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DTSTART;TZID=America/New_York:20220222T100000
DTEND;TZID=America/New_York:20220222T113000
DTSTAMP:20260406T052128
CREATED:20220214T152040Z
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UID:10007085-1645524000-1645529400@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Understanding Particulate Soft Materials: An Integrated Approach for Novel Energy and Environmental Solutions"
DESCRIPTION:Many industrial processes involve multiphase soft materials in which solid particles are dispersed or co-exists with a fluid phase and are therefore referred to as Particulate Soft Materials (PSMs). Examples can be found in many industries\, including food\, cosmetics\, pharmaceutical\, and energy\, as well as in natural settings\, e.g.\, soils and glaciers. PSMs often display a complex mechanical behavior that is characterized by features common to both viscous fluids and elasto-plastic solids\, with material properties that can change over time due to thermodynamic\, chemical or kinematic conditions. Consequently\, these complexities and our limited understanding of the behavior of PSMs can lead to critical industrial challenges\, ranging from quality control of concrete to shelf-life of consumer products. These issues can also prove environmentally disastrous\, as in the case of clogged subsea pipelines or in landslides and avalanches. Such problems call for immediate solutions to measure and model the PSM overall mechanical behavior\, towards an improved understanding of this vast class of materials and corresponding processes. \nMy research demonstrates that these challenges can be overcome by: (i) introducing novel experimental tools and protocols that allow us to study the mechanical and rheological response of PSMs\, even when their behavior is rapidly changing\, or mutating\, in time; and (ii) rigorously setting sound theoretical frameworks that explain the experimental observations. In this talk\, focusing on two PSMs that are of interest to the energy industry (i.e.\, paraffin gels and hydrate suspensions)\, I will introduce an example of the integrated experimental and theoretical framework that can successfully capture PSM complex visco-plastic response. As I will demonstrate in my talk\, this powerful approach not only improves our understanding of both artificial and natural PSMs\, but also provides guidelines to develop superior materials for critical energy and environmental challenges.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-understanding-particulate-soft-materials-an-integrated-approach-for-novel-energy-and-environmental-solutions/
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:20220222T110000
DTEND;TZID=America/New_York:20220222T120000
DTSTAMP:20260406T052128
CREATED:20220216T145905Z
LAST-MODIFIED:20220216T145905Z
UID:10007087-1645527600-1645531200@seasevents.nmsdev7.com
SUMMARY:ESE Spring Colloquium - "Towards Fair and Efficient Machine Learning with Large Models"
DESCRIPTION:Deep networks often achieve better accuracy as we employ larger models. However\, modern machine learning applications involve multiple considerations alongside accuracy\, such as resource-efficiency\, robustness\, or fairness. Deploying ML in the real-world requires sound solutions addressing these considerations. \nIn this talk\, I will first discuss optimizing fairness objectives for imbalanced data. We observe that a large model can easily achieve “perfect fairness” on training data but dramatically fail at the test-time due to overfitting. To address this\, we propose two strategies\, (1) A new family of fairness-seeking loss functions\, (2) Algorithms that optimize validation (rather than training) objective\, and combine them to achieve state-of-the-art performance. We also introduce new optimization methods that extend these to decentralized settings. \nI will then discuss training efficient sparse models. While conventional wisdom strongly advocates the use of regularization\, we observe that perfectly fitting a large model to data and then pruning it achieves stellar accuracy. We demystify this surprising feature-selection ability through a flexible theory which can answer “How good is the pruned model?”. \nIn summary\, our results provide several insights on learning with large models: (1) Our theory based on linear and random-feature models provide useful intuitions for understanding modern deep learning\, (2) Large models can benefit from unconventional training strategies such as new loss functions\, and (3) Validation phase is particularly helpful for large models that are susceptible to overfitting.
URL:https://seasevents.nmsdev7.com/event/ese-spring-colloquium-towards-fair-and-efficient-machine-learning-with-large-models/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar,Colloquium
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220222T153000
DTEND;TZID=America/New_York:20220222T163000
DTSTAMP:20260406T052128
CREATED:20220208T210700Z
LAST-MODIFIED:20220208T210700Z
UID:10007075-1645543800-1645547400@seasevents.nmsdev7.com
SUMMARY:CIS Seminar: "Modern Fine-grained Algorithms for Classic Problems"
DESCRIPTION:How fast can we solve or approximate classic problems that are known to admit a polynomial time solution? Often times the existing polynomial time algorithms are slow for practical applications. Fine-grained algorithm design aims to better understand the computational complexity of these problems and illustrates tradeoffs between the runtime of the algorithms and the quality of their solutions.\n\nIn this talk\, I will present my work on classic and fundamental problems in fine-grained complexity including edit distance\, longest common subsequence\, pattern matching\, longest increasing subsequence\, and knapsack. In particular\, my talk will cover an algorithm that approximates edit distance within a constant factor in truly subquadratic time. This answers a well-known question in combinatorial pattern matching.
URL:https://seasevents.nmsdev7.com/event/cis-seminar-modern-fine-grained-algorithms-for-classic-problems/
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:20220223T110000
DTEND;TZID=America/New_York:20220223T120000
DTSTAMP:20260406T052128
CREATED:20220211T150239Z
LAST-MODIFIED:20220211T150239Z
UID:10007084-1645614000-1645617600@seasevents.nmsdev7.com
SUMMARY:ESE Spring Colloquium - "Quantum Many-Body Physics in the NISQ Era"
DESCRIPTION:Rapid progress in quantum computing technologies is ushering in a new era for quantum many-body physics. Today’s noisy\, intermediate-scale quantum (NISQ) devices\, while still far from fault-tolerant quantum computers\, are exceptional laboratory systems\, with large many-body Hilbert spaces and unprecedented capabilities for control and measurement. This allows the exploration of quantum dynamics in new far-from-equilibrium regimes\, and motivates new paradigms of phase structure. In this talk I will focus on two such paradigms: eigenstate-ordered phases in periodically driven systems\, and entanglement phases in “monitored” systems\, whose dynamics include projective measurements alongside unitary operations. As an example of the former\, I will discuss the realization of a “discrete time crystal” (DTC) on Google Quantum AI’s Sycamore processor\, focusing on the conceptual challenges involved in detecting the DTC’s signature eigenstate order despite intrinsic limitations of NISQ hardware. I will then present a new window into measurement-induced entanglement phases based on the idea of space-time duality: a transformation that relates unitary and monitored circuits by exchanging the roles of space and time in the dynamics\, which can be implemented on digital quantum simulators through a generalized “quantum teleportation” protocol.
URL:https://seasevents.nmsdev7.com/event/ese-spring-colloquium-quantum-many-body-physics-in-the-nisq-era/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar,Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220223T150000
DTEND;TZID=America/New_York:20220223T160000
DTSTAMP:20260406T052128
CREATED:20220218T161426Z
LAST-MODIFIED:20220218T161426Z
UID:10007090-1645628400-1645632000@seasevents.nmsdev7.com
SUMMARY:Spring 2022 GRASP SFI: Marc Finzi\, New York University\, "Embedding Symmetries and Conservation Laws in Deep Learning Models for Dynamical Systems"
DESCRIPTION:*This will be a HYBRID Event with in-person attendance in Levine 512 and Virtual attendance via Zoom \nIn contrast to traditional control systems where detailed dynamics models are constructed from a mix of physical understanding and empirical data\, machine learning for intuitive physics\, reinforcement learning\, and robotics often takes a hands off approach treating the dynamics as a black box with little to no assumed structure. We show how desirable high level properties like symmetries\, energy and momentum conservation\, and other constraints can be reintroduced into these models to improve generalization. These high level attributes represent prior knowledge about the underlying physics of the system in the Bayesian sense\, and can even be incorporated in a way that does not limit the flexibility of the model.
URL:https://seasevents.nmsdev7.com/event/spring-2022-grasp-sfi-marc-finzi-new-york-university-embedding-symmetries-and-conservation-laws-in-deep-learning-models-for-dynamical-systems/
LOCATION:Levine 512
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:20220223T153000
DTEND;TZID=America/New_York:20220223T163000
DTSTAMP:20260406T052128
CREATED:20220113T034509Z
LAST-MODIFIED:20220113T034509Z
UID:10007010-1645630200-1645633800@seasevents.nmsdev7.com
SUMMARY:CBE Seminar: “Beyond Ethanol: A Process and Systems Engineering Framework for the Design of Advanced Biofuels”
DESCRIPTION:Abstract \nIn my talk\, I will present my work on the application of process and systems engineering techniques to the design of integrated biorefineries that produce middle distillates (jet fuel and diesel). Unlike gasoline\, demand for middle distillates is expected to increase over the next 30 years\, and electrification and decarbonization of sectors where middle distillates are used (such as aviation and freight) is challenging. Biofuels offer a potentially sustainable alternative\, with the upgrading of ethanol to diesel and jet fuel being especially attractive. The ethanol upgrading platform has several advantages: (1) it can make use of available infrastructure; (2) it can be used to produce fuels over the whole distillation spectrum; and (3) it offers multiple chemical alternatives\, enabling the possibility of tailoring the properties of the fuels produced. \nDespite its advantages\, designing an optimal ethanol upgrading strategy is challenging\, as it requires the integration of three different areas: catalysis\, process synthesis\, and fuel property modeling. The challenges associated with the formulation of a framework integrating these areas\, coupled with the large design space characteristic of the problem\, have led scientists to rely on ad hoc approaches. In contrast\, in this talk\, the systematic design of ethanol upgrading biorefineries based on superstructure optimization will be discussed. Four fundamental questions will be addressed: (1) What are the energy requirements associated with the production of middle distillates? (2) What is the interplay among fuel properties\, economics\, and processes? (3) What is the relationship among biorefinery complexity\, processes\, and the fuels obtained? and (4) What is the ability of the advanced fuels identified in this work to satisfy fuel demand and mitigate CO2 emissions?
URL:https://seasevents.nmsdev7.com/event/cbe-seminar-beyond-ethanol-a-process-and-systems-engineering-framework-for-the-design-of-advanced-biofuels/
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:20220223T153000
DTEND;TZID=America/New_York:20220223T163000
DTSTAMP:20260406T052128
CREATED:20220211T010712Z
LAST-MODIFIED:20220211T010712Z
UID:10007078-1645630200-1645633800@seasevents.nmsdev7.com
SUMMARY:CIS Seminar: "Firm Foundations for Private Machine Learning and Statistics"
DESCRIPTION:How can researchers use sensitive datasets for machine learning and statistics without compromising the privacy of the individuals who contribute their data?  In this talk I will describe my work on the foundations of differential privacy\, a rigorous framework for answering this question.  In the past decade\, differential privacy has gone from largely theoretical to widely deployed\, and a theme of the talk will be how new deployments are forcing us to revisit foundational questions about differential privacy.  This talk will cover a range of issues from the fundamental—like optimal private statistical inference—to the applied—like auditing differentially private machine learning.
URL:https://seasevents.nmsdev7.com/event/cis-seminar-firm-foundations-for-private-machine-learning-and-statistics/
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:20220224T110000
DTEND;TZID=America/New_York:20220224T120000
DTSTAMP:20260406T052128
CREATED:20220222T131917Z
LAST-MODIFIED:20220222T131917Z
UID:10007099-1645700400-1645704000@seasevents.nmsdev7.com
SUMMARY:ESE Spring Colloquium - "Provably Robust Algorithms for Prediction and Control"
DESCRIPTION:Feedback-driven decision-making systems are at the emerging frontier of machine learning applications. Upcoming applications of societal consequence\, such as self-driving vehicles and smartwatch-based health interventions\, have to contend with the challenge of operating in reactive stateful environments. In this talk\, I will describe my work on designing principled robust algorithms for feedback-driven learning\, with provable guarantees on computational and statistical efficiency. \nFirst\, I will introduce an efficient instance-optimal algorithm for control in the presence of adversarial disturbances. Beyond the realm of both stochastic and robust control\, such a data-driven notion of optimality combines worst-case guarantees with a promise of exceptional performance on benign instances. Moving on to prediction\, I will present a computationally and statistically efficient forecasting strategy for latent-state dynamical systems exhibiting long term dependencies\, mitigating the statistical challenge of learning with correlated samples\, and the computational difficulties associated with a non-convex maximum likelihood objective. To conclude\, I will discuss some practically relevant fundamental questions at the intersection of machine learning\, optimization\, and control that have the potential to unlock real progress in downstream applications.
URL:https://seasevents.nmsdev7.com/event/ese-spring-colloquium-provably-robust-algorithms-for-prediction-and-control/
LOCATION:Zoom – Meeting ID 958 3045 4776
CATEGORIES:Seminar,Colloquium
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220224T153000
DTEND;TZID=America/New_York:20220224T163000
DTSTAMP:20260406T052128
CREATED:20220211T012014Z
LAST-MODIFIED:20220211T012014Z
UID:10007079-1645716600-1645720200@seasevents.nmsdev7.com
SUMMARY:CIS Seminar: "Learning to Generate Data by Estimating Gradients  of the Data Distribution"
DESCRIPTION:Generating data with complex patterns\, such as images\, audio\, and molecular structures\, requires fitting very flexible statistical models to the data distribution. Even in the age of deep neural networks\, building such models is difficult because they typically require an intractable normalization procedure to represent a probability distribution. To address this challenge\, I propose to model the vector field of gradients of the data distribution (known as the score function)\, which does not require normalization and therefore can take full advantage of the flexibility of deep neural networks. I will show how to (1) estimate the score function from data with flexible deep neural networks and principled statistical methods\, (2) generate new data using stochastic differential equations and Markov chain Monte Carlo\, and even (3) evaluate probabilities as in a traditional statistical model. The resulting method\, called score-based generative modeling\, achieves record-breaking performance in applications including image synthesis\, text-to-speech generation\, time series prediction\, and point cloud generation\, challenging the long-time dominance of generative adversarial networks (GANs) on many of these tasks. Furthermore\, unlike GANs\, score-based generative models are suitable for Bayesian reasoning tasks such as solving ill-posed inverse problems\, and I have demonstrated their superior performance on examples like sparse-view computed tomography and accelerated magnetic resonance imaging. Finally\, I will discuss how score-based generative modeling opens up new opportunities and new future research directions for building better machines to create and understand complex data in various disciplines of science and engineering.
URL:https://seasevents.nmsdev7.com/event/cis-seminar-learning-to-generate-data-by-estimating-gradients-of-the-data-distribution/
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:20220225T100000
DTEND;TZID=America/New_York:20220225T110000
DTSTAMP:20260406T052128
CREATED:20220217T164449Z
LAST-MODIFIED:20220217T164449Z
UID:10007089-1645783200-1645786800@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Microfluidic Devices with Capillary Circuits for User-friendly\, Low-cost\, Multiplexed Point-of-care\, Molecular Diagnostics"
DESCRIPTION:Rapid\, sensitive\, and specific detection is key to personalized medicine and to the prompt implementation of appropriate mitigation measures to reduce disease transmission\, mortality\, morbidity\, and cost. Conventional molecular detection methods require trained personnel and specialized laboratories\, which limits their use to centralized laboratories. Microfluidics enables point-of-care testing. \nIn this talk\, I will show how capillary circuits help automate liquid-distribution and sealing processes and eliminate the need for expensive equipment and highly skilled personnel. Next\, I will present two examples of fully 3D-printed microfluidic devices with capillary valves that were designed\, respectively\, for single-stage and two-stage\, multiplexed isothermal molecular detections of human\, animal\, and plant diseases.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-microfluidic-devices-with-capillary-circuits-for-user-friendly-low-cost-multiplexed-point-of-care-molecular-diagnostics/
LOCATION:Zoom – Email MEAM for Link\, peterlit@seas.upenn.edu
CATEGORIES:Seminar,Doctoral
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220225T103000
DTEND;TZID=America/New_York:20220225T114500
DTSTAMP:20260406T052128
CREATED:20220221T195817Z
LAST-MODIFIED:20220221T195817Z
UID:10007098-1645785000-1645789500@seasevents.nmsdev7.com
SUMMARY:GRASP on Robotics: Jia Deng\, Princeton University\, “Toward Dense 3D Reconstruction in the Wild”
DESCRIPTION:This seminar will be held in person in Wu and Chen Auditorium as well as virtually via Zoom. \nReconstructing depth and motion of every pixel for arbitrary scenes is a core problem in 3D vision with many downstream applications. In this talk\, I will describe some of our recent efforts toward this goal\, including various strategies to obtain effective training data for single-image 3D reconstruction\, and new neural architectures that advance the state of the art of multiview 3D reconstruction.
URL:https://seasevents.nmsdev7.com/event/grasp-on-robotics-jia-deng-princeton-university-toward-dense-3d-reconstruction-in-the-wild-2/
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:20220225T140000
DTEND;TZID=America/New_York:20220225T150000
DTSTAMP:20260406T052128
CREATED:20211109T145824Z
LAST-MODIFIED:20211109T145824Z
UID:10006962-1645797600-1645801200@seasevents.nmsdev7.com
SUMMARY:PICS Colloquium "Driving physics of inverted flag flapping"
DESCRIPTION:Abstract: Fluid-structure interaction (FSI) is ubiquitous in the natural and engineered world\, and a better understanding of FSI systems can aid in the design of renewable energy harvesting technologies\, bio-inspired propulsion vehicles\, and biomedical devices (to name a few applications). In this talk we will investigate “inverted” flag flapping\, in which the flag is clamped at its trailing edge with respect to the incoming uniform flow. This canonical system exhibits a diverse range of behavioral regimes\, including flapping with amplitudes comparable to the flag length\, making it promising for energy harvesting. We will identify the physical mechanisms responsible for the onset of flapping\, the role of vortex shedding in flapping\, and the chaotic flapping regime that the system undergoes for different parameters. We will also characterize the effect of nonuniform flexibility in the dynamics of this beautiful FSI system\, and discuss some efforts for reduced-order modeling of these varied behaviors.
URL:https://seasevents.nmsdev7.com/event/pics-colloquium-driving-physics-of-inverted-flag-flapping/
LOCATION:Zoom – email kathom@seas.upenn.edu
CATEGORIES:Colloquium
ORGANIZER;CN="Penn Institute for Computational Science (PICS)":MAILTO:dkparks@seas.upenn.edu
END:VEVENT
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