T134731
CREATED:20240410T212450Z
LAST-MODIFIED:20240410T212450Z
UID:10007944-1713258000-1713265200@seasevents.nmsdev7.com
SUMMARY:ESE PhD Thesis Defense: "Multiferroic Micro Electromechanical Systems for Magnetic Sensing and Wireless Power Transfer in Biomedical Applications"
DESCRIPTION:Multiferroic micro-electromechanical systems (MEMS) enable small\, room temperature\, low power magnetic sensing and wireless power transfer (WPT) in biomedical applications. Current biomagnetic sensing relies on sensitive magnetometers like superconducting quantum interference devices (SQUIDs)\, but their reliance on cryogenic temperatures is undesirable. \n  \nThis thesis presents the theory\, design\, microfabrication\, and characterization of multiferroic MEMS magnetic sensors and WPT devices. Iron cobalt/silver (Fe50Co50/Ag) magnetostrictive material is coupled to piezoelectric aluminum nitride (AlN) to form a multiferroic sensor. Low frequency biomagnetic signals are upconverted around the length-extensional beam’s 7-16 MHz mechanical resonance to provide Q enhancement to the sensitivity. The up conversion exploits a nonlinear phenomenon of magnetostrictive materials with applied mechanical strain. For two devices studied\, modulated sensitivities of 58.4 mA/T and 37.7 mA/T were observed along with resolutions of 5.03 nT/√Hz and 2.72 nT/√Hz over a bandwidth larger than the biomagnetic frequency spectrum (0.1Hz to 1kHz). The sensors’ sensitivity was limited by Duffing nonlinearity and the relatively low piezoelectric coefficients of AlN. \n  \nTo improve sensitivity\, magnetoelectric sensors were fabricated using (Fe0.5Co0.5)0.92Hf0.08 coupled to 28% aluminum scandium nitride (Al0.72Sc0.28N). Increasing sensitivity improved the resolution from 5.03 nT/√Hz to 2.16 nT/√Hz. To delay the onset of thermal Duffing nonlinearity\, various anchoring tether lengths were explored in Fe0.5Co0.5/Ag – AlN magnetoelectric sensors to provide better heat conduction away from the structure. Also\, silicon dioxide (SiO2) was added to compensate the temperature coefficient of frequency (TCF). Larger achievable strain was verified before the onset of Duffing nonlinearity\, providing increased modulation of the Fe0.5Co0.5/Ag and a resolution of 1.11 nT/√Hz\, an 86% improvement when compared to a long tether device with the same layer stack (8.02 nT/√Hz) and a 78% improvement over the initial (Fe50Co50/Ag) – AlN long tether devices with no SiO2 thermal compensation. \n  \nWPT measurements were taken using (Fe50Co50/Ag) – AlN magnetoelectric devices. By sending a magnetic field at the device resonance frequency\, optimal WPT can be achieved. Devices were packaged with a magnetic bias circuit and the output power was measured. For a device at 7.44MHz\, an output power of 126.8 nW and a power density of 1196.2 uW/mm3 is projected when measuring with both electrodes.
URL:https://seasevents.nmsdev7.com/event/ese-phd-thesis-defense-multiferroic-micro-electromechanical-systems-for-magnetic-sensing-and-wireless-power-transfer-in-biomedical-applications/
LOCATION:Fisher Bennett Hall\, Room 401\, 3340 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Dissertation or Thesis Defense
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240416T100000
DTEND;TZID=America/New_York:20240416T113000
DTSTAMP:20260403T134731
CREATED:20240319T165717Z
LAST-MODIFIED:20240319T165717Z
UID:10007907-1713261600-1713267000@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Cardiovascular Engineering – A 'Personal' Journey from Bench to Bedside"
DESCRIPTION:Over the past few decades\, significant contributions have been made by engineers to healthcare. The successful translation of fundamental engineering concepts has helped improve patient care and diagnosis. This impact has been particularly evident in the field of cardiovascular medicine where the roles of fluid and solid mechanics\, and imaging are critical. In ~45 years of pioneering research\, Professor Ajit Yoganathan’s Cardiovascular Fluid Mechanics Laboratory at the Georgia Institute of Technology & Emory University\, has been in the vanguard of this movement: advancing knowledge and technology in native and replacement heart valves\, cardiovascular diagnostic techniques\, and pediatric surgical/interventional planning. Using state-of-the-art fluid dynamic measurement techniques\, Dr. Yoganathan and his group have developed methods to enable the optimization of replacement heart valve designs. Novel techniques in the assessment of native heart valve function have provided clinicians with improved tools to assess disease severity and helped identify effective treatment options. \nFor the treatment of congenital heart defects\, the development of novel computational modeling tools to simulate surgical procedures and their fluid dynamics outcomes have provided clinicians with new ways to plan for treatments for individual patients to increase the probability of success. Combined\, these advances have helped bridge the lab bench to the patient’s bedside/bassinet and integrate engineering science with the art of medicine.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-cardiovascular-engineering-a-personal-journey-from-bench-to-bedside/
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
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240416T110000
DTEND;TZID=America/New_York:20240416T120000
DTSTAMP:20260403T134731
CREATED:20240326T125329Z
LAST-MODIFIED:20240326T125329Z
UID:10007913-1713265200-1713268800@seasevents.nmsdev7.com
SUMMARY:ESE Spring Seminar - "Scaling Deep Learning Up and Down"
DESCRIPTION:Deep learning with neural networks has emerged as a key approach for discovering patterns and modeling relationships in complex data. AI systems powered by deep learning are used widely in applications across a broad spectrum of scales. There are strong needs for scaling deep learning both upward and downward. Scaling up highlights the pursuit of scalability – the ability to utilize increasingly abundant computing and data resources to achieve superior capabilities\, overcoming diminishing returns. Scaling down represents the demand for efficiency – there is limited data for many application domains\, and deployment is often in compute-limited settings. \nIn this talk\, we present several studies in both directions. For scaling up\, we first explore the design of scalable neural network architectures that are widely adopted in various fields. We then discuss an intriguing observation on modern vision datasets and its implication on scaling training data. For scaling down\, we introduce simple\, effective\, and popularly used approaches for compressing convolutional networks and large language models\, alongside interesting empirical findings. Notably\, a recurring theme in this talk is the careful examination of implicit assumptions in the literature\, which often leads to surprising revelations that reshape community understanding. Finally\, we discuss exciting avenues for future deep learning and vision research\, such as next-gen architectures and dataset modeling.
URL:https://seasevents.nmsdev7.com/event/ese-spring-seminar-scaling-deep-learning-up-and-down/
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
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240416T120000
DTEND;TZID=America/New_York:20240416T133000
DTSTAMP:20260403T134731
CREATED:20240410T150958Z
LAST-MODIFIED:20240410T150958Z
UID:10007943-1713268800-1713274200@seasevents.nmsdev7.com
SUMMARY:Raj and Neera Singh Program in Artificial Intelligence Town Hall
DESCRIPTION:
URL:https://seasevents.nmsdev7.com/event/artificial-intelligence-undergraduate-program-town-hall/
LOCATION:Glandt Forum\, Singh Center for Nanotechnology\, 3205 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Student,Panel Discussion,Undergraduate
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END:VCALENDAR