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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230710T100000
DTEND;TZID=America/New_York:20230710T110000
DTSTAMP:20260404T100819
CREATED:20230629T143806Z
LAST-MODIFIED:20230629T143806Z
UID:10007603-1688983200-1688986800@seasevents.nmsdev7.com
SUMMARY:MEAM Ph.D. Thesis Defense: "Data-Driven Model Discovery for Non-Equilibrium Phenomena: Unraveling Continuum Behavior from Stochastic Dynamics"
DESCRIPTION:Non-equilibrium phenomena are ubiquitous across material systems and of great technological relevance. Examples of such phenomena include diffusion processes in liquid and gases\, viscoelasticity and plasticity in solids\, and rheological behavior of colloidal and granular media. Despite their ubiquity and importance\, the understanding of non-equilibrium phenomena remains in its infancy compared with classical equilibrium thermodynamics and statistical mechanics from both theoretical and computational aspects. As a consequence\, current modeling and simulation strategies\, including multiscale paradigms\, are mostly trapped within a compromise between computational efficiency and physical fidelity. \nThis thesis leverages recent advances in non-equilibrium physics\, together with emerging machine learning techniques\, to develop theoretical and computational paradigms for learning continuum evolution equations using data-driven methods. First\, we present a new strategy for continuum model discovery that uses fluctuation theorems\, particularly\, the Jarzynski equality\, to identify the reversible (elastic) and irreversible (dissipative) response. Second\, we propose a machine learning architecture called Variational Onsager Neural Networks (VONNs) to learn thermodynamically consistent non-equilibrium evolution PDEs based on Onsager’s variational principle. Thirdly\, we develop a multiscale machine learning framework called Statistical-Physics-Informed Neural Networks (Stat-PINNs) to uniquely determine coarse-grained dissipative evolution equations from stochastic particle dynamics by leveraging fluctuation-dissipation relations. Lastly\, we introduce a statistical mechanics framework with quantified uncertainty to extrapolate material behavior to different loading conditions (including far-from-equilibrium conditions) or material systems.
URL:https://seasevents.nmsdev7.com/event/meam-ph-d-thesis-defense-data-driven-model-discovery-for-non-equilibrium-phenomena-unraveling-continuum-behavior-from-stochastic-dynamics/
LOCATION:Towne 307\, 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:20230710T123000
DTEND;TZID=America/New_York:20230710T143000
DTSTAMP:20260404T100819
CREATED:20230710T121407Z
LAST-MODIFIED:20230710T121407Z
UID:10007607-1688992200-1688999400@seasevents.nmsdev7.com
SUMMARY:ESE PhD Thesis Defense: "A Robot's Search for Meaning: Semantics as a Common Representation for Heterogeneous Robot State Estimation and Collaboration"
DESCRIPTION:Mapping and navigation have gone hand-in-hand since long before robots existed. For almost as long\, maps have also been a key form of communication\, allowing someone who has never been to an area to nonetheless navigate that area successfully. In the context of multi-robot systems\, the maps and information that flow between robots are what enables effective collaboration\, whether those robots are operating simultaneously or years apart in time. In this thesis\, we argue that maps must go beyond encoding purely geometric or color information in order to enable increasingly complex autonomy\, particularly between robots. We propose systems for mapping and localization\, showing that semantic maps can be an important end in themselves as well as a means to achieve improved global localization in a variety of contexts. We then build on these ideas and employ semantic maps to underly a framework for multi-robot autonomy\, focusing in particular on air and ground robots. A distinguishing characteristic of this thesis is that we strongly emphasize field experiments and testing\, and we demonstrate that these ideas can work at scale in the real world. We also perform extensive simulation experiments to validate our ideas at even larger scales. These experiments and systems constitute a step forward in large-scale\, collaborative multi-robot systems operating with real communication\, navigation\, and perception constraints.
URL:https://seasevents.nmsdev7.com/event/ese-phd-thesis-defense-a-robots-search-for-meaning-semantics-as-a-common-representation-for-heterogeneous-robot-state-estimation-and-collaboration/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 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:20230710T130000
DTEND;TZID=America/New_York:20230710T140000
DTSTAMP:20260404T100819
CREATED:20230616T131612Z
LAST-MODIFIED:20230616T131612Z
UID:10007587-1688994000-1688997600@seasevents.nmsdev7.com
SUMMARY:PSOC@Penn Seminar: Larry Dooling
DESCRIPTION:
URL:https://seasevents.nmsdev7.com/event/psocpenn-seminar-larry-dooling/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar,Postdoctoral
ORGANIZER;CN="PSOC":MAILTO:manu@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230710T143000
DTEND;TZID=America/New_York:20230710T163000
DTSTAMP:20260404T100819
CREATED:20230627T140337Z
LAST-MODIFIED:20230627T140337Z
UID:10007598-1688999400-1689006600@seasevents.nmsdev7.com
SUMMARY:BE Doctoral Dissertation Defense: "The roles of YAP and TAZ in fetal bone development" (Joseph Collins)
DESCRIPTION:The Department of Bioengineering at the University of Pennsylvania and Dr. Joel Boerckel are pleased to announce the Doctoral Dissertation Defense of Joseph Collins.\n \nTitle: The roles of YAP and TAZ in fetal bone development\n\nDate: July 10\, 2023 \nTime: 2:30pm\nLocation: CRB Austrian Auditorium\n\nThe public is welcome to attend.\n\n\nJoin Zoom Meeting\nhttps://us02web.zoom.us/j/4136654254?pwd=YWNLamdUKys4L09yWVVVU0NxTXBSQT09 \nMeeting ID: 413 665 4254\nPasscode: 01093
URL:https://seasevents.nmsdev7.com/event/9295/
LOCATION:CRB Auditorium\, 415 Curie Boulevard\, Philadelphia\, PA\, 19104\, United States
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:20230711T100000
DTEND;TZID=America/New_York:20230711T113000
DTSTAMP:20260404T100819
CREATED:20230628T203907Z
LAST-MODIFIED:20230628T203907Z
UID:10007601-1689069600-1689075000@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Semantic Localization\, Mapping\, and Exploration by Multiple Aerial Robots"
DESCRIPTION:Traditional approaches for active mapping focus on building geometric maps. For most real-world applications\, however\, actionable information is related to semantically meaningful objects in the environment. We propose an approach to the active metric-semantic mapping problem that enables multiple heterogeneous robots to collaboratively build a map of the environment. The robots actively explore to minimize the uncertainties in both semantic(object classification) and geometric (object modeling) information. We represent the environment using informative but sparse object models\, each consisting of a basic shape and a semantic class label\, and characterize uncertainties empirically using a large amount of real-world data. Given a prior map\, we use this model to select actions for each robot to minimize uncertainties. The performance of our algorithm is demonstrated through multi-robot experiments in diverse real-world environments. The proposed framework is applicable to a wide range of real-world problems\, such as precision agriculture\, infrastructure inspection\, and asset mapping in factories.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-semantic-localization-mapping-and-exploration-by-multiple-aerial-robots/
LOCATION:Moore 212
CATEGORIES:Seminar
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230711T100000
DTEND;TZID=America/New_York:20230711T120000
DTSTAMP:20260404T100819
CREATED:20230627T141221Z
LAST-MODIFIED:20230627T141221Z
UID:10007599-1689069600-1689076800@seasevents.nmsdev7.com
SUMMARY:BE Doctoral Dissertation Defense: "Macrophages & chromosomal instability: From unraveling immunomodulatory interactions to effects of chromosomal instability on macrophage-mediated anti-tumor response" (Brandon Hayes)
DESCRIPTION:The Department of Bioengineering at the University of Pennsylvania and Dr. Dennis Discher are pleased to announce the Doctoral Dissertation Defense of Brandon Hayes.\n\n\n\nTitle: Macrophages & chromosomal instability: From unraveling immunomodulatory interactions to effects of chromosomal instability on macrophage-mediated anti-tumor response\n\n\nDate: July 11\, 2023\nTime: 10:00am\nLocation: Berger Auditorium in Skirkanich\n\nThe public is welcome to attend.\n\nZoom option:\nhttps://upenn.zoom.us/j/92480264650?pwd=d0JwaHBwejRkaThvTFovb0RLZVVvQT09 \n\nMeeting ID: 924 8026 4650\nPasscode: 834477
URL:https://seasevents.nmsdev7.com/event/be-doctoral-dissertation-defense-macrophages-chromosomal-instability-from-unraveling-immunomodulatory-interactions-to-effects-of-chromosomal-instability-on-macrophage-mediated-anti-tumor-respons/
LOCATION:Berger Auditorium (Room 13)\, Skirkanich Hall\, 210 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
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:20230712T093000
DTEND;TZID=America/New_York:20230712T103000
DTSTAMP:20260404T100819
CREATED:20230629T134714Z
LAST-MODIFIED:20230629T134714Z
UID:10007602-1689154200-1689157800@seasevents.nmsdev7.com
SUMMARY:MEAM Ph.D. Thesis Defense: "Transport and Mixing with Swimming Microorganisms in Chaotic Flows"
DESCRIPTION:Microorganisms\, primitive unicellular forms of life\, form the basis of the food web and play crucial roles in the Earth’s biogeochemical cycles. Habitats of microorganisms\, from oceans and lakes to soil and human intestines\, are often characterized by constant fluid motion. Fluid flow exerts forces and torques on microorganisms that affect their movement and distribution\, and transports essential chemicals on which they rely for sensing\, foraging\, and mating. As a result\, flow has a broad range of effects on the behaviors of microorganisms\, including their locomotion\, reproduction\, nutrient uptake\, and communication. Despite many efforts to understand microbiology in aquatic environments\, it remains a challenge to interpret the physical and biological behaviors of microorganisms in the presence of fluid flows\, particularly unsteady and chaotic flows. \nIn this thesis\, I investigate the interaction between motile microorganisms and dynamical structures in chaotic flows\, and the effects of such interaction on transport and mixing. The flow dynamical structures investigated here are known as the Lagrangian coherent structures (LCSs). First\, I characterize the transport and mixing in a spatially periodic chaotic flow with swimming Escherichia coli. The microorganisms are found to align and accumulate near structures of strong stretching of fluid parcels\, or namely\, the hyperbolic LCSs. Such alignment and accumulation of microorganisms lead to reduction in large-scale transport but enhancement in small-scale mixing. Second\, I examine the transport and mixing with E. coli in a more complex spatially aperiodic chaotic flow. The microorganisms are found to escape and deplete in vortex-like dynamical structures known as the elliptic LCSs. The depletion leads to enhanced transport barriers into which the transport of diffusive chemicals is much slower. Lastly\, I investigate the mixing in the self-generated chaotic flows of swarming Serratia marcescens and show that dilute polymers can substantially enhance mixing induced by collective behaviors. Overall\, this dissertation elucidates the nontrivial effects of the interaction between microorganisms and flow structures on transport and mixing.
URL:https://seasevents.nmsdev7.com/event/meam-ph-d-thesis-defense-transport-and-mixing-with-swimming-microorganisms-in-chaotic-flows/
LOCATION:Moore 212
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:20230714T110000
DTEND;TZID=America/New_York:20230714T130000
DTSTAMP:20260404T100819
CREATED:20230627T161203Z
LAST-MODIFIED:20230627T161203Z
UID:10007600-1689332400-1689339600@seasevents.nmsdev7.com
SUMMARY:ESE PhD Thesis Defense: "Discrete and Continuous Optimization for Collaborative and Multi-task Learning"
DESCRIPTION:This thesis is dedicated to addressing the challenges of robust collaborative learning and optimization in both discrete and continuous domains. With the ever-increasing scale of data and the growing demand for effective distributed learning\, a multitude of obstacles emerge\, including communication limitations\, resilience to failures and corrupted data\, limited information access\, and collaboration in multi-task learning scenarios. The thesis consists of seven chapters\, each targeting specific aspects of these challenges. \nIn the first chapter\, novel algorithms are introduced for collaborative linear bandits\, offering a comprehensive exploration of the benefits of collaboration in the presence of adversaries through thorough analyses and lower bounds. The second chapter delves into multi-agent min-max learning problems by tackling the presence of Byzantine adversarial agents. Chapter three delves into the effects of delays within stochastic approximation schemes\, investigating non-asymptotic convergence rates under Markovian noise. \nMoving forward\, the fourth chapter focuses on analyzing the performance of standard min-max optimization algorithms with delayed updates. The fifth chapter concentrates on robustness in discrete learning\, specifically addressing convex-submodular problems in mixed continuous-discrete domains. The sixth chapter tackles the challenge of limited information access in collaborative problems with distributed constraints\, developing optimal algorithms for submodular maximization under distributed partition matroid constraints. \nLastly\, the seventh chapter introduces a discrete variant of multi-task learning and meta-learning. In summary\, this thesis contributes to the field of robust collaborative learning and decision-making by providing insights\, algorithms\, and theoretical guarantees in discrete and continuous optimization. The advancements made across linear bandits\, minimax optimization\, distributed robust learning\, delayed optimization\, and submodular maximization pave the way for future developments in collaborative and multi-task learning.
URL:https://seasevents.nmsdev7.com/event/ese-phd-thesis-defense-discrete-and-continuous-optimization-for-collaborative-and-multi-task-learning/
LOCATION:Room 452 C\, 3401 Walnut\, 3401 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:20230714T120000
DTEND;TZID=America/New_York:20230714T133000
DTSTAMP:20260404T100819
CREATED:20230703T125221Z
LAST-MODIFIED:20230703T125221Z
UID:10007605-1689336000-1689341400@seasevents.nmsdev7.com
SUMMARY:CBE PhD Thesis Defense: "BIJEL-Templated Advanced Functional Materials for a Sustainable Future"
DESCRIPTION:Advanced functional materials comprising multiple components with nano- and microscopic features typically rely on expensive and time-consuming fabrication methods. Kinetically-trapped disordered structures provide a powerful alternate route to fabricate fault-tolerant\, multi-component functional structures at scale. Developing such structures with controllable features that could serve as materials templates for various applications is of great importance. Bicontinuous interfacially jammed emulsion gels (bijels) are kinetically trapped disordered biphasic materials that can be converted to porous materials with tunable features. The self-assembled nature of the bijel structure\, coupled with its remarkable flexibility in terms of features and constituent materials\, positions it as a highly promising material for the advancement of functional porous materials. However\, the practical utilization of bijels is hindered by several challenges that need to be addressed. This study aims to overcome these challenges by simplifying the fabrication process\, improving control over bijel features\, and demonstrating their practical functions. To achieve scalable fabrication\, the vaporization induced phase separation (VIPS) method is introduced\, enabling bijel production under ambient conditions. The importance of quenching kinetics is also revealed\, leading to the achievement of bijels with uniform sub-micrometer domains through regulated co-solvent removal. Furthermore\, the unique structure of bijels allows for intriguing interactions with light\, fluids\, and electrons. This is exemplified by the successful development of bijel templated passive radiative cooling coatings and carbon air cathodes\, showcasing the exceptional functionality of bijels.
URL:https://seasevents.nmsdev7.com/event/cbe-phd-thesis-defense-bijel-templated-advanced-functional-materials-for-a-sustainable-future/
LOCATION:Towne 225
CATEGORIES:Doctoral,Dissertation or Thesis Defense
ORGANIZER;CN="Chemical and Biomolecular Engineering":MAILTO:cbemail@seas.upenn.edu
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