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CBE Seminar Series: “Flexible Protein Networks in Membrane Biology and Medicine” (Jeanne Stachowiak)
MSE Seminar: “Compressibility of Nanoconfined Fluids: Relating Atomistic Modeling to Ultrasonic Experiments”
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Wu and Chen Auditorium (Room 101), Levine Hall
3330 Walnut Street, Philadelphia, PA, United States
Fluids confined in nanopores are ubiquitous in nature and technology. In recent years, the interest in confined fluids has grown, driven by research on unconventional hydrocarbon resources -- shale gas […]
ESE Fall Colloquium – “On compression of, for, and with neural networks”
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Zoom - Meeting ID 971 2264 9281
Data compression is enjoying a renaissance fueled by an unprecedented growth in both the amount of data being generated and our reliance on powerful computation. At its heart is an […]
MEAM Ph.D. Thesis Defense: “An Aquatic Underactuated Modular Self-Reconfigurable Robotic System for Information-Limited Navigation in Gyre-Like Flows”
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Levine 307
3330 Walnut Street, Philadelphia, PA, United States
Aquatic modular self-reconfigurable robotic systems (MSRRs) have incredible potential for bringing practical, flexible, and adaptable robotic tools to challenging environments. They could build mobile ocean platforms or bridges for larger […]
Grace Hopper Distinguished Lecture: “How Memory Guides Value-Based Decisions” (Daphna Shohamy, Columbia University)
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Glandt Forum, Singh Center for Nanotechnology
3205 Walnut Street, Philadelphia, PA, United States
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 […]
CIS Seminar: ” Exploring the role of scientific machine learning in electric power system decarbonization”
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Wu and Chen Auditorium (Room 101), Levine Hall
3330 Walnut Street, Philadelphia, PA, United States
Electric power systems lie at the heart of efforts to mitigate and adapt to the effects of climate change. Mitigation requires shifting electricity generation away from carbon-emitting technologies toward zero […]
Fall 2022 GRASP on Robotics: David Fouhey, University of Michigan, “Understanding the Physical World from Images”
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Wu and Chen Auditorium (Room 101), Levine Hall
3330 Walnut Street, Philadelphia, PA, United States
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 […]
PICS Colloquium: “From Molecular Vibrations to Solvation, Protein Dynamics and Models of the Cytoplasm”
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PICS Conference Room 534 - A Wing , 5th Floor
3401 Walnut Street, Philadelphia, PA, United States
Abstract: Vibrational spectroscopies at mid-infrared frequencies provide excellent probes to characterize functional groups and their immediate chemical environment. However, from a thermodynamic and dynamic point of view, only the ground […]
Fall 2022 MEAM 5200: Block Stacking Challenge!
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Wu and Chen Auditorium (Room 101), Levine Hall
3330 Walnut Street, Philadelphia, PA, United States
This is a hybrid event with in-person attendance in Wu and Chen and virtual attendance via Zoom. ABSTRACT Teams of 3-4 students will compete head-to-head in a block stacking […]
PSOC Seminar: “Validation of a bicistronic CAR to overcome intratumoral heterogeneity in GBM” (Zev Binder, PSOM)
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Raisler Lounge (Room 225), Towne Building
220 South 33rd Street, Philadelphia, PA, United States
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
Fall 2022 GRASP Seminar: Nare Karapetyan, University of Maryland, “Area Coverage Path Planning: From Graphs to Field Deployments”
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Wu and Chen Auditorium (Room 101), Levine Hall
3330 Walnut Street, Philadelphia, PA, United States
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 […]
ASSET Seminar: ML for Causal Inference, Konrad Kording, University of Pennsylvania
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Levine 307
3330 Walnut Street, Philadelphia, PA, United States
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 […]