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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200929T103000
DTEND;TZID=America/New_York:20200929T120000
DTSTAMP:20260407T151516
CREATED:20200827T212941Z
LAST-MODIFIED:20200827T212941Z
UID:10006449-1601375400-1601380800@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Aluminum Scandium Nitride Microdevices for Next Generation Nonvolatile Memory and Microelectromechanical Systems"
DESCRIPTION:Aluminum Nitride (AlN) is a well-established thin film piezoelectric material. AlN bulk acoustic wave (BAW) radio frequency (RF) filters were one of the key innovations that enabled the 3G and 4G smart phone revolution. Recently\, the substitutional doping of scandium (Sc) for aluminum (Al) to form aluminum scandium nitride (AlScN) has been studied to significantly enhance the piezoelectric properties and to introduce ferroelectric properties into AlN based material systems. The properties achieved have profound implications for the performance of future 5G and 6G RF filters\, piezoelectric sensors\, piezoelectric energy harvesters\, and for scaling the bit density of ferroelectric nonvolatile memories. This talk will present on the synthesis of highly Sc doped AlScN materials of the thickness and quality needed for applications in memory and microelectromechanical systems (MEMS). The material properties achieved will be reported and placed in the context of device specific figures-of-merit and competing material systems. Ferroelectric and electromechanical devices that utilize the unique properties of AlScN to achieve state-of-the-art (SOA) performance will be shown.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-aluminum-scandium-nitride-microdevices-for-next-generation-nonvolatile-memory-and-microelectromechanical-systems/
LOCATION:Zoom – Email MEAM for Link\, peterlit@seas.upenn.edu
CATEGORIES:Seminar
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200929T110000
DTEND;TZID=America/New_York:20200929T120000
DTSTAMP:20260407T151516
CREATED:20200918T004934Z
LAST-MODIFIED:20200918T004934Z
UID:10006505-1601377200-1601380800@seasevents.nmsdev7.com
SUMMARY:ESE Seminar: "Evolutionary Adaptations and Spreading Processes in Complex Networks"
DESCRIPTION:A common theme among many models for spreading processes in networks is the assumption that the propagating object (e.g.\, a pathogen\, in the context of infectious disease propagation\, or a piece of information\, in the context of information propagation) is transferred across network nodes without going through any modification. However\, in real-life spreading processes\, pathogens often evolve in response to changing environments or medical interventions\, and information is often modified by individuals before being forwarded. In this talk\, we will discuss the effects of such adaptations on spreading processes in complex networks with the aim of revealing their role in determining the threshold\, probability\, and final size of epidemics\, and exploring the interplay between them and the structural properties of the network.
URL:https://seasevents.nmsdev7.com/event/ese-seminar-vince-poor/
LOCATION:Zoom – Email ESE for Link jbatter@seas.upenn.edu
CATEGORIES:Seminar,Faculty,Colloquium,Graduate,Undergraduate
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200929T110000
DTEND;TZID=America/New_York:20200929T120000
DTSTAMP:20260407T151516
CREATED:20200923T204317Z
LAST-MODIFIED:20200923T204317Z
UID:10006511-1601377200-1601380800@seasevents.nmsdev7.com
SUMMARY:CIS Seminar: "Experiencing a new Internet architecture"
DESCRIPTION:Imagining a new Internet architecture enables us to explore new networking concepts without the constraints imposed by the current infrastructure. What are the benefits of a routing protocol that does not rely on convergence? What about a data plane without inter-domain forwarding tables on routers? How can we build secure systems if a router can derive a symmetric key for any host within 20ns? \nIn this presentation\, we invite you to join us on our 11-year long expedition of creating a next-generation secure Internet architecture: SCION. SCION has already been deployed at several ISPs and domains\, and has been in production use for the past 3 years. On our journey\, we have found that path-aware networking and multipath communication not only provide security benefits\, but also enable higher efficiency for communication\, increased network capacity\, and even reduce power utilization. \n 
URL:https://seasevents.nmsdev7.com/event/cis-seminar-2/
LOCATION:Zoom – Email CIS for link\, cherylh@cis.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200929T150000
DTEND;TZID=America/New_York:20200929T170000
DTSTAMP:20260407T151516
CREATED:20200922T212121Z
LAST-MODIFIED:20200922T212121Z
UID:10006510-1601391600-1601398800@seasevents.nmsdev7.com
SUMMARY:ESE PhD Defense: "Orbital angular momentum microlasers: From the first demonstration to ultrafast tunability"
DESCRIPTION:Orbital angular momentum (OAM) carried by structured vortex light establishes a new information dimension\, thereby promising high capacity optical communication and high performance computation in both classical and quantum regimes. Meanwhile\, laser is the key driver in the field of optics and photonics over other photonic components. Since its discovery\, laser technology has demonstrated strong impacts on a broad variety of applications\, especially in today’s information technology supporting fast growing cloud computing and communication. As microlaser plays an imperative role in modern integrated photonic platforms\, the development of OAM microlasers capable of direct generation of vortex light is critical in applying unbounded OAMs as information carriers to address the upcoming information explosion. However\, conventional microlaser designs offer coherent emission with rather simple polarization/phase states and suffer from instability. In this work\, we bring the non-Hermitian physics into the regime of nanophotonics to explore feasible designs of OAM microlasers. Non-Hermitian photonics based on parity-time symmetry successfully expands the design freedom from real material permittivity to a complete complex domain\, providing a versatile toolbox that empowers new functionalities in the realm of nanophotonics. Adapting optical non-Hermiticity into the design of microlasers enables enhanced lasing stability and efficiency\, leading to vortex microlaser emissions with a high sideband suppression ratio. By tailoring the complex index modulations at an exceptional point (EP) on a microring laser\, we demonstrated the very first OAM microlaser of which both the topological charge and the polarization state can be designed on demand. More recently\, we successfully promoted the OAM microlaser technology and achieved a dynamically tunable and scalable vortex microlaser\, providing 5 different OAM states at the same wavelength\, by optically controlled non-Hermitian coupling and spin-orbit interaction. Moreover\, we realized the ultrafast control of the fractional OAM by leveraging the rapid transient response of the semiconductor optical gain and demonstrated continuous sweeping of the fractional charge of microlaser emissions from 0 to +2 in a 100 ps time scale. The toolbox of ultrafast generation and control of various vortex light holds great promise for the development of entirely new high-speed secure information systems in a unique multidimensional space.
URL:https://seasevents.nmsdev7.com/event/ese-phd-defense-orbital-angular-momentum-microlasers-from-the-first-demonstration-to-ultrafast-tunability/
LOCATION:Zoom – Email ESE for Link jbatter@seas.upenn.edu
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:20200930T150000
DTEND;TZID=America/New_York:20200930T160000
DTSTAMP:20260407T151516
CREATED:20200916T233843Z
LAST-MODIFIED:20200916T233843Z
UID:10006500-1601478000-1601481600@seasevents.nmsdev7.com
SUMMARY:CBE Seminar: "Understanding and Engineering Catalytic Materials Using Nanocrystal Precursors"
DESCRIPTION:Abstract\n \nCatalytic processes are central to the goal of a sustainable future. A promising approach in developing catalytic materials is represented by the design of catalytic sites based on the knowledge of reaction mechanisms and structure-property relationships and aided by computation\, and in the precise synthesis of these sites at the atomic and molecular level. Nanocrystal precursors\, with tunable active sites and compositions\, can help in this mission. The goal of this talk is to show how this approach can provide not only fundamental understanding of catalytic reactions\, but also a way to precisely engineer sites to produce efficient catalysts that are active\, stable and selective for several important transformations. Advances in the synthesis of these materials will be presented. Examples of the use of these building blocks as supported systems\, or in combination with hybrid organic materials\, will be shown. This will be done to both understand trends in methane and CO2 activation\, and in the preparation of optimized catalytic systems combining multiple active phases. In all these examples\, important efforts to obtain precious structure-property relationships will be highlighted with this knowledge used to prepare more efficient and stable catalysts for the sustainable production of fuels and chemicals.
URL:https://seasevents.nmsdev7.com/event/cbe-seminar-understanding-and-engineering-catalytic-materials-using-nanocrystal-precursors/
LOCATION:Zoom – Email CBE for link
CATEGORIES:Seminar
ORGANIZER;CN="Chemical and Biomolecular Engineering":MAILTO:cbemail@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201001T104500
DTEND;TZID=America/New_York:20201001T114500
DTSTAMP:20260407T151516
CREATED:20200828T150842Z
LAST-MODIFIED:20200828T150842Z
UID:10006453-1601549100-1601552700@seasevents.nmsdev7.com
SUMMARY:MSE Seminar: "Predicting Properties of Structurally and Chemically Complex Materials using Physics-informed Statistical Learning"
DESCRIPTION:To apply statistics and data science tools to aid computational designs of materials is under fast development. There are two unique aspects of the applications of these tools in materials science. First\, the training sets are usually small. Second\, physical mechanisms of material properties can be applied to facilitate the constructions of descriptors and statistics learning methods. In this talk\, I will give three examples to address these two issues. The first example is to use machine learning to predict density and elastic moduli of SiO2-based glasses. Our machine learning approach relies on a training set generated by high-throughput atomistic simulations and a set of elaborately constructed descriptors with the fundamental physics of interatomic bonding. The predictions of our model are comprehensively compared and validated with a large amount of both simulation and experimental data. In the second example\, a general linear correlation can be found between two descriptors of local electronic structures at defects in pure metals and the solute-defect interaction energies in binary alloys of refractory metals with transition-metal substitutional solutes. This linear correlation plus a residual-corrected regression model provides quantitative and efficient predictions on the solute-defect interactions in alloys. In addition\, with these local/global electronic descriptors and a simple bond-counting model\, we developed regression models to accurately and efficiently predict the unstable stacking fault energy (γusf) and surface energy (γsurf) for refractory multicomponent alloys. Using the regression models\, we performed a systematic screening of γusf\, γsurf\, and their ratio in the high-order multicomponent systems to search for alloy candidates that may have enhanced strength-ductile synergies. First-principles calculations also confirmed search results.
URL:https://seasevents.nmsdev7.com/event/mse-seminar-predicting-properties-of-structurally-and-chemically-complex-materials-using-physics-informed-statistical-learning/
LOCATION:PA
ORGANIZER;CN="Materials Science and Engineering":MAILTO:johnruss@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201001T150000
DTEND;TZID=America/New_York:20201001T160000
DTSTAMP:20260407T151516
CREATED:20200814T180447Z
LAST-MODIFIED:20200814T180447Z
UID:10006445-1601564400-1601568000@seasevents.nmsdev7.com
SUMMARY:BE Seminar: "Predicting the effects of engineering immune cells using systems biology modeling" (Stacey Finley)
DESCRIPTION:This event will be held virtually on zoom. Check your email for the link and passcode or contact ksas@seas.upenn.edu. \nSystems biology approaches\, including computational models\, provide a framework to test biological hypotheses and optimize effective therapeutic strategies to treat human diseases. In this talk\, I present recent work in modeling signaling in cancer-targeting immune cells\, including CAR T cells at Natural Killer cells. Chimeric antigen receptors (CARs) are comprised of a variety of different activating domains and co-stimulatory domains that initiate signaling required for T cell activation. There is a lack of understanding of the mechanisms by which activation occurs. We apply mathematical modeling to investigate how CAR structure influences downstream T cell signaling and develop new hypotheses for the optimal design of CAR-engineered T cell systems. Natural Killer cells also provide a useful platform for targeting cancer cells. However\, NK cells have been shown to exhibit reduced killing ability with prolonged stimulation by cancer cells. We use a combination of mechanistic model\, optimal control theory and in silico synthetic biology to investigate strategies to enhance NK cell-mediated killing.
URL:https://seasevents.nmsdev7.com/event/be-seminar-stacey-finley/
LOCATION:PA
CATEGORIES:Seminar
ORGANIZER;CN="Bioengineering":MAILTO:be@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201002T100000
DTEND;TZID=America/New_York:20201002T110000
DTSTAMP:20260407T151516
CREATED:20200924T191009Z
LAST-MODIFIED:20200924T191009Z
UID:10006513-1601632800-1601636400@seasevents.nmsdev7.com
SUMMARY:SIG Seminar:"Differentiable Machine Learning in Deformable Simulation"
DESCRIPTION:Using the digital computer to simulate dynamic behavior of elastic and soft objects is a highly desired feature in many scientific and engineering areas: in computer animation\, it provides realistic effects of soft characters; in surgical simulation\, it delivers vivid visual experiences to the trainee; in digital fabrication\, it couples geometry design and mechanical analysis. While the basic computation model has been well established\, robustly simulating nonlinear and detailed elastic models remains an open problem\, and significant implementation and computation efforts are needed. In this talk\, I will share several new perspectives to tackle those classic yet challenging computation problem. We leverage deep neural nets mapping linear and nonlinear models by carefully crafting informative context features. This framework is called NNWarp. NNWarp is probably the first neural network based deformable simulator. With it\, we obtain nonlinear simulations via solving a fixed linear system (so the complexity is lowed by an order). More importantly\, NNWarp is highly re-usable. The resulting net trained for a rectangular beam can be directly used to simulate a swaying maple tree or a soft Armadillo. To relieve the implementation efforts\, we lift the simulation from high-dimension real domain to high-dimension complex domain. By doing so\, we generalize classic Taylor theory to a new set of equations for numerical differentiation. Unlike classic finite difference method\, this complex-step finite difference method does not suffer with subtractive cancellation issues\, making the implementation joyful\, relaxing and as accurate as using the analytic differentiation. In our recently work\, it is also used for training deep neural works. This is the first true second-order neural network training algorithm that has strong quadratic convergency in various classic network architectures.
URL:https://seasevents.nmsdev7.com/event/sig-seminardifferentiable-machine-learning-in-deformable-simulation/
LOCATION:Zoom – Email CIS for link\, cherylh@cis.upenn.edu
ORGANIZER;CN="Computer and Information Science":MAILTO:cherylh@cis.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201002T140000
DTEND;TZID=America/New_York:20201002T150000
DTSTAMP:20260407T151516
CREATED:20200914T132816Z
LAST-MODIFIED:20200914T132816Z
UID:10006491-1601647200-1601650800@seasevents.nmsdev7.com
SUMMARY:PICS Seminar: "Fusing machine learning and atomistic simulations for materials design"
DESCRIPTION:Data-driven approaches match or outperform humans at a number of tasks\, including pattern recognition in images and text or planning and strategy in rule-based games. The application of machine learning techniques is also promising for accelerating materials design. However\, experimental data for training is typically scarce and sparse. The interplay between physics-based simulations and data-driven models is particularly advantageous. It allows relying on transferable laws rather than only fitting data in a black box fashion. Meanwhile\, learning from data\nprovides a unique opportunity to parameterize and augment physics-based models\, or completely replace them. \nModels can be built that map the structure and composition of materials to their properties. With such models\, it is\nthen possible to rapidly screen libraries of candidate materials for a desired application before going to the lab. Generative models go one step further and allow tackling the inverse problem: given the desired property\, automatically suggesting a new optimal material that achieves it. \nHow to represent matter so that it can be read into or written by a computer program is key for these coupled tasks of property prediction and materials optimization. Strategies are needed to represent materials in a machine-readable way that is data-efficient\, expressive\, respectful of physical invariants and\, ideally\, invertible. \nHere\, we will discuss our current efforts in building bottom-up atom-level representations for materials design. These include variational autoencoders for dimensionality reduction and inverse design in molecules and polymers\,\nrepresentation and unsupervised learning for graphs and sequences in crystals and polymers\, generative models to\naccelerate Monte Carlo simulations of alloy phase diagrams or end-to-end differentiable simulations. \n 
URL:https://seasevents.nmsdev7.com/event/pics-seminar-fusing-machine-learning-and-atomistic-simulations-for-materials-design/
LOCATION:Zoom – email kathom@seas.upenn.edu
CATEGORIES:Colloquium
ORGANIZER;CN="Penn Institute for Computational Science (PICS)":MAILTO:dkparks@seas.upenn.edu
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