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DTSTART;TZID=America/New_York:20240703T101500
DTEND;TZID=America/New_York:20240703T111500
DTSTAMP:20260403T154316
CREATED:20240626T130018Z
LAST-MODIFIED:20240626T130018Z
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SUMMARY:MEAM Seminar: "Fingertip Friction\, Materials\, and Tactile Perception"
DESCRIPTION:Sliding touch is one of the key inputs for the perception of materials in our environment. We are interested in the contribution of fingertip friction to the process of tactile exploration and perception. Psychophysical studies address correlations between subjective judgements of perception and measured physical parameters of interactions\, in our case of friction on materials with systematically varied surface structure. A challenge in this approach are large variances in skin physiological parameters between the participants in our studies. I will discuss how surface structures are perceived in sliding touch\, for which materials the determination of physiological parameters is helpful to predict friction\, and if humans are able to rank friction differences correctly. Materials range from 3D printed plastic over micro-structured rubber to honey.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-fingertip-friction-materials-and-tactile-perception/
LOCATION:Room 337\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Colloquium
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240708T150000
DTEND;TZID=America/New_York:20240708T163000
DTSTAMP:20260403T154316
CREATED:20240624T192550Z
LAST-MODIFIED:20240624T192550Z
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SUMMARY:CBE Doctoral Dissertation Defense: "Metal Oxides and Ion-Modification: A Study in Chemical Stability\, Transformations\, and Dynamics" (Bader A. Alayyoub)
DESCRIPTION:Abstract: \n\n\n\nMetal oxides\, both simple and complex\, are essential for catalysis and energy conversion due to their versatile physicochemical properties and are often enhanced through ion doping\, a well-established strategy to improve their functionality. However\, challenges remain\, particularly in accurately characterizing the surface stability of such oxides beyond idealized conditions and in understanding the thermodynamics and kinetics associated with ion doping and migration. This thesis combines first-principles methods\, including density functional theory\, with molecular dynamics and enhanced sampling techniques to provide atomic-level insights into intrinsic and dopant ionic behaviors and dynamics within metal oxides. Additionally\, in the context of heterogeneous catalysis\, it aims to establish a pathway to guide future design improvements and enhance their chemical properties. The composition and stability of metal oxide surfaces are essential in shaping their properties. This thesis focuses on SrTiO3 and examines entropic effects by incorporating phonon contributions and utilizing established thermodynamic principles. It maps the free energies of different surface structures across temperatures\, finding that phonon effects on surface morphology at higher temperatures cannot be ignored. This discovery strengthens the link between experiments and theory. Furthermore\, the interplay between titanate-based perovskite oxides and transition metal ions through doping and surface adsorption forms a central focus of this work. Although these interactions are generally examined for their bulk doping properties\, surface compositions can differ significantly. Through a systematic evaluation of the thermodynamics of 4d-transition metal doping\, migration\, and segregation with and without host strain effects\, this research provides a high-throughput initial assessment of their thermal stability and synthesis feasibility. Another aspect of ion-perovskite interaction involves the thermodynamics of Ni egress and ingress across different SrTiO3 facets. The findings reveal a pronounced egress propensity from the (110) facet\, which is uniquely significant for advancing novel catalyst synthesis methods like exsolution. Similarly\, the thermodynamic impacts of Pd\, Pt\, and Rh migrating to the surface of LaFeO3 are explored\, revealing that these metal ions can form surface alloys and alter the reducibility of surface oxygens\, thereby modifying surface properties compared to the bulk material. Beyond thermodynamic analysis\, ion surface migration and dissolution dynamics are explored through ab initio molecular dynamics and enhanced sampling methods. Modeling the exsolution of Pt from various perovskite titanate hosts reveals that Pt diffusion to the surface varies with the host material and specific exposed facets\, with distinct temperatures required to overcome the kinetic barriers for exsolution. These insights inform predictions for the synthesis of supported metal catalysts. Additionally\, ion dissolution dynamics and surface stability at oxide-solvent interfaces are investigated using metadynamics\, focusing on a CaO-water interface to provide insights into phenomena such as catalyst surface leaching contributing to catalyst degradation. Collectively\, these analyses provide a comprehensive atomistic understanding of complex metal oxides and establish a framework for furthering their strategic design.
URL:https://seasevents.nmsdev7.com/event/cbe-doctoral-dissertation-defense-metal-oxides-and-ion-modification-a-study-in-chemical-stability-transformations-and-dynamics-bader-a-alayyoub/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Doctoral,Dissertation or Thesis Defense
ORGANIZER;CN="Chemical and Biomolecular Engineering":MAILTO:cbemail@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240709T101500
DTEND;TZID=America/New_York:20240709T111500
DTSTAMP:20260403T154316
CREATED:20240625T190811Z
LAST-MODIFIED:20240625T190811Z
UID:10007996-1720520100-1720523700@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Metallivore Robots Powered by Aluminum-air Batteries"
DESCRIPTION:Mobile robots have shown significant advancements in agility\, intelligence\, and efficiency over the past few decades. However\, their endurance and overall performance remain limited by the onboard power supplies. Current power sources typically restrict mobile robots to areas close to the electrical grid and necessitate heavier batteries for extended range. Energy refueling could be significantly challenging in remote and inaccessible regions\, and traditional energy harvesting methods have also proven inadequate for sustaining continuous operations. To address this challenge\, we propose an alternative approach: enabling robots to “digest” energy-dense metals to generate power\, analogous to how animals consume food. This concept is based on the working principle of an aluminum-air battery\, which operates like a small chemical plant converting aluminum into electricity. This method could dramatically reduce the trade-off between endurance and battery weight\, thus unleashing new potential for mobile robotics. This talk will begin by quantifying the energy gap between mobile robots and their biological counterparts based on a comparative analysis of their energy density and power density. I will then highlight our recent progress in developing metallivore robots\, particularly addressing how we have overcome various challenges associated with conventional aluminum-air batteries\, such as byproduct accumulation\, hydrogen production\, and water consumption. Lastly\, I will showcase our development of a highly stretchable metal-air battery using sliding electrodes\, making it ideal for integration into soft robotics.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-metallivore-robots-powered-by-aluminum-air-batteries/
LOCATION:Room 337\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar,Doctoral
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240709T130000
DTEND;TZID=America/New_York:20240709T150000
DTSTAMP:20260403T154316
CREATED:20240507T173053Z
LAST-MODIFIED:20240507T173053Z
UID:10007965-1720530000-1720537200@seasevents.nmsdev7.com
SUMMARY:CBE Doctoral Dissertation Defense: "Path-Sampling and Machine Learning for Rare Abnormal Safety and Reliability Events" (Vikram Sudarshan)
DESCRIPTION:Abstract: \nIt is crucial for chemical and manufacturing industries to ensure safe and reliable operation of their plants and processes\, by mitigating safety issues (e.g.\, extreme operating conditions) and reliability issues (e.g.\, production losses). But\, a significant challenge faced by these industries is that such events are rare and undesirable\, with little occurrence data available from process historians. Extensive control and alarm systems\, with Safety Instrumented Systems (SIS) and reliability risk assessment methods\, are often successful in mitigating postulated abnormal events anticipated in HAZOPs. However\, it is very challenging to consider the effects of highly infrequent unpostulated abnormal events (i.e.\, non-specific\, randomly-occurring events)\, which cannot be anticipated in process design\, and lead to severe consequences. Hence\, in this thesis\, novel\, improved multivariate alarm systems are developed using path-sampling and machine learning\, for handling rare unpostulated abnormal events resulting from random perturbations in one or more process variables. \nAs a first application of path-sampling to analyze rare abnormal events for chemical process safety\, Moskowitz (2016 ) introduced transition path-sampling (TPS) to locate rare safety pathways for an exothermic CSTR and an air separation unit (ASU). In this thesis\, to circumvent the computational limitations posed by TPS\, forward-flux sampling (FFS) is introduced. It simulates rare unpostulated abnormal events more-efficiently in a piecewise manner\, moving from desirable to undesirable operating regions\, with valuable key process-variable data stored during the simulations\, followed by calculations of committer probabilities to reach undesirable regions (i.e.\, p B ). \nGiven the process variable-p B data\, accurate predictive models are developed using machine learning (ML) – one of the cornerstones of Industry 4.0’s vision for increased automation. Using predictions provided by the ML-based models\, initial multivariate alarm systems are developed\, which are improved significantly by introducing an alarm rationalization- dynamic risk analyses (DRAn) integrated framework. Such improved systems\, when implemented alongside widely-used HAZOP studies\, aid operators in handling both postulated and unpostulated abnormal events to improve overall safety and reliability.
URL:https://seasevents.nmsdev7.com/event/cbe-doctoral-dissertation-defense-path-sampling-and-machine-learning-for-rare-abnormal-safety-and-reliability-events-vikram-sudarshan/
LOCATION:Towne 336\, 220 S 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Doctoral,Dissertation or Thesis Defense
ORGANIZER;CN="Chemical and Biomolecular Engineering":MAILTO:cbemail@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240709T130000
DTEND;TZID=America/New_York:20240709T150000
DTSTAMP:20260403T154316
CREATED:20240705T134714Z
LAST-MODIFIED:20240705T134714Z
UID:10008005-1720530000-1720537200@seasevents.nmsdev7.com
SUMMARY:ESE Ph.D. Thesis Defense: "Fair and Generalizable Machine Learning for Neuroimaging"
DESCRIPTION:Machine learning has been widely adopted to medical imaging research\, yet it suffers from domain shift for real world applications. Due to the heterogeneity of medical data\, machine learning-based diagnostic models are also prone to biases. In this thesis\, we start from arguing the necessity of domain adaptation to achieve the optimal performance for each subcategory. We develop an adaptation algorithm which doesn’t require any ground-truth labels from the unseen domain. We also discuss the value of handcrafted imaging features in the representation learning era for brain imaging application. Next\, we show that machine learning-based diagnostic models can be unbiased if they are trained using rigorous data pre-processing techniques and well-constructed models. We find that multi-source data is helpful sometimes in elevating both model performance consistency and precision. Finally\, we introduce a weighted-empirical risk minimization algorithm to further boost the model’s performance on unseen data distribution by only using a few samples. We demonstrate the empirical results on large-scale diverse-population brain imaging datasets for rich clinical tasks.
URL:https://seasevents.nmsdev7.com/event/ese-ph-d-thesis-defense-fair-and-generalizable-machine-learning-for-neuroimaging/
LOCATION:Zoom – Meeting ID 3394168579
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:20240709T150000
DTEND;TZID=America/New_York:20240709T180000
DTSTAMP:20260403T154316
CREATED:20240702T124633Z
LAST-MODIFIED:20240702T124633Z
UID:10008002-1720537200-1720548000@seasevents.nmsdev7.com
SUMMARY:ESE Ph.D. Thesis Defense: "Graph Machine Learning under Requirements"
DESCRIPTION:Graphs are powerful mathematical tools that enable modeling of complex systems. Graph machine learning exploits possibly unknown data structures\, which provides a unified approach to tackle a wide variety of problems. However useful in practice\, graph machine learning solutions tend to suffer from three main limitations: they do not scale with the size of the graph\, they are not robust to changes of the graph\, and they require an homogeneous underlying graph. In this thesis we address all three of these requirements. In terms of scalability\, we show that Graph Neural Networks (GNNs) improve their generalization capabilities with the number of nodes. This positive result with ample empirical evidence\, motivates the need for scalable training solutions. To this end\, I developed two strategies to train GNNs on large scale graphs\, first by growing the graphs in time as we train\, and second by distributing the graph in a set of machines and growing the graph in space. These two methods alleviate the computational costs required to train GNNs on large scale graphs\, without compromising the accuracy. In terms of robustness\, even though many data modalities reside in a very high-dimensional space\, their dynamics can be assumed to belong to a lower-dimensional structure. We can model the low dimensional space using a graph Laplacian\, and show that the problem of learning a Lipschitz continuous function on a manifold is equivalent to a dynamically weighted manifold regularization problem. Heterogeneity is a fundamental property of networks. Even if a network is composed of homogeneous agents\, every agent will have different interactions with the environment\, which will translate into heterogeneous data acquisition. Graph machine learning solutions that do not address the heterogeneous nature of the data tend to only allocate the needs of some of the nodes in the graph. I propose two ways to tackle heterogeneity\, a single solution to improve the outcome of all the individual components in the network\, and a node-specific solution such that every agent in the network improves upon working with their individual data.
URL:https://seasevents.nmsdev7.com/event/ese-ph-d-thesis-defense-graph-machine-learning-under-requirements/
LOCATION:Greenberg Lounge (Room 114)\, Skirkanich Hall\, 210 South 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:20240710T110000
DTEND;TZID=America/New_York:20240710T120000
DTSTAMP:20260403T154316
CREATED:20240625T125853Z
LAST-MODIFIED:20240625T125853Z
UID:10007994-1720609200-1720612800@seasevents.nmsdev7.com
SUMMARY:ESE Seminar: "Designing emerging computing systems with ferroelectric devices"
DESCRIPTION:This talk will present a brief overview of advances in ferroelectric devices and their integration into computing systems to provide novel functionality and energy efficiency in various data intensive applications. The talk will emphasize on cross-stack design opportunities in designing stacked intelligent 3D memory systems.
URL:https://seasevents.nmsdev7.com/event/ese-seminar-designing-emerging-computing-systems-with-ferroelectric-devices/
LOCATION:Greenberg Lounge (Room 114)\, Skirkanich Hall\, 210 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240711T120000
DTEND;TZID=America/New_York:20240711T130000
DTSTAMP:20260403T154316
CREATED:20240614T134645Z
LAST-MODIFIED:20240614T134645Z
UID:10007988-1720699200-1720702800@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Examples of Elasto-Capillary Mediated Interactions on Thin Elastic Film"
DESCRIPTION:I plan to tell two short stories. The first story is about a new drop fluidics that uses a deformable and stretchable elastomeric film as the platform. Such a soft film decorated with magnetic particles can be deformed locally with an external magnetic field. When these deformations are coupled with capillary force\, small liquid drops can be moved over the platform over long distances efficiently and accurately. The resulting drop fluidics could be suitable for the digital control of drop motion by simply switching on and off the local magnetic fields applied at different positions underneath the elastomeric film in a Boolean sequence. \nThe second story is about the interaction of two beads on the surface of a thin elastic film supported on a pool of liquid. Here\, the excess energy of the surface due to the curvature of the stretched film induces attraction of the beads. It is found that the hysteresis of adhesion between the beads and the elastic film retard the motion of the objects\, which\, however\, can be reduced by introducing a thin hydrogel layer atop the elastic film. Morphological instabilities in the gel create corrugated paths to the motion\, thus causing the particles to aggregate along the patterns thus developed. These observations suggest that a deformable elastic film affords an effective model system to study elasticity and mediated interaction of particles on its surface.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-examples-of-elasto-capillary-mediated-interactions-on-thin-elastic-film/
LOCATION:Levine 307\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240716T101500
DTEND;TZID=America/New_York:20240716T111500
DTSTAMP:20260403T154316
CREATED:20240703T171655Z
LAST-MODIFIED:20240703T171655Z
UID:10008004-1721124900-1721128500@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Tribosintering of Metal Oxide Nanocrystal Antiwear Additives: Growth\, Wear\, and Modeling"
DESCRIPTION:Machinery relies on lubrication to regulate the friction and wear at contacting interfaces. As new lubricants become less viscous to comply with environmental and cost-saving standards\, the risk of surface-initiated machine failure grows. We show that metal oxide additives in low-viscosity lubricants will form protective coatings\, or tribofilms\, in situ at contacting interfaces. Compared to state-of-the-art surface coatings and anti-wear additives\, metal oxide coatings have several practical benefits and environmental advantages\, but their formation mechanism is not well-understood. We will first discuss the mechanistic and practical implications of the coatings’ cooperativity of with S- and P-based co-additives\, then the interplay between the coatings’ growth on and their removal from contacting machinery. Finally\, we will discuss data-driven and traditional modeling efforts to develop predictive models for these antiwear coatings.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-tribosintering-of-metal-oxide-nanocrystal-antiwear-additives-growth-wear-and-modeling/
LOCATION:Room 337\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar,Doctoral
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240717T100000
DTEND;TZID=America/New_York:20240717T110000
DTSTAMP:20260403T154316
CREATED:20240709T153759Z
LAST-MODIFIED:20240709T153759Z
UID:10008006-1721210400-1721214000@seasevents.nmsdev7.com
SUMMARY:BE Doctoral Dissertation Defense: "INVESTIGATION OF IMAGE QUALITY SPATIAL DEPENDENCY IN RELATION TO ACQUISITION GEOMETRY IN DIGITAL BREAST TOMOSYNTHESIS" (Chloe Jeongin Choi)
DESCRIPTION:The Department of Bioengineering at the University of Pennsylvania and Dr. Andrew Maidment are pleased to announce the Doctoral Dissertation Defense of Chloe Jeongin Choi.\n\nTitle: INVESTIGATION OF IMAGE QUALITY SPATIAL DEPENDENCY IN RELATION TO ACQUISITION GEOMETRY IN DIGITAL BREAST TOMOSYNTHESIS\n \nDate: Wednesday\, July 17\, 2024\nTime: 10:00am\nLocation: Reunion auditorium\, JMB.  
URL:https://seasevents.nmsdev7.com/event/be-doctoral-dissertation-defense-investigation-of-image-quality-spatial-dependency-in-relation-to-acquisition-geometry-in-digital-breast-tomosynthesis-chloe-jeongin-choi/
LOCATION:JMB Reunion Auditorium\, 3620 Hamilton Walk\, 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:20240717T130000
DTEND;TZID=America/New_York:20240717T140000
DTSTAMP:20260403T154316
CREATED:20240702T175343Z
LAST-MODIFIED:20240702T175343Z
UID:10008003-1721221200-1721224800@seasevents.nmsdev7.com
SUMMARY:IC2S2 Conference Virtual Keynote: "Societies are complex systems. So\, how to manage them?"
DESCRIPTION:To kick off the IC2S2 conference being held right here at the University of Pennsylvania\, we warmly invite you to join us for Dirk Helbing’s (ETH Zurich) Virtual Keynote address “Societies are complex systems. So\, how to manage them?\, to be given at 1 pm ET / 7 pm CET on Wednesday\, July 17th. The talk will be at this Zoom link: \nhttps://upenn.zoom.us/j/97394867827?pwd=a3jRWgWJT56JFL6edF9DyAQjEbPYEr.1 \nYou do not have to register to attend the virtual keynote. However\, if you wish to attend the in-person sessions\, please register by Wednesday\, July 3rd at: https://ic2s2-2024.org/register
URL:https://seasevents.nmsdev7.com/event/ic2s2-conference-virtual-keynote-societies-are-complex-systems-so-how-to-manage-them/
LOCATION:Zoom
ORGANIZER;CN="Computer and Information Science":MAILTO:cherylh@cis.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240717T143000
DTEND;TZID=America/New_York:20240717T153000
DTSTAMP:20260403T154316
CREATED:20240711T174508Z
LAST-MODIFIED:20240711T174508Z
UID:10008017-1721226600-1721230200@seasevents.nmsdev7.com
SUMMARY:BE Doctoral Dissertation Defense: "Knowledge-Integrated Learning for AI in Medical Image Analysis" (Yifan Wu)
DESCRIPTION:The Department of Bioengineering at the University of Pennsylvania and Dr. Jim Gee are pleased to announce the Doctoral Dissertation Defense of Yifan Wu.\n\nTitle: Knowledge-Integrated Learning for AI in Medical Image Analysis\nDate: July 17\, 2024\, Wednesday\nTime: 2:30 PM to 4:40 PM\nLocation: Towne Room 337 \nZoom option: https://upenn.zoom.us/j/96378457553?pwd=Zf5UWdZmA87iVawEPnGmvMOOURbNDo.1\n\nThe public is welcome to attend.
URL:https://seasevents.nmsdev7.com/event/be-doctoral-dissertation-defense-knowledge-integrated-learning-for-ai-in-medical-image-analysis-yifan-wu/
LOCATION:Towne 337
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:20240718T110000
DTEND;TZID=America/New_York:20240718T120000
DTSTAMP:20260403T154316
CREATED:20240627T185835Z
LAST-MODIFIED:20240627T185835Z
UID:10008001-1721300400-1721304000@seasevents.nmsdev7.com
SUMMARY:BE/PICS Joint Seminar: Uma Balakrishnan and Kunal Poorey\, Sandia National Laboratories
DESCRIPTION:These talks will be held jointly\, with each talk 30 minutes each (1 hour total). \nTalk 1: “Optimizing Anomaly Detection for GenAI based Digital Twins of Wearable Data” (Uma Balakrishnan) \nIn this presentation\, we introduce a methodology that utilizes both real and synthetic datasets (digital twins) to address the uncertainties associated with anomaly detection thresholds in health data from wearables. By integrating state-of-the-art wearables using generative AI\, and sophisticated anomaly detection techniques\, our approach offers a precise and comprehensive understanding of potential health issues\, significantly reducing the false negative rate. Enhancing real datasets with generative AI-based digital twins increases population size and achieves strong concordance in uncertainty analysis with results obtained from real data alone. This robust concordance is consistent even when applied to small village populations\, showcasing the scalability and reliability of our generative algorithm. Validating synthetic users (digital twins) by comparing their statistical signatures with real datasets confirms the effectiveness of our approach. Our methodology promises to revolutionize healthcare data collection and address privacy concerns by providing a more comprehensive and reliable health assessment tool for early detection of biothreats or pandemics. Moreover\, we have developed a versatile anomaly detection method based on the fourth-order moments of physiological parameters\, applicable to a wide range of datasets and compatible with various healthcare data sources\, including wearables. Our goal is to empower individuals and healthcare systems with advanced tools for real-time anomaly detection and enhanced health assessment\, paving the way for improved public health outcomes. \nTalk 2: “AI-aided Computational Methods to Overcome Challenges in Biology and Engineering” (Kunal Poorey) \nEarly detection of an emerging biothreat and robust supply of relevant medical counter measures\, coupled with the capacity for developing novel therapeutics in response to emerging diseases\, is vital for enhancing national healthcare resilience. Challenges such as supply chain disruptions\, propriety formulations\, drug resistance\, and reliance on single-source raw materials or the products itself can hinder access to proper medical countermeasures. Today\, advancements in artificial intelligence and machine learning are revolutionizing detection\, diagnostics\, and intervention strategies across all sectors of science and technology. Here we will discuss data science applications including Generative AI can early detect an emerging threat and accelerate drug discovery. \nWe have developed a cutting-edge computational strategy for the early detection of emerging biothreats such as infectious outbreaks (natural or intentional). Our approach utilizes advanced anomaly detection techniques applied to a diverse array of health datasets\, including wearable technology\, healthcare site data\, Google search terms\, and Twitter feeds. These sources are analyzed at multiple scales\, from individual health to broader population health dynamics\, to identify and notify anomalies at multiple resolutions. \nFurthermore\, we will discuss how machine learning (ML) aided drug discovery significantly reduces bottlenecks by enhancing both the speed and cost-effectiveness of the process. By using extensive datasets\, ML algorithms identify potential drugs\, predict interactions with biological targets\, and optimize chemical properties for effective manufacturing. New drug development also aids in improving treatment efficacy and reducing development costs. There are still needs in developing “explainable” machine learning (XML) methods that enhances our understanding of structure-property relationships\, aiding in the optimized design of effective and safer drugs by understanding model recommendations\, identifying new drug targets\, and predicting side effects. Additionally\, we explore the potential of generative artificial intelligence (GenAI) in leveraging this knowledge to further enhance accelerated drug development. We introduce MIRA (Machine Intelligence for Rapid Acceleration of Drug Discovery and Repurposing)\, an innovative model combining GenAI tools and XML for accelerated drug discovery. MIRA integrates state-of-the-art GenAI models capable of conditionally generating drug-like molecules. And utilizing publicly available databases\, we’ve compiled a database of drug compounds and their properties\, such as toxicity\, solubility\, and permeability. This holistic approach paves a promising future in more accurate and informed drug discovery.
URL:https://seasevents.nmsdev7.com/event/be-pics-joint-seminar-uma-balakrishnan-and-kunal-poorey-sandia-national-laboratories/
LOCATION:Room 534 A Wing\, 3401 Walnut\, 3401 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:20240719T150000
DTEND;TZID=America/New_York:20240719T170000
DTSTAMP:20260403T154316
CREATED:20240712T171637Z
LAST-MODIFIED:20240712T171637Z
UID:10008018-1721401200-1721408400@seasevents.nmsdev7.com
SUMMARY:CBE Doctoral Dissertation Defense: "Fabrication of Functional Nanostructured Polymers Based on Thermotropic and Lyotropic Liquid Crystals Derived from Sustainable Resources" (Ruiqi Dong)
DESCRIPTION:Abstract: \nNanostructured materials with interconnected nanoscale structures have garnered significant attention due to the ability to precisely regulate molecular transport by tuning molecular-level properties and offering more functional interfaces for interactions compared to bulk systems. Through self-assembly\, liquid crystal (LC) mesophases offer a route to achieve nanostructured polymers\, a promising alternative to traditional nanostructured inorganic materials and block copolymers. LC mesophases can spontaneously form monodisperse nanostructures and their self-assembly can be precisely controlled to yield well-organized 1D\, 2D\, and 3D periodic nanostructured materials. \nDeveloping functional polymers from sustainable resources is crucial due to the economic and environmental benefits that result. Sustainably derived unsaturated fatty acids are of interest in this regard because their unsaturated carbon bonds allow crosslinking to form stable polymers\, and their carboxylic acid functional groups enable specific surface chemistry and chemical derivatization. \nThis presentation focuses on how useful nanostructured functional materials can be realized from thermotropic and lyotropic liquid crystals derived from unsaturated fatty acids\, and on the properties of the materials thus produced. While prior use of the so-called “molecular templating” approach has produced well-defined nanostructured membranes from thermotropic LCs\, precise tuning of pore size and functionality (e.g. for addressing different applications) remains a challenge. We developed a new approach in which changing the stoichiometry of building blocks of self-assembling supramolecular constructs led to a robust handle for controlling pore shape\, and pore size with sub-nm resolution. The approach used thermotropic LCs based on citronellol\, a plant-derived molecule. To balance selectivity and permeability\, we developed highly selective and permeable thin nanofiltration membranes based on lyotropic LCs from conjugated linoleic acid. This reproducible process can be applied to various systems for creating larger-scale nanostructured thin films. Additionally\, we explored LC materials for ion and electron transport\, presenting for the first time a nanostructured lyotropic LC membrane for cation conduction derived from conjugated linoleic acid\, which shows improved electron transport performance compared to single-ion polymeric electrolyte materials.
URL:https://seasevents.nmsdev7.com/event/cbe-doctoral-dissertation-defense-fabrication-of-functional-nanostructured-polymers-based-on-thermotropic-and-lyotropic-liquid-crystals-derived-from-sustainable-resources-ruiqi-dong/
LOCATION:Towne 337
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:20240722T100000
DTEND;TZID=America/New_York:20240722T120000
DTSTAMP:20260403T154316
CREATED:20240712T174714Z
LAST-MODIFIED:20240712T174714Z
UID:10008019-1721642400-1721649600@seasevents.nmsdev7.com
SUMMARY:CBE Doctoral Dissertation Defense: "MEMS-Based Electrochemical Power Sources for Extended Operational Duration of Micro Unmanned Vehicles” (Yanghang Huang)
DESCRIPTION:Abstract: \nMicro unmanned vehicles\, defined as 25-gram to 5-kg in weight\, are increasingly being utilized in applications such as search and rescue or agricultural monitoring. Such vehicles are typically powered with lithium-ion batteries. However\, vehicle operational efficiency and capability are often compromised by the limited energy density of these batteries\, resulting in short operational duration. This dissertation explores high-energy chemistries beyond lithium-ion batteries\, including metal-air batteries and fuel cells\, as power sources to significantly extend the operational duration of both the kilogram-scale and gram-scale micro unmanned vehicles. \nThree different power source chemistries are proposed and investigated for different types of micro unmanned vehicles: gram-scale micro unmanned-ground-vehicles; gram-scale micro unmanned-aerial-vehicles; and kilogram-scale micro unmanned-aerial-vehicles. \n1. Gram-Scale Unmanned-Ground-Vehicles: Micro zinc-air batteries are developed for extended operation durations for gram-scale unmanned-ground-vehicles. The lean electrolyte operation of the battery enhances its gravimetric performance; however\, this approach suffers from rapid electrolyte degradation\, limiting battery performance. The study identifies carbonation and slow zincate decomposition as the main degradation mechanisms. Based on these findings\, a discharge energy model is developed\, successfully predicting the discharge performance of the micro zinc-air battery. Insights from the degradation study are used to enhance the electrochemical performance of micro zinc-air batteries\, demonstrating their potential to significantly extend the operation duration of gram-scale unmanned ground vehicles. \n2. Gram-Scale Micro Unmanned-Aerial-Vehicles: Due to the higher power requirements of gram-scale unmanned aerial vehicles\, micro aluminum-air batteries are developed as onboard power sources. The cell design and cell packaging of the battery are optimized to achieve an energy density of 320 Wh/kg above a power density of 500 W/kg\, far surpassing the performance of equivalent commercial lithium-ion batteries. A 3D-printed small-scale quadrotor platform is used to evaluate flight duration\, with the micro aluminum-air battery delivering 13.1 minutes of flight time compared to the 4.5 minutes of the commercial micro lithium-ion battery. This demonstration also marks the first successful untethered flight of a gram-scale micro unmanned-aerial-vehicle powered by air batteries. Additionally\, cathode fabrication techniques are explored\, utilizing a Ag-based cathode developed through microfabrication techniques to maximize the electrochemically active surface area. This cathode can support the necessary power density for the small-scale quadrotor while offering a potential cost reduction of 1/1500th of that of conventional cathodes. This work highlights the potential of micro aluminum-air batteries to serve as efficient\, cost-effective\, and long-lasting power sources for gram-scale unmanned-aerial-vehicles. \n3. Kilogram-Scale Micro Unmanned-Aerial-Vehicles: For kilogram-scale micro unmanned-aerial-vehicles\, solid oxide fuel cells are investigated. A flight duration model is first developed\, showing that the solid oxide fuel cell is promising for significantly extending the flight duration. To meet the power requirement of the kilogram-scale micro unmanned-aerial-vehicle\, a microfabrication process for thin-film solid oxide fuel cells is developed\, aiming to increase its power performance. While preliminary results confirm the successful fabrication of the thin-film cell\, scaling the technology to power the kilogram-scale micro unmanned-aerial-vehicle remains a significant challenge. \nThis work demonstrates that high-energy-density electrochemical power sources with simultaneous high-power-density for micro unmanned vehicles are feasible. Such unconventional high-energy power sources\, including zinc-air batteries\, aluminum-air batteries\, and solid oxide fuel cells\, show significant potential to substantially extend operational durations of multiple micro unmanned vehicle types.
URL:https://seasevents.nmsdev7.com/event/cbe-doctoral-dissertation-defense-mems-based-electrochemical-power-sources-for-extended-operational-duration-of-micro-unmanned-vehicles-yanghang-huang/
LOCATION:Room 221\, Singh Center for Nanotechnology\, 3205 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:20240722T103000
DTEND;TZID=America/New_York:20240722T113000
DTSTAMP:20260403T154316
CREATED:20240709T184707Z
LAST-MODIFIED:20240709T184707Z
UID:10008016-1721644200-1721647800@seasevents.nmsdev7.com
SUMMARY:MEAM Ph.D. Thesis Defense: "Multi-Robot Coordination and Cooperation via Graph-Based Computation"
DESCRIPTION:Multi-robot coordination and cooperation are critical behaviors that improve team performance and enable new tasks in application areas like autonomous construction\, agriculture\, and extended operation in large unknown regions. This dissertation examines these behaviors in the context of the multi-robot resource allocation problem\, where robots must be allocated to regions of demand. In particular\, we are interested in uncertainty-tolerant approaches that apply to large multi-robot teams. We introduce graph-based computation via Graph Neural Networks applied to the distributed multi-agent coverage control problem\, which boasts improved performance and scalability by leveraging learned inter-agent communication strategies. We also introduce a graph-based modeling and planning approach to multi-robot task allocation in complex multi-task missions where coordination and cooperation are explicitly required. We expand this approach into an online setting that re-plans around task failures and unexpected observations. We demonstrate empirically that these modeling approaches and algorithms bring performance improvements that further the state of the art by leveraging the fundamental graph structure present in some multi-robot problems.
URL:https://seasevents.nmsdev7.com/event/meam-ph-d-thesis-defense-multi-robot-coordination-and-cooperation-via-graph-based-computation/
LOCATION:Levine 307\, 3330 Walnut 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:20240722T130000
DTEND;TZID=America/New_York:20240722T150000
DTSTAMP:20260403T154316
CREATED:20240722T125810Z
LAST-MODIFIED:20240722T125810Z
UID:10008029-1721653200-1721660400@seasevents.nmsdev7.com
SUMMARY:ESE PhD Thesis Defense: "Aluminum Scandium Nitride Ultra-Wideband Resonator and Filter Systems"
DESCRIPTION:Aluminum scandium nitride (AlScN)\, a ternary alloy by doping scandium into aluminum nitride (AlN)\, has circumspectly gained recognition through the last decade as one of the promised materials in forming the next generation radio frequency (RF) filters and resonators – core components in the wireless telecommunication systems that interconnect today’s world. This\, coupled with the recent discovery of its ferroelectricity\, has led to unexpected possibilities and prospects. Woefully\, while Sc-alloying has gifted AlScN remarkable properties\, it took great effort to make these properties come into realization. Namely\, AlScN is difficult to deposit\, hard to etch\, and arduous to be made useful. In this dissertation\, techniques and methodologies were developed to handle these specific challenges\, and approaches for new actuation principles were proposed and validated.  Utilizing an AlN seed and a gradient AlScN seed layer\, the sputtering deposition of high quality AlScN films on commercial substrates was demonstrated. Through exploring the wet and dry etching methods\, a variety of etching rates\, selectivity and sidewall profile were calibrated and therefore made selectable. Via the implementation of an AlN buffer layer\, a surface acoustic wave (SAW) resonator built directly on a low-cost silicon substrate with a high electromechanical coupling coefficient (kt2) is shown. Furthermore\, by adopting and adapting a method known as apodization\, Lamb wave resonators (LWRs) and filters with record-breaking figure of merits (FOM) exhibiting spurious free operation characteristics were achieved. Second harmonic generation (SHG)\, a powerful tool in laser optics for the purpose of frequency doubling was examined and the ability of periodic poling to a domain width as small as 220 nm with tailored 50 % duty cycle has been attained\, allowing quasi phase matching (QPM) for the generation of deep ultraviolet (DUV) in photonic integrated circuits. Finally\, by leveraging this ability\, a brand-new actuation mechanism for periodically poled LWR (PPLWR) was introduced and investigated. Acoustic resonances were realized at all target frequencies\, with the highest one providing double-digit kt2 when working in the cross-sectional Lamé mode. With all roadblocks removed\, such devices working in the X-band or above have great potential of serving the goal of frequency selection in the coming 6 G era.
URL:https://seasevents.nmsdev7.com/event/ese-phd-thesis-defense-aluminum-scandium-nitride-ultra-wideband-resonator-and-filter-systems/
LOCATION:Room 35\, Singh Center for Nanotechnology\, 3205 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:20240722T133000
DTEND;TZID=America/New_York:20240722T143000
DTSTAMP:20260403T154316
CREATED:20240709T183405Z
LAST-MODIFIED:20240709T183405Z
UID:10008015-1721655000-1721658600@seasevents.nmsdev7.com
SUMMARY:MEAM MSE Thesis Defense: "Algorithmic Design of an Origami Squirrel-Inspired Paw for Quadrupedal Locomotion in Bar-like Terrains"
DESCRIPTION:The Dynamic Origami Quadruped (DOQ)\, an origami quadrupedal robot\, has been primarily utilized for hopping and walking on flat ground. However\, adapting it walking on bar-like terrains presents significant challenges. Inspired by the Squirrel-inspired Rapid Tenodesis (SQRT) foot which is capable of passively grasping onto curved surfaces during perching\, this thesis explores the integration of the DOQ and SQRT designed through origami fabrication techniques. Origami fabrication can offer a parametric approach to robot design\, enabling researchers to rapidly prototype by inputting serval constraint parameters. On the other hand\, the inherent compliance of origami bodies further enhances the passivity of the foot\, reducing the restoring time during takeoff from the bar. The origami SQRT demonstrates promising performance\, showcasing its potential in closely grasping bars and assisting the DOQ in maintaining stability.
URL:https://seasevents.nmsdev7.com/event/meam-mse-thesis-defense-algorithmic-design-of-an-origami-squirrel-inspired-paw-for-quadrupedal-locomotion-in-bar-like-terrains/
LOCATION:Levine 307\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Dissertation or Thesis Defense,Master's
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240722T150000
DTEND;TZID=America/New_York:20240722T170000
DTSTAMP:20260403T154316
CREATED:20240712T182623Z
LAST-MODIFIED:20240712T182623Z
UID:10008020-1721660400-1721667600@seasevents.nmsdev7.com
SUMMARY:ESE Ph.D. Thesis Defense: "Integrating graphene Hall sensors with co-designed silicon circuits for high-throughput magnetic biosensing"
DESCRIPTION:The limitations of silicon electronic devices increasingly constrain the performance of silicon integrated circuits (ICs) and their use in new applications. Next-generation devices with exceptional performance and new functionalities have been realized using two-dimensional materials such as graphene. For example\, graphene Hall-effect sensors (GHSs) greatly outperform commercial silicon magnetic-field sensors and could significantly improve the performance of sensor arrays used in magnetic imagers and biosensing. However\, the 2D nature of graphene leads to undesirable effects such as device heterogeneity\, offset\, and noise which limit the practical appeal of GHSs compared to silicon devices with poorer performance but higher reliability. \nThis thesis investigates several techniques drawn from device\, circuit\, and system-level perspectives to address the existing limitations of graphene Hall sensors and enable their more widespread usage. A central theme of this work is combining graphene Hall sensors with silicon integrated circuits and using the standout aspects of silicon IC technology – reliability\, high speed\, and scalability – to mitigate the undesirable properties of GHSs while retaining their advantages. This thesis also explores the applications of GHSs for in-flow detection of magnetically labeled cells and other biological particles which can be used to analyze blood samples to study the progression of cancer and infectious disease with minimal sample processing.
URL:https://seasevents.nmsdev7.com/event/ese-ph-d-thesis-defense-integrating-graphene-hall-sensors-with-co-designed-silicon-circuits-for-high-throughput-magnetic-biosensing/
LOCATION:Auditorium\, LRSM Building\, 3231 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:20240722T150000
DTEND;TZID=America/New_York:20240722T170000
DTSTAMP:20260403T154316
CREATED:20240718T144611Z
LAST-MODIFIED:20240718T144611Z
UID:10008024-1721660400-1721667600@seasevents.nmsdev7.com
SUMMARY:BE Doctoral Dissertation Defense: "Nanoparticle-based RNA therapeutic strategies for treating multiple myeloma" (Christian Figueroa-Espada)
DESCRIPTION:The Department of Bioengineering at the University of Pennsylvania and Dr. Michael J. Mitchell are pleased to announce the Doctoral Dissertation Defense of Christian Figueroa-Espada.\n\nTitle: Nanoparticle-based RNA therapeutic strategies for treating multiple myeloma\n\nDate: Monday\, July 22\, 2024\nLocation: Heilmeier Hall\, Towne Rm 100\nTime: 3:00 pm\n\nZoom information: https://upenn.zoom.us/j/94229801039?pwd=UzhVdTZ4L1VncXJTeUkwN0NWaXlmdz09\n Meeting ID: 942 2980 1039\n Passcode: 097271\n\nThe public is welcome to attend!
URL:https://seasevents.nmsdev7.com/event/be-doctoral-dissertation-defense-nanoparticle-based-rna-therapeutic-strategies-for-treating-multiple-myeloma-christian-figueroa-espada/
LOCATION:Heilmeier Hall (Room 100)\, Towne Building\, 220 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:20240723T101500
DTEND;TZID=America/New_York:20240723T111500
DTSTAMP:20260403T154316
CREATED:20240709T181937Z
LAST-MODIFIED:20240709T181937Z
UID:10008014-1721729700-1721733300@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Bio-inspired Architected Materials/Structures with Enhanced Failure Characteristics"
DESCRIPTION:Structural failure is a critically important design consideration in many engineering applications. Nature provides a number of interesting examples of lightweight structural features that exhibit outstanding failure characteristics. Recent progress in additive manufacturing has facilitated precise control over geometric features\, allowing for the implementation of bio-inspired structural designs on a layer-by-layer basis. In this talk\, we will introduce the role of geometry in the failure characteristics of bio-inspired architected materials/structures. We will first present the use of bamboo-inspired void patterns to geometrically improve the failure properties of structures made from brittle polymers under flexural bending. Then\, we will extend this study into a material system with tunable plasticity to study the effect of plasticity on the fracture behavior of different architected voids. Lastly\, we will showcase the concept of employing simulated “swarms” of bio-inspired agents\, such as those mimicking the behavior of bees and ants\, to create novel designs of architected materials/structures that exhibit enhanced fracture properties\, including improved energy dissipation to failure and damage tolerance\, specifically under uniaxial tension.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-bio-inspired-architected-materials-structures-with-enhanced-failure-characteristics/
LOCATION:Heilmeier Hall (Room 100)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar,Doctoral
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240723T103000
DTEND;TZID=America/New_York:20240723T113000
DTSTAMP:20260403T154316
CREATED:20240718T170602Z
LAST-MODIFIED:20240718T170602Z
UID:10008025-1721730600-1721734200@seasevents.nmsdev7.com
SUMMARY:MEAM/MSE Special Seminar: "Starfish-Inspired Tubefeet for Temporary Underwater Adhesion"
DESCRIPTION:Temporary and switchable underwater adhesion plays an important role in robotic underwater locomotion and transportation. The physical features of the suction cups of octopuses have been mimicked\, where the vacuum chamber formed by the deformation of these cups enable strong adhesion. However\, the works mainly focused on the strength of the underwater adhesion. For the underwater locomotion and transportation of objects in aqueous environment\, rapid and autonomous detachment following strong adhesion should be accomplished. To address this challenge\, we turned to the starfish and its tube feet for inspiration. Starfish possess hundreds of tube feet beneath their arms\, each comprising a thin cylindrical tube. At the end of these tube feet lies the cupped pad\, which resembles a conically shaped frustum with recessed dimples that widen outward. During movement\, when a starfish steps on a surface\, the foot pushes the mouth downward\, causing it to spread along the underlying surface and secure attachment. Moreover\, when digging\, starfish employ their tube feet to carry sand grains. Taking inspiration from starfish tube feet\, three strategies have been proposed to achieve temporary adhesion. Firstly\, high adhesion hysteresis\, characterized by a high ratio of normal to shear adhesion force\, is observed when the aspect ratio is high. This implies that detaching the tube foot from an object becomes easier by simply tilting it. Secondly\, autonomous detachment is facilitated by utilizing stimuli-responsive hydrogels within the supple mouth of the tube foot. When exposed to stimuli such as heat or light\, the hydrogel undergoes shrinkage\, rapidly reducing the adhesion force. Lastly\, immediate detachment can be achieved by integrating the tube foot with an external syringe. By manipulating the syringe\, the water-filled cylinder of the tube foot can apply pressure swiftly\, leading to rapid detachment. These strategies can provide efficient and versatile solutions for temporary adhesion and detachment in underwater locomotion and transportation.
URL:https://seasevents.nmsdev7.com/event/meam-mse-special-seminar-starfish-inspired-tubefeet-for-temporary-underwater-adhesion/
LOCATION:Towne 227 (MEAM Conference Room)\, 220 S. 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240724T100000
DTEND;TZID=America/New_York:20240724T120000
DTSTAMP:20260403T154316
CREATED:20240722T123736Z
LAST-MODIFIED:20240722T123736Z
UID:10008028-1721815200-1721822400@seasevents.nmsdev7.com
SUMMARY:BE Doctoral Dissertation Defense: "Remote control of cell function using temperature as an input" (William Benman)
DESCRIPTION:The Department of BIoengineering at the University of Pennsylvania and Dr. Lukasz Bugaj proudly announce the Doctoral Dissertation Defense of WIlliam Benman.\n\nTitle: Remote control of cell function using temperature as an input\nAdvisor: Lukasz Bugaj\nDate: Wednesday\, July 24\, 2024\nTime: 10:00AM EST\nLocation: Skirkanich Hall\, Berger Auditorium\, Rm. 013\n\nZoom option:\nhttps://upenn.zoom.us/j/91967695550 \nThe public is welcome to attend.
URL:https://seasevents.nmsdev7.com/event/be-doctoral-dissertation-defense-remote-control-of-cell-function-using-temperature-as-an-input-william-benman/
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:20240725T150000
DTEND;TZID=America/New_York:20240725T170000
DTSTAMP:20260403T154316
CREATED:20240722T122930Z
LAST-MODIFIED:20240722T122930Z
UID:10008027-1721919600-1721926800@seasevents.nmsdev7.com
SUMMARY:BE & Pathology Joint Seminar: "Extracellular vesicles and microglia as biomarker and therapeutic intervention of Alzheimer's disease and related disorders" (Tsuneya Ikezu\, Mayo Clinic)
DESCRIPTION:
URL:https://seasevents.nmsdev7.com/event/be-pathology-joint-seminar-extracellular-vesicles-and-microglia-as-biomarker-and-therapeutic-intervention-of-alzheimers-disease-and-related-disorders-tsuneya-ikezu-mayo-clinic/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Bioengineering":MAILTO:be@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240730T101500
DTEND;TZID=America/New_York:20240730T111500
DTSTAMP:20260403T154316
CREATED:20240717T153107Z
LAST-MODIFIED:20240717T153107Z
UID:10008023-1722334500-1722338100@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Dynamic Bipedal Walking on Unstructured Terrain through Real-Time Perception and Control"
DESCRIPTION:Bipedal robots have seen significant interest from academia and industry for their potential to efficiently traverse unstructured environments\, such as disaster zones\, industrial infrastructure\, and cluttered homes. Autonomous bipedal walking in the wild remains an unsolved challenge\, however\, partially due to the difficulty of perceiving and reacting to obstacles in real-time\, while maintaining balance. Existing walking control approaches for rough terrain decompose the problem into separate footstep planning and motion control tasks\, limiting their usefulness for dynamic\, underactuated robots. We first present a hierarchical walking controller which can generate dynamic walking motions over stepping-stone like terrains by jointly optimizing over foothold choices and motion plans. This controller is formulated as a Mixed-Integer Quadratic Program\, and can be solved at 50-200Hz\, depending on the complexity of the terrain. We then propose a perception system for generating a convex polygon representation of the terrain in real-time from robot-mounted depth cameras. The controller and perception system are demonstrated on the underactuated bipedal robot Cassie.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-dynamic-bipedal-walking-on-unstructured-terrain-through-real-time-perception-and-control/
LOCATION:Room 337\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar,Doctoral
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240805T133000
DTEND;TZID=America/New_York:20240805T140000
DTSTAMP:20260403T154316
CREATED:20240802T174416Z
LAST-MODIFIED:20240802T174416Z
UID:10008035-1722864600-1722866400@seasevents.nmsdev7.com
SUMMARY:Summer 2024 ROBO Master's Thesis Presentation - Gabriel Bennett\, "Midair Refueling of Electronic Drones: A First Step Towards Solving the Energy Limitations of the Modern Drone"
DESCRIPTION:This is a virtual event which will take place via Zoom. \n1:30pm Gabriel Bennett \n“Midair Refueling of Electronic Drones: A First Step Towards Solving the Energy Limitations of the Modern Drone” \nAdvised by Dr. Mark Yim
URL:https://seasevents.nmsdev7.com/event/summer-2024-robo-masters-thesis-presentation-gabriel-bennett/
LOCATION:Zoom
CATEGORIES:Master's
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:20240806T101500
DTEND;TZID=America/New_York:20240806T111500
DTSTAMP:20260403T154316
CREATED:20240731T165705Z
LAST-MODIFIED:20240731T165705Z
UID:10008032-1722939300-1722942900@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Soft Pneumatic Force Application through Membrane Design and Stiffness Control"
DESCRIPTION:Modern robotic platforms are designed for precision\, but their cost and rigidity make them impractical for many at-home physical human-robot interaction (pHRI) applications. Soft pneumatic actuators (SPA) have potential as safe and affordable means of providing comfortable force application for use in pHRI\, but lack shape planning and control during contact. In this seminar\, we will discuss electroadhesive clutches as a novel strain limiter for soft membranes and their applications in real-time programming of inflation trajectory and force response. We outline our force-pressure characterization for a heterogeneous SPA membrane design\, the use of autonomous experimentation for design parameter exploration\, and our vision for applications in pHRI for the elderly or those with significant medical ailments.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-soft-pneumatic-force-application-through-membrane-design-and-stiffness-control/
LOCATION:Towne 337
CATEGORIES:Seminar,Doctoral
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240813T101500
DTEND;TZID=America/New_York:20240813T111500
DTSTAMP:20260403T154316
CREATED:20240801T185343Z
LAST-MODIFIED:20240801T185343Z
UID:10008034-1723544100-1723547700@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Towards Understanding Tactile Sensing Across the Robot Manipulation Stack"
DESCRIPTION:Humans are exceptionally skilled at manipulating a diverse range of objects\, apparent from the order of magnitude difference in sizes\, weight distributions\, stiffnesses\, and geometries of items we use on a daily basis. For robots to seamlessly integrate into a world centered around human dexterity\, they must also possess comparable manipulation abilities. My research aims to address this challenge by exploring and understanding how tactile sensing can contribute to robot manipulation across the stack in sensor design\, perception\, and control. First\, we leverage a selectively transmissive soft membrane for a novel tactile and proximity sensor that offers uncompromised spatial resolution in both modalities. Then\, we explore how to fuse the tactile and proximity modalities to extract contact patches from extreme deformations of the soft sensing surface\, extending functionality to new deformation regimes. Finally\, I will conclude with our work on an RL-tractable tactile skin model that enables zero-shot sim2real transfer of ternary shear and binary normal forces\, which we use to develop an RL policy for dexterous in-hand translation.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-towards-understanding-tactile-sensing-across-the-robot-manipulation-stack/
LOCATION:Towne 337
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240814T101500
DTEND;TZID=America/New_York:20240814T111500
DTSTAMP:20260403T154316
CREATED:20240801T131847Z
LAST-MODIFIED:20240801T131847Z
UID:10008033-1723630500-1723634100@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Exploring Self-assembly of 2D Materials: Insights from Graphene Auto-kirigami"
DESCRIPTION:Two dimensional (2D) materials with atomic-scale thickness like graphene exhibit substantial in-plane stiffness and strength while maintaining significant out-of-plane flexibility. This enables the assembly of graphene into complex multilayer and even three-dimensional (3D) structures through a unique combination of self-folding\, self-tearing\, and nearly frictionless self-propagation\, all driven by interfacial energy. We call this phenomenon ‘graphene auto-kirigami’. \nAuto-kirigami provides an innovative approach to potentially sculpting 2D materials into intricate micro/nanostructures. However\, significant challenges exist in understanding and controlling it. To address this\, we have probed graphene auto-kirigami with atomic force microscopy (AFM) experiments\, continuum mechanics models\, and molecular dynamics (MD) simulations. We will present results toward our goal of developing a complete\, atomistically-informed physical description of auto-kirigami formation in graphene-based systems.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-exploring-self-assembly-of-2d-materials-insights-from-graphene-auto-kirigami/
LOCATION:Towne 319\, 220 S. 33rd Street\, Philadelphia\, 19104\, United States
CATEGORIES:Seminar,Doctoral
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240816T130000
DTEND;TZID=America/New_York:20240816T150000
DTSTAMP:20260403T154316
CREATED:20240717T143942Z
LAST-MODIFIED:20240717T143942Z
UID:10008022-1723813200-1723820400@seasevents.nmsdev7.com
SUMMARY:BE Doctoral Dissertation Defense: "Addressing the genome structure-function relationship in neural activation and neurological disorders" (Harshini Chandrashekar)
DESCRIPTION:The Department of Bioengineering at the University of Pennsylvania and Dr. Jennifer Cremins are pleased to announce the Doctoral Dissertation Defense of Harshini Chandrashekar.\n\n\nTitle: Addressing the genome structure-function relationship in neural activation and neurological disorders\nDate: Friday\, August 16\, 2024\nTime: 1-3pm\nLocation: Clinical Research Building (CRB) Austrian Auditorium\nZoom link: https://upenn.zoom.us/j/3641987868\n\n\nThe public is welcome to attend.
URL:https://seasevents.nmsdev7.com/event/be-doctoral-dissertation-defense-addressing-the-genome-structure-function-relationship-in-neural-activation-and-neurological-disorders-harshini-chandrashekar/
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
END:VCALENDAR