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DTSTART;TZID=America/New_York:20240412T150000
DTEND;TZID=America/New_York:20240412T170000
DTSTAMP:20260403T193054
CREATED:20240326T152430Z
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SUMMARY:CBE Doctoral Dissertation Defense: "A Multifaceted Approach to CO2 Emissions Reductions and Removals" (Maxwell Pisciotta)
DESCRIPTION:Abstract:\nThe scientific consensus is that climate change is not only actively occurring\, but that it is irrevocably due to human activities associated with greenhouse gas emissions. Greenhouse gas emissions have been accumulating in the atmosphere since the beginning of the industrial revolution. This thesis specifically focuses on one greenhouse gas in particular\, CO 2 . The continued CO 2 emissions from human activity can be quantified with the atmospheric concentration\, which amounts to upwards of 420 ppm today. To mitigate the harmful impacts of climate change\, these CO 2 emissions must be mitigated\, through pathways such as reducing their initial generation\, capturing them when they are unable to be avoided\, and removing them from the atmosphere when they cannot be captured at the source. This thesis investigates different technologies that fit into these broad categories\, notably\, deploying carbon capture technologies on natural gas combined cycle power plants\, decarbonizing industrial sectors\, and pairing direct air capture technologies to geothermal energy. To readily address the CO 2 emissions from natural gas combined cycle power plants\, a novel approach of using thermal energy storage was developed and evaluated to ensure its technological performance and economic viability. By integrating natural gas combined cycle power plants with carbon capture and storage (CCS) and thermal energy storage opportunities\, the economic viability of these plants improve. This was measured using the net present value of each of the configurations assessed over real-world locational marginal pricing (LMP) signals from NYISO and CAISO. Of the thermal energy storage options\, eight of the 19 thermal energy storage configurations led to an increased net present value on 11.5% – 98% of the LMP signals. Additionally\, a framework was developed and used to identify opportunities to integrate direct air capture (DAC) systems with geothermal energy resources to maximize the CO 2 abatement potential. The Geothermal-Framework can be used with various geothermal resources ranging from 86ºC – 225ºC\, using various working fluids\, and brine salinity ranging from 0-6%. When the integration of geothermal energy and DAC systems are compared to geothermal energy being used to generate low-carbon electricity\, the CO 2 abatement potential is increase by 105% to 452% when geothermal energy is integrated with DAC systems. This illustrates beneficial synergies between the two technologies\, namely being able to use geothermal energy as thermal energy rather than solely converting it to electricity. Lastly\, the Geothermal-DAC Framework was used to showcase opportunities for integrating DAC with the geothermal resources near Gerlach\, NV\, in preparation for a community meeting. The community feedback was then incorporated\, facilitating updates to the Geothermal-DAC Framework to account for community needs\, illustrating that engineering can be community-centered from the start of the project. All the approaches explored in this thesis highlight the need for a diverse portfolio of solutions to address the ongoing CO 2 emissions and abatement required to avoid the most harmful impacts of climate change. Furthermore\, the efforts of researchers\, scientists\, policymakers and frontline communities will be needed in concert to deploy a portfolio that meets the needs to address climate change and protect against further environmental injustices.
URL:https://seasevents.nmsdev7.com/event/cbe-doctoral-dissertation-defense-a-multifaceted-approach-to-co2-emissions-reductions-and-removals-maxwell-pisciotta/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Doctoral,Student,Dissertation or Thesis Defense
ORGANIZER;CN="Chemical and Biomolecular Engineering":MAILTO:cbemail@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240416T090000
DTEND;TZID=America/New_York:20240416T110000
DTSTAMP:20260403T193054
CREATED:20240410T132128Z
LAST-MODIFIED:20240410T132128Z
UID:10007942-1713258000-1713265200@seasevents.nmsdev7.com
SUMMARY:BE Doctoral Dissertation Defense: "The Origin and Factors Affecting Differentiation of Progenitor Cells in Tendon-to-Bone Integration" (Tim Kamalitdinov)
DESCRIPTION:The Department of Bioengineering at the University of Pennsylvania and Dr. Nat Dyment are pleased to announce the Doctoral Dissertation Defense of Tim Kamalitdinov.\n\n\nTitle: The Origin and Factors Affecting Differentiation of Progenitor Cells in Tendon-to-Bone Integration\nDate: April 16\, 2024\nTime: 9:00 AM\nLocation: SCTR (Smilow Center for Translational Research) 12-146AB\n\nThe public is welcome to attend.
URL:https://seasevents.nmsdev7.com/event/be-doctoral-dissertation-defense-the-origin-and-factors-affecting-differentiation-of-progenitor-cells-in-tendon-to-bone-integration/
LOCATION:Smilow Center for Translational Research in SCTR 11-146AB
CATEGORIES:Doctoral,Graduate,Student,Dissertation or Thesis Defense
ORGANIZER;CN="Bioengineering":MAILTO:be@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240416T090000
DTEND;TZID=America/New_York:20240416T110000
DTSTAMP:20260403T193055
CREATED:20240410T212450Z
LAST-MODIFIED:20240410T212450Z
UID:10007944-1713258000-1713265200@seasevents.nmsdev7.com
SUMMARY:ESE PhD Thesis Defense: "Multiferroic Micro Electromechanical Systems for Magnetic Sensing and Wireless Power Transfer in Biomedical Applications"
DESCRIPTION:Multiferroic micro-electromechanical systems (MEMS) enable small\, room temperature\, low power magnetic sensing and wireless power transfer (WPT) in biomedical applications. Current biomagnetic sensing relies on sensitive magnetometers like superconducting quantum interference devices (SQUIDs)\, but their reliance on cryogenic temperatures is undesirable. \n  \nThis thesis presents the theory\, design\, microfabrication\, and characterization of multiferroic MEMS magnetic sensors and WPT devices. Iron cobalt/silver (Fe50Co50/Ag) magnetostrictive material is coupled to piezoelectric aluminum nitride (AlN) to form a multiferroic sensor. Low frequency biomagnetic signals are upconverted around the length-extensional beam’s 7-16 MHz mechanical resonance to provide Q enhancement to the sensitivity. The up conversion exploits a nonlinear phenomenon of magnetostrictive materials with applied mechanical strain. For two devices studied\, modulated sensitivities of 58.4 mA/T and 37.7 mA/T were observed along with resolutions of 5.03 nT/√Hz and 2.72 nT/√Hz over a bandwidth larger than the biomagnetic frequency spectrum (0.1Hz to 1kHz). The sensors’ sensitivity was limited by Duffing nonlinearity and the relatively low piezoelectric coefficients of AlN. \n  \nTo improve sensitivity\, magnetoelectric sensors were fabricated using (Fe0.5Co0.5)0.92Hf0.08 coupled to 28% aluminum scandium nitride (Al0.72Sc0.28N). Increasing sensitivity improved the resolution from 5.03 nT/√Hz to 2.16 nT/√Hz. To delay the onset of thermal Duffing nonlinearity\, various anchoring tether lengths were explored in Fe0.5Co0.5/Ag – AlN magnetoelectric sensors to provide better heat conduction away from the structure. Also\, silicon dioxide (SiO2) was added to compensate the temperature coefficient of frequency (TCF). Larger achievable strain was verified before the onset of Duffing nonlinearity\, providing increased modulation of the Fe0.5Co0.5/Ag and a resolution of 1.11 nT/√Hz\, an 86% improvement when compared to a long tether device with the same layer stack (8.02 nT/√Hz) and a 78% improvement over the initial (Fe50Co50/Ag) – AlN long tether devices with no SiO2 thermal compensation. \n  \nWPT measurements were taken using (Fe50Co50/Ag) – AlN magnetoelectric devices. By sending a magnetic field at the device resonance frequency\, optimal WPT can be achieved. Devices were packaged with a magnetic bias circuit and the output power was measured. For a device at 7.44MHz\, an output power of 126.8 nW and a power density of 1196.2 uW/mm3 is projected when measuring with both electrodes.
URL:https://seasevents.nmsdev7.com/event/ese-phd-thesis-defense-multiferroic-micro-electromechanical-systems-for-magnetic-sensing-and-wireless-power-transfer-in-biomedical-applications/
LOCATION:Fisher Bennett Hall\, Room 401\, 3340 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Dissertation or Thesis Defense
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240416T100000
DTEND;TZID=America/New_York:20240416T113000
DTSTAMP:20260403T193055
CREATED:20240319T165717Z
LAST-MODIFIED:20240319T165717Z
UID:10007907-1713261600-1713267000@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Cardiovascular Engineering – A 'Personal' Journey from Bench to Bedside"
DESCRIPTION:Over the past few decades\, significant contributions have been made by engineers to healthcare. The successful translation of fundamental engineering concepts has helped improve patient care and diagnosis. This impact has been particularly evident in the field of cardiovascular medicine where the roles of fluid and solid mechanics\, and imaging are critical. In ~45 years of pioneering research\, Professor Ajit Yoganathan’s Cardiovascular Fluid Mechanics Laboratory at the Georgia Institute of Technology & Emory University\, has been in the vanguard of this movement: advancing knowledge and technology in native and replacement heart valves\, cardiovascular diagnostic techniques\, and pediatric surgical/interventional planning. Using state-of-the-art fluid dynamic measurement techniques\, Dr. Yoganathan and his group have developed methods to enable the optimization of replacement heart valve designs. Novel techniques in the assessment of native heart valve function have provided clinicians with improved tools to assess disease severity and helped identify effective treatment options. \nFor the treatment of congenital heart defects\, the development of novel computational modeling tools to simulate surgical procedures and their fluid dynamics outcomes have provided clinicians with new ways to plan for treatments for individual patients to increase the probability of success. Combined\, these advances have helped bridge the lab bench to the patient’s bedside/bassinet and integrate engineering science with the art of medicine.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-cardiovascular-engineering-a-personal-journey-from-bench-to-bedside/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240416T110000
DTEND;TZID=America/New_York:20240416T120000
DTSTAMP:20260403T193055
CREATED:20240326T125329Z
LAST-MODIFIED:20240326T125329Z
UID:10007913-1713265200-1713268800@seasevents.nmsdev7.com
SUMMARY:ESE Spring Seminar - "Scaling Deep Learning Up and Down"
DESCRIPTION:Deep learning with neural networks has emerged as a key approach for discovering patterns and modeling relationships in complex data. AI systems powered by deep learning are used widely in applications across a broad spectrum of scales. There are strong needs for scaling deep learning both upward and downward. Scaling up highlights the pursuit of scalability – the ability to utilize increasingly abundant computing and data resources to achieve superior capabilities\, overcoming diminishing returns. Scaling down represents the demand for efficiency – there is limited data for many application domains\, and deployment is often in compute-limited settings. \nIn this talk\, we present several studies in both directions. For scaling up\, we first explore the design of scalable neural network architectures that are widely adopted in various fields. We then discuss an intriguing observation on modern vision datasets and its implication on scaling training data. For scaling down\, we introduce simple\, effective\, and popularly used approaches for compressing convolutional networks and large language models\, alongside interesting empirical findings. Notably\, a recurring theme in this talk is the careful examination of implicit assumptions in the literature\, which often leads to surprising revelations that reshape community understanding. Finally\, we discuss exciting avenues for future deep learning and vision research\, such as next-gen architectures and dataset modeling.
URL:https://seasevents.nmsdev7.com/event/ese-spring-seminar-scaling-deep-learning-up-and-down/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Colloquium
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240416T120000
DTEND;TZID=America/New_York:20240416T133000
DTSTAMP:20260403T193055
CREATED:20240410T150958Z
LAST-MODIFIED:20240410T150958Z
UID:10007943-1713268800-1713274200@seasevents.nmsdev7.com
SUMMARY:Raj and Neera Singh Program in Artificial Intelligence Town Hall
DESCRIPTION:
URL:https://seasevents.nmsdev7.com/event/artificial-intelligence-undergraduate-program-town-hall/
LOCATION:Glandt Forum\, Singh Center for Nanotechnology\, 3205 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Student,Panel Discussion,Undergraduate
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240417T120000
DTEND;TZID=America/New_York:20240417T130000
DTSTAMP:20260403T193055
CREATED:20240405T162609Z
LAST-MODIFIED:20240405T162609Z
UID:10007933-1713355200-1713358800@seasevents.nmsdev7.com
SUMMARY:Webinar: "Engineering in the Age of AI"
DESCRIPTION:Join Penn Engineering faculty to learn how to harness the power of AI for innovation. \nDean Vijay Kumar will moderate a discussion with Michael Kearns\, Professor and National Center Chair in Computer and Information Science (CIS); Surbhi Goel\, Magerman Term Assistant Professor in CIS; and René Vidal\, Rachleff University Professor\, with joint appointments in Electrical and Systems Engineering and Radiology. These experts will guide you through the cutting-edge tools\, techniques and methodologies transforming industries and reshaping engineering. \nThe event is open to everyone. \nRegister here
URL:https://seasevents.nmsdev7.com/event/webinar-engineering-in-the-age-of-ai/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240417T120000
DTEND;TZID=America/New_York:20240417T133000
DTSTAMP:20260403T193055
CREATED:20240212T185750Z
LAST-MODIFIED:20240212T185750Z
UID:10007857-1713355200-1713360600@seasevents.nmsdev7.com
SUMMARY:ASSET Seminar: "Reasoning Myths about Language Models: What is Next?" (Dan Roth\, University of Pennsylvania)
DESCRIPTION:ABSTRACT:  \nThe rapid progress made over the last few years in generating linguistically coherent natural language has blurred\, in the mind of many\, the difference between natural language generation\, understanding\, and the ability to reason with respect to the world. Nevertheless\, robust support of high-level decisions that depend on natural language understanding\, and one that requires dealing with “truthfulness” are still beyond our capabilities\, partly since most of these tasks are very sparse\, often require grounding\, and may depend on new types of supervision signals. \nI will discuss some of the challenges underlying reasoning and argue that we should focus on LLMs as orchestrators – coordinating and managing multiple models\, applications\, and services\, as a way to execute complex tasks and processes. I will discuss some of the challenges and present some of our work in this space\, focusing on supporting task decomposition and planning. \nZOOM LINK (if unable to attend in-person): https://upenn.zoom.us/j/92067502115
URL:https://seasevents.nmsdev7.com/event/asset-seminar-reasoning-myths-about-language-models-what-is-next-dan-roth-university-of-pennsylvania/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240417T130000
DTEND;TZID=America/New_York:20240417T150000
DTSTAMP:20260403T193055
CREATED:20240408T145749Z
LAST-MODIFIED:20240408T145749Z
UID:10007937-1713358800-1713366000@seasevents.nmsdev7.com
SUMMARY:BE Doctoral Dissertation Defense: "Deep Learning for Unpaired Domain Adaptive Medical Image Segmentation" (Yuemeng Li)
DESCRIPTION:The Department of Bioengineering at the University of Pennsylvania and Dr. Yong Fan are pleased to announce the Doctoral Dissertation Defense of Yuemeng Li.\n\nTitle: Deep Learning for Unpaired Domain Adaptive Medical Image Segmentation\nDate: April 17\, 2024\nTime: 1:00PM-3:00PM\nLocation: BRB Auditorium\nZoom link\n\nThe public is welcome to attend.
URL:https://seasevents.nmsdev7.com/event/be-doctoral-dissertation-defense-deep-learning-for-unpaired-domain-adaptive-medical-image-segmentation/
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:20240417T140000
DTEND;TZID=America/New_York:20240417T140000
DTSTAMP:20260403T193055
CREATED:20240415T170733Z
LAST-MODIFIED:20240415T170733Z
UID:10007949-1713362400-1713362400@seasevents.nmsdev7.com
SUMMARY:ESE PhD Thesis Defense: "Cellular Cosheaves\, Graphic Statics\, and Mechanics"
DESCRIPTION:Methods from algebraic topology enable simplifications and extensions of fundamental concepts in structural and mechanical engineering. Chief among these tools are cellular sheaves and cosheaves – abstract mathematical data structures over polyhedra and discrete spaces. The homology of cellular cosheaves (and cohomology of cellular sheaves) combines and distills distributed data into the most meaningful algebraic-topological features of the underlying system. While sheaves in general have a rich lineage in pure mathematics\, only recently has this theory been simplified and streamlined towards practical applications. \nThe main contribution of this thesis is in describing and enriching graphic statics\, a structural design method that emerged in the 19th century. This is a geometric form of Poincaré duality where primal and dual graphs encode the form and forces of a truss structure. We first model planar truss statics using cosheaves\, then prove that the long exact sequence of cosheaf homology precisely recovers the graphic statics relationship. This relation further extends to the equivariant setting\, where the statics of symmetric structures (under finite group action) splits by irreducible representations (and symmetry types). The cosheaf method proves invaluable in the modern 3D polyhedral setting\, where a spectral sequence is used to untangle the linear relations between a range of filtered geometric cosheaf equilibrium spaces. Here many novel results are derived\, interlinking the statics of geometrically dual trusses and other systems. \nThere are several secondary results presented in this thesis. We connect the statics of trusses with the statics of rigid frames\, deriving the novel anchored frame system. We prove that mechanical linkage kinematics are in fact encoded by anchored frame self-stresses. The kinematics of rigid origami surfaces is described by cellular cosheaves as well in dual systems. The Jacobian between hinge angular velocities and spatial velocity vectors is shown to be a connecting homomorphism\, connecting different origami models and extending widely used linearization techniques in closed-chain kinematics.
URL:https://seasevents.nmsdev7.com/event/ese-phd-thesis-defense-cellular-cosheaves-graphic-statics-and-mechanics/
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:20240417T140000
DTEND;TZID=America/New_York:20240417T150000
DTSTAMP:20260403T193055
CREATED:20240415T145901Z
LAST-MODIFIED:20240415T145901Z
UID:10007948-1713362400-1713366000@seasevents.nmsdev7.com
SUMMARY:Spring 2024 GRASP SFI: Karl Pertsch\, University of California\, Berkeley and Stanford University\, "Building Open-Source Generalist Robot Policies"
DESCRIPTION:This will be a hybrid event with in-person attendance in Towne 337 and virtual attendance on Zoom. \nABSTRACT\nGeneralist robot policies\, trained on large and diverse robot datasets\, have the potential to transform how robot learning research is done: in the same way that current models in NLP are almost universally derived from pretrained large language models\, future robot policies might be initialized from generalist robot models and finetuned with only modest amounts of target domain data. \nIn this talk I will discuss our efforts on building such generalist robot policies. I will focus on two key ingredients: data and models. On the data side\, I will discuss our recent works on building the largest open-source real robot manipulation datasets to date\, the Open X-Embodiment dataset and DROID\, with a total of 2M+ robot trajectories. On the model side\, I will summarize our learnings from building RT-X and Octo\, the first generalist robot policies trained on the Open X-Embodiment dataset. I will discuss their current limitations and outline important steps for future research towards ubiquitous robot foundation models.
URL:https://seasevents.nmsdev7.com/event/spring-2024-grasp-sfi-karl-pertsch/
LOCATION:Towne 337
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:20240417T153000
DTEND;TZID=America/New_York:20240417T163000
DTSTAMP:20260403T193055
CREATED:20240116T182935Z
LAST-MODIFIED:20240116T182935Z
UID:10007813-1713367800-1713371400@seasevents.nmsdev7.com
SUMMARY:CBE Seminar: "Creating Real Steak Without the Cow: Using Insights from Wine and Biopharmaceutical Production to Commercialize Cultivated Meat"
DESCRIPTION:Abstract\nBetween a growing global population and increased consumption of meat from developing countries\, it is projected that meat production will have to increase by at least 60% by 2050 to meet demand. It is unlikely that expanded conventional animal agriculture alone will be able to meet this need. Therefore\, alternatives to conventional meat will be required at a very large scale. This is likely to include plant- and fungal-based meat alternatives\, as well as cultivated meat—the growth of animal stem cells in large-scale fermentors with subsequent differentiation into muscle\, fat\, and connective tissue cells. While close to 150 companies have formed in the last eight years internationally to commercialize this technology\, only three products are currently on the market\, a chicken nugget sold at two restaurants in Singapore\, and two chicken products recently approved for sale in the US\, but not yet widely available. Aside from federal regulatory hurdles\, difficult technical hurdles remain. These include development of cell lines well-suited for production\, inexpensive growth and differentiation media\, creation of structure into whole cuts like marbled steaks\, and scale-up to a commercial size potentially 10 times larger than anything previously attempted for cell-culture-based processes. After presenting the field and our consortium-based approach to addressing these hurdles\, this talk will focus on media optimization\, as over 80% of the cost of these products is projected to be from the nutrients used to grow the cells. For optimization\, using both spent media analysis and AI-based efficient experimental design techniques for complex optimization problems will be discussed. In addition\, initial efforts to facilitate the scale-up of processing will be discussed. While the field of cultivated meat is extremely new\, many of the problems facing large-scale commercialization of this fermentation process are not. Thus\, we can look to gain critical insight from decades of research in allied fields from wine to biopharmaceutical production.
URL:https://seasevents.nmsdev7.com/event/cbe-seminar-creating-real-steak-without-the-cow-using-insights-from-wine-and-biopharmaceutical-production-to-commercialize-cultivated-meat/
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:20240418T100000
DTEND;TZID=America/New_York:20240418T110000
DTSTAMP:20260403T193055
CREATED:20240415T143038Z
LAST-MODIFIED:20240415T143038Z
UID:10007947-1713434400-1713438000@seasevents.nmsdev7.com
SUMMARY:MEAM Ph.D. Thesis Defense: "Do the Twist: Toward Agile Control of an Axially Twisting Robotic Quadruped"
DESCRIPTION:Even as they continue to improve\, legged robots pale in comparison to their biological counterparts. This discrepancy is at least partly due to robots possessing an order of magnitude fewer degrees of freedom. In fact\, most dynamically capable quadrupedal robots lack any degrees of freedom in the torso\, opting instead for a simpler\, single\, rigid body. This rigidity results in the legs competing for workspace and power optimality during locomotion. Furthermore\, although some quadrupeds do feature a flexible torso\, most research primarily focuses on bending in either the sagittal or lateral planes. In contrast\, this thesis explores the integration of the often-neglected axially twisting spinal degree of freedom into quadrupedal robotic platforms. \nFirst\, taking inspiration from biomechanical reorientation data in geckos\, the thesis develops an axial twisting strategy that reduces the effort required for a robot to right itself. Following this\, a trajectory-optimization-based study compares the energetic and dynamic performance of two quadrupedal models\, one with a rigid torso and one with a twisting torso\, across various dynamic and aperiodic locomotory tasks. Hoping to realize these results\, this work introduces “Twist”\, a novel quadrupedal robotic platform with an axially twisting spine\, and proceeds to develop controllers for agile\, spatial locomotion. Starting in the sagittal plane\, an angular-momentum-based coordinate is developed for a three-degree-of-freedom\, extensible inverted pendulum model and is shown to be an approximate asymptotic phase variable and to produce an input decoupling. Toward generalizing those results\, the underactuation of the spatial floating torso model during two-point contact is thoroughly examined and informs a composition-based controller for the “Twist” platform. Finally\, integrating these ideas\, this thesis develops a trotting gait\, which shows promising results using this composition for spatial quadrupedal locomotion.
URL:https://seasevents.nmsdev7.com/event/meam-ph-d-thesis-defense-do-the-twist-toward-agile-control-of-an-axially-twisting-robotic-quadruped/
LOCATION:David Rittenhouse Laboratory Building\, Room 4C4\, 209 S. 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Doctoral,Dissertation or Thesis Defense
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240418T103000
DTEND;TZID=America/New_York:20240418T120000
DTSTAMP:20260403T193055
CREATED:20240408T185929Z
LAST-MODIFIED:20240408T185929Z
UID:10007939-1713436200-1713441600@seasevents.nmsdev7.com
SUMMARY:A Franklin Medal Laureate Lecture: "Building Therapies Layer-By-Layer"
DESCRIPTION:Recipient of the 2024 Benjamin Franklin Medal in Chemistry
URL:https://seasevents.nmsdev7.com/event/a-franklin-medal-laureate-lecture-building-therapies-layer-by-layer/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar,Distinguished Lecture
ORGANIZER;CN="Materials Science and Engineering":MAILTO:johnruss@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240418T110000
DTEND;TZID=America/New_York:20240418T120000
DTSTAMP:20260403T193055
CREATED:20240409T183802Z
LAST-MODIFIED:20240409T183802Z
UID:10007941-1713438000-1713441600@seasevents.nmsdev7.com
SUMMARY:Benjamin Franklin Medal in Mechanical Engineering Lecture:  "Molecular and Micro-Structural Mechanics and Design of Soft Materials"
DESCRIPTION:Soft synthetic and natural polymeric-based materials offer particular new avenues for the design and fabrication of materials and devices. Engineering the molecular and geometrical structures of the constituent materials\, together with utilizing their ability to sustain large deformations enables materials and designs with novel properties and functional behavior. We begin with the development of physically-based models of elastomeric mechanics\, from the elegant simplicity of the “8-chain” network model of rubber elasticity to the more complex enhancements that capture the molecular mechanisms of nonlinear time-dependent behavior. We also address the behavior of versatile co-polymers\, which can form micro-composites of “hard” and “soft” domains\, providing an ability to engineer unique combinations of highly resilient elastic and dissipative systems. The structure and behavior of natural analogs such as mussel byssal threads are included and shown to yield further insights. Finally\, we present the ability to tailor constituent geometrical features of soft composites using new fabrication techniques including 3D printing. Exemplars include patterned and layered structures which exhibit deformation and instability-induced pattern transformations. These structural transformations result in concomitant changes in a multitude of behaviors giving super-elastic and multi-linear elastic response\, enhanced mechanisms for energy storage\, switchable band gaps\, soft actuators\, and morphable surface topology. Looking to the future\, the predictive ability of multi-scale nonlinear mechanics of soft materials\, combined with the rapid developments in fabrication techniques provide profound opportunities to truly design functional materials\, devices and products.
URL:https://seasevents.nmsdev7.com/event/benjamin-franklin-medal-in-mechanical-engineering-lecture-molecular-and-micro-structural-mechanics-and-design-of-soft-materials/
LOCATION:Glandt Forum\, Singh Center for Nanotechnology\, 3205 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Symposium
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240418T120000
DTEND;TZID=America/New_York:20240418T130000
DTSTAMP:20260403T193055
CREATED:20240402T141245Z
LAST-MODIFIED:20240402T141245Z
UID:10007927-1713441600-1713445200@seasevents.nmsdev7.com
SUMMARY:ESE Spring Seminar - "Miniaturized Biomedical Devices for Navigation\, Sensing and Stimulation"
DESCRIPTION:Medical electronic devices are an integral part of the healthcare system today and are used in a variety of applications around us. The design of such devices has several stringent requirements\, the key being miniaturization\, low-power operation\, and wireless functionality. In this talk\, I will present CMOS-based miniaturized\, low-power and wireless biomedical devices in three broad domains: (a) in-vivo navigation and tracking\, (b) in-vivo sensing of biomarkers and physiological signals\, and (c) in-vivo stimulation and drug delivery. For the first part\, I will talk about ingestible and implantable devices that can be used to achieve sub-mm tracking accuracy in 3D and in real time inside the human body\, which is very useful for localizing devices in the GI tract\, during precision surgeries and minimally invasive procedures. In the second part\, I will present the design of a novel on-chip 3D magnetic sensor that is highly miniaturized and low-power\, thus making it suitable for many biomedical applications. In the last part\, I will briefly talk about my recent work on a wearable device for multi-modal sensing from sweat\, followed by ongoing work on devices for stimulation and drug-delivery in the GI tract. I will end the talk with a glimpse of my future research direction.
URL:https://seasevents.nmsdev7.com/event/ese-spring-seminar-miniaturized-biomedical-devices-for-navigation-sensing-and-stimulation/
LOCATION:Towne 327
CATEGORIES:Colloquium
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240418T130000
DTEND;TZID=America/New_York:20240418T150000
DTSTAMP:20260403T193055
CREATED:20240326T180014Z
LAST-MODIFIED:20240326T180014Z
UID:10007917-1713445200-1713452400@seasevents.nmsdev7.com
SUMMARY:BE Doctoral Dissertation Defense: "Proteome-seq: Sequencing-Based Readout of Proteomic Analytical Assay" (Mariia (Masha) Alibekova Long)
DESCRIPTION:The Department of Bioengineering at the University of Pennsylvania and Dr. Alex Hughes are pleased to announce the Doctoral Dissertation Defense of Mariia (Masha) Alibekova Long.\n\nTitle:  Proteome-seq: Sequencing-Based Readout of Proteomic Analytical Assay\nDate: April 18\, 2024\nTime: 1:00 PM\nLocation: SCTR (Smilow Center for Translational Research) 11-146AB\n\nZoom option:\n\nTopic: Mariia Alibekova Long’s PhD Thesis Defense\nTime: Apr 18\, 2024 01:00 PM Eastern Time (US and Canada) \nJoin Zoom Meeting\nhttps://upenn.zoom.us/j/98332256725?pwd=UjU2MXllaHlqMFdHemZaL2VHeTQ5UT09 \nMeeting ID: 983 3225 6725\nPasscode: 183014 \n\n\n\nThe Public is welcome to attend.
URL:https://seasevents.nmsdev7.com/event/be-doctoral-dissertation-defense-proteome-seq-sequencing-based-readout-of-proteomic-analytical-assay-mariia-masha-alibekova-long/
LOCATION:Smilow Center for Translational Research in SCTR 11-146AB
CATEGORIES:Doctoral,Graduate,Student,Dissertation or Thesis Defense
ORGANIZER;CN="Bioengineering":MAILTO:be@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240418T153000
DTEND;TZID=America/New_York:20240418T163000
DTSTAMP:20260403T193055
CREATED:20240326T135130Z
LAST-MODIFIED:20240326T135130Z
UID:10007914-1713454200-1713457800@seasevents.nmsdev7.com
SUMMARY:BE Seminar: "Using Computers to Derive Protein Structure from Sparse Data – A Case Study for Mass Spectrometry" (Steffen Lindert\, Ohio State)
DESCRIPTION:Mass spectrometry-based methods such as covalent labeling\, surface induced dissociation (SID) or ion mobility (IM) are increasingly used to obtain information about protein structure. However\, in contrast to other high-resolution structure determination methods\, this information is not sufficient to deduce all atom coordinates and can only inform on certain elements of structure\, such as solvent exposure of individual residues\, properties of protein-protein interfaces or protein shape. Computational methods are needed to predict high-resolution protein structures from the mass spectrometry (MS) data. Our group develops algorithms within the Rosetta software package that use mass spectrometry data to guide protein structure prediction. These algorithms can incorporate several different types of mass spectrometry data\, such as covalent labeling\, surface induced dissociation\, and ion mobility. We developed scoring functions that assess the agreement of residue exposure with covalent labeling data\, the agreement of protein-protein interface energies with SID data and the agreement of protein model shapes with collision cross section (CCS) IM measurements. We subsequently rescored Rosetta models generated with de novo protein folding and protein-protein docking and we were able to accurately predict protein structure from MS labeling\, SID and IM data. Future work is focusing on developing custom machine learning models to predict protein structure from MS data.
URL:https://seasevents.nmsdev7.com/event/be-seminar-using-computers-to-derive-protein-structure-from-sparse-data-a-case-study-for-mass-spectrometry/
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:20240419T103000
DTEND;TZID=America/New_York:20240419T114500
DTSTAMP:20260403T193055
CREATED:20240122T211347Z
LAST-MODIFIED:20240122T211347Z
UID:10007811-1713522600-1713527100@seasevents.nmsdev7.com
SUMMARY:Spring 2024 GRASP on Robotics: Kristi Morgansen\, University of Washington\, "Integrated Sensing and Actuation for Robust Flight Systems"
DESCRIPTION:This is a hybrid event with in-person attendance in Wu and Chen and virtual attendance on Zoom. \nABSTRACT\nA fundamental element of effective operation of autonomous systems is the need for appropriate sensing and processing of measurements to enable desired system actions. Model-based methods provide a clear framework for careful proof of system capabilities but suffer from mathematical complexity and lack of scaling as probabilistic structure is incorporated. Conversely\, learning methods provide viable results in probabilistic and stochastic structures\, but they are not generally amenable to rigorous proof of performance. A key point about learning systems is that the results are based on use of a set of training data\, and those results effectively lie in the convex hull of the training data. This presentation will focus on use of model-based nonlinear empirical observability criteria to assess and improving and bounding performance of learning pose (position and orientation) of rigid bodies from computer vision. A particular question to be addressed is what sensing data should be captured to best improve the existing training data. The particular tools to be leveraged here focus on the use of empirical observability gramian techniques being developed for nonlinear systems where sensing and actuation are coupled in such a way that the separation principle of linear methods does not hold. These ideas will be discussed relative to both engineering applications in the form of motion planning for range and bearing only navigation in autonomous vehicles\, vortex position and strength estimation from pressure measurements on airfoils\, and effective strain sensor placement on insect wings for inertial measurements.
URL:https://seasevents.nmsdev7.com/event/spring-2024-grasp-on-robotics-kristi-morgansen-university-of-washington-integrated-sensing-and-actuation-for-robust-flight-systems/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="General Robotics%2C Automation%2C Sensing and Perception (GRASP) Lab":MAILTO:grasplab@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240419T123000
DTEND;TZID=America/New_York:20240419T180000
DTSTAMP:20260403T193055
CREATED:20240327T181031Z
LAST-MODIFIED:20240327T181031Z
UID:10007918-1713529800-1713549600@seasevents.nmsdev7.com
SUMMARY:2024 Bioengineering Graduate Research Symposium
DESCRIPTION:Join the Graduate Association of Bioengineers (GABE) for the 2024 Graduate Research Symposium!\n\nWhen: April 19\, 2024 from 12:30-6:00 PM\nWhere: The Singh Center for Nanotechnology\nWhat: Keynote by Dr. David Kaplan; BE graduate student posters and presentations; food buffet and reception; BE swag and awards.\n\nRegistration is free and is open to anyone affiliated with the Department of Bioengineering. Register here.\n\nShould you have any questions\, contact the symposium co-chairs: Dimitris Boufidis boufidis@seas.upenn.edu & Miles Arnett mjarnett@seas.upenn.edu
URL:https://seasevents.nmsdev7.com/event/2024-bioengineering-graduate-research-symposium/
LOCATION:Singh Center for Nanotechnology\, 3205 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Doctoral,Graduate,Student,Master's,Symposium
ORGANIZER;CN="Bioengineering":MAILTO:be@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240419T140000
DTEND;TZID=America/New_York:20240419T150000
DTSTAMP:20260403T193055
CREATED:20240321T144058Z
LAST-MODIFIED:20240321T144058Z
UID:10007909-1713535200-1713538800@seasevents.nmsdev7.com
SUMMARY:PICS Colloquium: "Physics-compatible kinetic-energy and entropy preserving (KEEP) scheme for high-fidelity simulation of compressible turbulence"
DESCRIPTION:Low (or ideally zero) numerical dissipation is always critical for high-fidelity scale-resolving flow simulations\, as numerical dissipation prevents the physics of inviscid kinetic energy and entropy conservation\, which is an essential attribute of compressible turbulence. However\, contrary to the requirement\, numerical schemes in compressible flow heavily rely on numerical dissipation for stable computation\, preventing high-fidelity simulations\, especially for flows around complex geometries. We address this challenge by devising a physics-compatible numerical scheme that satisfies the kinetic energy and entropy preservation (KEEP) properties by discretely satisfying the analytical relations of the governing equations. The KEEP scheme is highly stable without introducing numerical dissipation\, something that existing numerical schemes fail to do. The stability stems from the significant improvement of entropy preservation in the KEEP scheme. The KEEP scheme allows robust and high-fidelity simulation not only for academic purposes but also for engineering applications with complex geometries. We also discuss an illustrative application to near-stall flows around complex full aircraft configurations with high-lift devices to show the capability of our numerical framework.
URL:https://seasevents.nmsdev7.com/event/pics-colloquium-physics-compatible-kinetic-energy-and-entropy-preserving-keep-scheme-for-high-fidelity-simulation-of-compressible-turbulence/
LOCATION:PICS Conference Room 534 – A Wing \, 5th Floor\, 3401 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Colloquium
ORGANIZER;CN="Penn Institute for Computational Science (PICS)":MAILTO:dkparks@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240419T153000
DTEND;TZID=America/New_York:20240419T173000
DTSTAMP:20260403T193055
CREATED:20240326T153852Z
LAST-MODIFIED:20240326T153852Z
UID:10007916-1713540600-1713547800@seasevents.nmsdev7.com
SUMMARY:CBE Doctoral Dissertation Defense: "Identifying Material Fingerprints of Relevance to Understand Adsorbate-Surface Interactions Using First Principles Modeling and Machine Learning" (Genesis Quiles-Galarza)
DESCRIPTION:Abstract: \n\n\n\nAdsorption of chemical species on surfaces of materials is one of the critical phenomenon governing the reactivity and activity of the material for surface and interface driven chemical reactions. At the core of the analytical $d$-band adsorption model is the correlation between the adsorption energy of a chemical species (molecule or reaction intermediate) on the metal surface and an electronic material property\, namely the d-band center from the density of states (DOS). Although very successful\, the d-band model has its limitations and cannot be applied to all materials. Therefore\, efforts have been devoted to discover material fingerprints that can be used to describe adsorption of chemical species on more complex surfaces and materials. Herein\, we use first principles methods (density functional theory\, DFT) and machine learning (ML) to elucidate what kind of material fingerprints or features are needed to describe the interaction between an adsorbate and the surface of a metal material\, a two-dimensional (2D) transition metal carbide and nitride compound materials known as MXenes\, and a hybrid (molecular catalyst supported on a heterogeneous surface) catalyst material. The ML models used in this study belong either to the “black-box” or “glass-box” category\, enabling not only prediction of the adsorption energy with small errors\, but also allow insight into the material physics governing the adsorbate-surface interaction. These ML studies indisputably demonstrate that the electronic fingerprints of the material are the most critical features in reliably determining the adsorbate-surface interactions.For metals\, we confirm the findings of the analytical d-band model by achieving adsorption formulas with contributions from both the sp and d-DOS bands\, as well as multiple higher order contributions. For MXenes\, we find that the adsorbate-surface interaction is complex with significant contributions from the terminating functional group atom\, specifically their sp-DOS band features. Generally\, our studies shows that the nature of the adsorbate-surface interactions cannot be fully captured by single or simple linear correlations between the adsorbate energy and a materials feature\, but instead require higher order\, multi-dimensional feature combinations. These findings imply that further investigations are needed to develop physically-sound\, multi-dimensional features which could be used as descriptors to predict adsorbate-surface interactions with an accuracy comparable to that of DFT.
URL:https://seasevents.nmsdev7.com/event/cbe-doctoral-dissertation-defense-identifying-material-fingerprints-of-relevance-to-understand-adsorbate-surface-interactions-using-first-principles-modeling-and-machine-learning-genesis-quiles/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd 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:20240422T120000
DTEND;TZID=America/New_York:20240422T130000
DTSTAMP:20260403T193055
CREATED:20240404T211020Z
LAST-MODIFIED:20240404T211020Z
UID:10007930-1713787200-1713790800@seasevents.nmsdev7.com
SUMMARY:MEAM Master's Thesis Defense: "Optical Analysis of Buckling-Induced Micro-Robotic Membranes"
DESCRIPTION:In recent years\, micro-robotic membranes have attracted increasing interest due to their unique properties and potential applications in various fields. The optical properties of these membranes have been playing a crucial role in the design and development of optical devices such as reflective displays with customizable colors. The primary challenge to understanding the mechanical-spectral interaction is the limitation of conventional microscopic techniques. The AFM cannot be employed when voltage is applied. Conversely\, hyperspectral imaging offers insights into the spectral response but lacks the capacity to infer topological characteristics directly. In this research\, I build an optical model that stands on the theoretical foundation laid by Maxwell’s equations\, Fresnel equations\, and the Transfer Matrix Method (TMM). By feeding the hyperspectral imaging\, the model can reconstruct the 3D topologies of buckling membranes. This is achieved through least-square regressions to accurately predict height data across various points. Through this methodology\, this research offers a novel framework for understanding the complex interplay between mechanical deformation and optical phenomena.
URL:https://seasevents.nmsdev7.com/event/meam-masters-thesis-defense-optical-analysis-of-buckling-induced-micro-robotic-membranes/
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:20240423T100000
DTEND;TZID=America/New_York:20240423T113000
DTSTAMP:20260403T193055
CREATED:20240408T175418Z
LAST-MODIFIED:20240408T175418Z
UID:10007938-1713866400-1713871800@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Flows About Superhydrophobic Surfaces"
DESCRIPTION:Superhydrophobic surfaces\, formed by air entrapment within the cavities of hydrophobic solid substrates\, offer a promising potential for hydrodynamic drag reduction. In several of the prototypical surface geometries the flows are two-dimensional\, governed by Laplace’s equation in the longitudinal problem and the biharmonic equation in the transverse problem. Moreover\, low-drag configurations are typically associated with singular limits. Accordingly\, the analysis of liquid slippage past superhydrophobic surfaces naturally invites the use of both singular-perturbation methods and conformal-mapping techniques. I will discuss the combined application of these methodologies to several emerging problems in the field.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-flows-about-superhydrophobic-surfaces/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240423T110000
DTEND;TZID=America/New_York:20240423T140000
DTSTAMP:20260403T193055
CREATED:20240408T140915Z
LAST-MODIFIED:20240408T140915Z
UID:10007935-1713870000-1713880800@seasevents.nmsdev7.com
SUMMARY:Sustainable Catering - Earth Week 2024
DESCRIPTION:Learn about sustainable products and practices your caterer can implement to reduce waste\, minimize plastic and lower carbon footprint. Planet-friendly menu Plastic-alternative packaging and utensils Nutrition label for customized eating preferences Vendor engagement beyond delivery.
URL:https://seasevents.nmsdev7.com/event/sustainable-catering-earth-week-2024/
LOCATION:Lobby and Mezzanine\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Student,Staff
ATTACH;FMTTYPE=image/jpeg:https://seasevents.nmsdev7.com/wp-content/uploads/2024/04/Earth-Week-2024-PosterHorizontal-scaled-1.jpg
ORGANIZER;CN="SEAS Green Team":MAILTO:dianepa@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240423T120000
DTEND;TZID=America/New_York:20240423T130000
DTSTAMP:20260403T193055
CREATED:20240404T170254Z
LAST-MODIFIED:20240404T170254Z
UID:10007928-1713873600-1713877200@seasevents.nmsdev7.com
SUMMARY:MEAM Master's Thesis Defense: "Gaussian Process-Based Active Exploration Strategies in Vision and Touch"
DESCRIPTION:Robots struggle to understand object properties like shape\, material\, and semantics due to limited prior knowledge\, hindering manipulation in unstructured environments. In contrast\, humans learn these properties through interactive multi-sensor exploration. This work proposes fusing visual and tactile observations into a unified Gaussian Process Distance Field (GPDF) representation for active perception of object properties. While primarily focusing on geometry\, this approach also demonstrates potential for modeling surface properties beyond geometry. \nThe GPDF encodes signed distance\, gradients\, and uncertainty estimates. Starting with an initial visual shape estimate\, the framework iteratively refines the geometry by integrating dense vision measurements using differentiable rendering and tactile measurements at uncertain regions. By quantifying multi-sensor uncertainties\, it plans exploratory motions to maximize information gain for recovering precise 3D structures. To improve scalability\, it investigates approximation methods like inducing point parameterization for Gaussian Processes. This probabilistic multi-modal fusion enables active exploration and mapping of complex object geometries.
URL:https://seasevents.nmsdev7.com/event/meam-masters-thesis-defense-gaussian-process-based-active-exploration-strategies-in-vision-and-touch/
LOCATION:Meyerson Hall\, Room B2\, 210 S. 34th 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:20240424T100000
DTEND;TZID=America/New_York:20240424T110000
DTSTAMP:20260403T193055
CREATED:20240415T203631Z
LAST-MODIFIED:20240415T203631Z
UID:10007950-1713952800-1713956400@seasevents.nmsdev7.com
SUMMARY:PICS Colloquium: "Exploiting time-domain parallelism to accelerate neural network training and PDE constrained optimization"
DESCRIPTION:This talk will explore methods for accelerating numerical optimization constrained by transient problems using parallelism. Two types of transient problems will be considered. In the first case training algorithms for Neural ODEs will be discussed. Neural ODEs are a class of neural network architecture where the depth of the neural network (the layers) is modeled as a continuous time domain. For the second case\, transient PDE-constrained optimization problems will be described. In either case\, simulation-based optimization requires repeated executions of the simulator’s forward and backward (adjoint) time integration schemes. Consequently\, the arrow of time creates a major sequential bottleneck in the optimization process. Second\, for performance these methods rely strongly on the available parallelization for the forward and adjoint solves. Thus\, when forward and adjoint solvers are already operating at the limit of strong scaling and hardware utilization\, the arrow-of-time bottleneck cannot be overcome by additional parallelization across the spatial grid or network layers.  \nDeep neural networks are a powerful machine learning tool with the capacity to‚ learn complex nonlinear relationships described by large data sets. Despite their success training these models remains a challenging and computationally intensive undertaking. We will present a layer-parallel training algorithm that exploits a multigrid scheme to accelerate both forward and backward propagation. Introducing a parallel decomposition between layers requires inexact propagation of the neural network. The multigrid method used in this approach stitches these subdomains together with sufficient accuracy to ensure rapid convergence. We demonstrate an order of magnitude wall-clock time speedup over the serial approach\, opening a new avenue for parallelism that is complementary to existing approaches. We also discuss applying the layer-parallel methodology to recurrent neural networks and transformer architectures.  \nThe second half of this talk focuses on PDE-constrained optimization formulations. Solving optimization problems with transient PDE-constraints is computationally costly due to the number of nonlinear iterations and the cost of solving large-scale KKT matrices. These matrices scale with the size of the spatial discretization times the number of time steps. We propose a new 2-level domain decomposition preconditioner to solve these linear systems when constrained by the heat equation. Our approach leverages the observation that the Schur-complement is elliptic in time\, and thus amenable to classical domain decomposition methods. Further\, the application of the preconditioner uses existing time integration routines to facilitate implementation and maximize software reuse. The performance of the preconditioner is examined in an empirical study demonstrating the approach is scalable with respect to the number of time steps and subdomains.
URL:https://seasevents.nmsdev7.com/event/pics-colloquium-exploiting-time-domain-parallelism-to-accelerate-neural-network-training-and-pde-constrained-optimization/
LOCATION:PICS Conference Room 534 – A Wing \, 5th Floor\, 3401 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Colloquium
ORGANIZER;CN="Penn Institute for Computational Science (PICS)":MAILTO:dkparks@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240424T120000
DTEND;TZID=America/New_York:20240424T131500
DTSTAMP:20260403T193055
CREATED:20240104T163727Z
LAST-MODIFIED:20240104T163727Z
UID:10007790-1713960000-1713964500@seasevents.nmsdev7.com
SUMMARY:ASSET Seminar: "Statistical Methods for Trustworthy Language Modeling" (Tatsu Hashimoto\, Stanford University)
DESCRIPTION:ABSTRACT: \nLanguage models work well\, but they are far from trustworthy. Major open questions remain on high-stakes issues such as detecting benchmark contamination\, identifying LM-generated text\, and reliably generating factually correct outputs. Addressing these challenges will require us to build more precise\, reliable algorithms and evaluations that provide guarantees that we can trust. \nDespite the complexity of these problems and the black-box nature of modern LLMs\, I will discuss how in all three problems — benchmark contamination\, watermarking\, and factual correctness — there are surprising connections between classic statistical techniques and language modeling problems that lead to precise guarantees for identifying contamination\, watermarking LM-generated text\, and ensuring the correctness of LM outputs. \n  \nZOOM LINK (if unable to attend in-person): https://upenn.zoom.us/j/94597712175
URL:https://seasevents.nmsdev7.com/event/asset-seminar-tatsu-hashimoto-stanford-university/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240424T150000
DTEND;TZID=America/New_York:20240424T160000
DTSTAMP:20260403T193055
CREATED:20240408T195558Z
LAST-MODIFIED:20240408T195558Z
UID:10007940-1713970800-1713974400@seasevents.nmsdev7.com
SUMMARY:Spring 2024 GRASP SFI: Harish Ravichandar\, Georgia Institute of Technology\, "New Wine in an Old Bottle: A Structured Approach to Democratize Robot Learning"
DESCRIPTION:This will be a hybrid event with in-person attendance in Levine 307 and virtual attendance on Zoom. \nABSTRACT\nDecades of rigorous research in dynamical systems and control helped us integrate robots into a wide variety of domains\, ranging from factory floors to the moon. Today\, it would appear that deep learning has taken over the torch and will bring robots to our homes\, freeing us all from banal chores. In this utopian vision\, learning-based approaches tend to replace analytical methods. Moving away from handcrafted bespoke solutions to generalist robots that can operate in unstructured environments. But one can instead view learning-based and analytical approaches as two ends of a broad spectrum\, with one end optimizing for reliability (at the cost of human effort) and the other for emergent intelligence (at the cost of data and computation). In this talk\, I will argue why it is better for robots to be in the middle of this broad spectrum. Using manipulation as a case study\, I will discuss how our lab combines ideas from dynamical systems and machine learning to overcome three often-overlooked issues with contemporary methods: i) high barrier to entry due to demands for expensive computational resources and annotated data\, ii) inability to handle new tasks without relying on significant user expertise (e.g.\, for reward or controller design\, hyperparameter tuning\, data collection and curation)\, and iii) unreliable behaviors due to inscrutable and unpredictable learned policies. Addressing these issues will enable robot learning to escape the confines of well-resourced research labs and positively impact the larger society.
URL:https://seasevents.nmsdev7.com/event/spring-2024-grasp-sfi-harish-ravichandar/
LOCATION:Levine 307\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="General Robotics%2C Automation%2C Sensing and Perception (GRASP) Lab":MAILTO:grasplab@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240424T153000
DTEND;TZID=America/New_York:20240424T163000
DTSTAMP:20260403T193055
CREATED:20240116T183524Z
LAST-MODIFIED:20240116T183524Z
UID:10007814-1713972600-1713976200@seasevents.nmsdev7.com
SUMMARY:John A. Quinn Distinguished Lecture in Chemical Engineering: "Exploring the Physics\, Materials Science\, and Biological Implications of Polyelectrolyte Complexation" (Matthew Tirrell\, University of Chicago)
DESCRIPTION:Abstract\nThe richness of liquid-liquid phase separation behavior in mixtures of oppositely-charged polyelectrolyte has been greatly illuminated recently in the polymer physics literature. Precise determinations of phase diagrams\, measurements of interfacial tension\, scattering measurements of chain configurations\, and increasingly insightful theory are all producing a clearer understanding of these phenomena. In parallel\, physics is also being brought to bear on manifestations of these behaviors in biology. Diverse biological examples related to liquid0liquid phase separation of polyelectrolyte complexes include membraneless organelles\, biological condensates that enhance transcription or protect from stress shock\, and other biological functions. This talk will spell out current understanding of the various contributions to the phase behavior\, including the role of various entropic contributions\, as well as the effects of charge density of the macromolecules. New\nresults on asymmetric mixtures will be presented\, which are more the norm in nature than the perfectly symmetrical mixtures in polymer physics studies.
URL:https://seasevents.nmsdev7.com/event/john-a-quinn-distinguished-lecture-in-chemical-engineering-exploring-the-physics-materials-science-and-biological-implications-of-polyelectrolyte-complexation-matthew-tirrell-university-of-c/
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
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