Fall 2022 Hybrid-Seminar Series Mondays 1.00-2.00 pm (EST) Towne 225 / Raisler Lounge For Zoom link, please contact <manu@seas.upenn.edu
Seminar
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Sustainable transportation and aviation are critical to address climate change and renewable energy powered battery electric vehicles represent a promising path towards this goal. I will discuss the performance metrics needed of batteries for electric land and air vehicles, and assess the energy-efficiency of electric vertical take-off and landing (eVTOL) aircraft compared to ground vehicles. […] |
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ABSTRACT: Neurosymbolic learning is an emerging paradigm which, at its core, combines the otherwise complementary worlds of classical algorithms and deep learning; in doing so, it ushers in more accurate, interpretable, and domain-aware solutions for today’s most complex machine learning challenges. I will begin by reviewing the various fundamentals, such as algorithmic supervision, symbolic reasoning, […] |
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Pathological calcification is a wide-spread phenomenon in the human body, in which calcium minerals form in soft tissues and are found in both healthy and diseased tissues. One example are microcalcifications (MCs), which are primarily biological apatite and occur in cancerous and benign breast pathologies. MCs are key mammographic indicators, however, little is known about […]
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*This is a HYBRID Event with in-person attendance in Levine 307 and virtual attendance via Zoom. ABSTRACT Domains such as high-mix manufacturing, domestic robotics, space exploration, etc., are key areas of interest for robotics. Yet, the difficulty of anticipating the role of robots in these domains is a crucial hurdle for the adoption of robots. […]
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This is a hybrid seminar: Check email for the zoom link and passcode. "Cellular and Acellular Strategies for Heart and Lung Repair" Therapeutic tissue regeneration using stem cells has been hampered by the controversial identity of resident stem cells, low cell retention/engraftment, tumorigenecity and immunogenicity issues. Taking a bioengineering/biomaterials approach, this lecture will introduce the uses of drug delivery and biomaterials […] |
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This is a hybrid event with in-person attendance in Wu and Chen and virtual attendance via Zoom. ABSTRACT Mobile robots now deliver vast amounts of sensor data from large unstructured environments. In attempting to process and interpret this data there are many unique challenges in bridging the gap between prerecorded data sets and the […] |
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As 3D printing and other advanced manufacturing techniques have become more common, it is increasingly possible to produce structures with nearly arbitrary internal geometric and compositional features, opening up vast new design space for engineers. In this work, we consider a kirigami-inspired, flexible architected material comprising rotating squares joined at their vertices. The rotational degrees […] |
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This distinguished lecture will be a hybrid event held in the Glandt Forum (Singh Center) and via Zoom. A light reception will follow the live lecture. Zoom link Passcode: 704696 "How Memory Guides Value-Based Decisions" From robots to humans, the ability to learn from experience turns a rigid response system into a flexible, adaptive one. […] |
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This is a hybrid event with in-person attendance in Wu and Chen and virtual attendance via Zoom. ABSTRACT If I show you a photo of a place you have never been to, you can easily imagine what you could do in that picture. Your understanding goes from the surfaces you see to the ones […] |
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Fall 2022 Hybrid-Seminar Series Mondays 1.00-2.00 pm (EST) Towne 225 / Raisler Lounge For Zoom link, please contact <manu@seas.upenn.edu |
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This is a hybrid event with in-person attendance in Levine 307 and virtual attendance via Zoom. ABSTRACT Area coverage path planning is the problem of finding an efficient path that traverses the region of interest while avoiding existing obstacles. When dealing with real systems, the dynamic changes and uncertainties of the environments increase the […]
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ABSTRACT Machine learning traditionally does not get at causality and causality research traditionally treats machine learning as a dangerous set of highly biased estimators. In my talk I will talk about our lab’s efforts to use machine learning as a component of more traditional quasi-experimental techniques. I will also discuss meta-learning approaches to causal inference, […] |
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