PICS Colloquium: “Learning about learning by many-body systems”

Abstract: Many-body systems from soap bubbles to suspensions to polymers learn the drives that push them far from equilibrium. This learning has been detected with thermodynamic properties, such as work absorption and strain. We progress beyond these macroscopic properties that were first defined for equilibrium contexts: We quantify statistical mechanical learning with representation learning, a […]

ODEI Spotlight: USABE Fireside Chat with Dr. CJ Taylor

Fireside Chat with Dr. CJ Taylor Date: Friday, February 26th Time: 4:00 - 5:00 PM EST Join USABE on Friday, February 26th at 4 PM for a conversation with Dr. CJ Taylor, Associate Dean for Diversity, Equity, and Inclusion at Penn Engineering, to discuss his experiences in STEM and engineering and his role as a […]

MEAM Seminar: “Fusion for Robot Perception and Controls”

Zoom - Email MEAM for Link peterlit@seas.upenn.edu

Machine learning has led to powerful advances in robotics: deep learning for visual perception from raw images and deep reinforcement learning (RL) for learning controls from trial and error. Yet, these black-box techniques can often require large amounts of data, have results difficult to interpret, and fail catastrophically when dealing with out-of-distribution data. In this […]

MSE Seminar: “Engineering topological phases in graphene moiré heterostructures”

Taming topological electronic phases is a fundamental challenge and an important milestone on the way towards novel electronic devices and topological quantum computation. Recent advances in fabrication techniques have made van der Waals (vdW) heterostructures one of the most active platforms for the experimental investigation of topological electronic phases in 2D. Moiré superlattices, which arise […]

MEAM Ph.D. Thesis Defense: “Reactive Planning with Legged Robots in Unknown Environments”

Zoom - Email MEAM for Link peterlit@seas.upenn.edu

Unlike the problem of safe task and motion planning in a completely known environment, the setting where the obstacles in a robot's workspace are not initially known and are incrementally revealed online has so far received little theoretical interest, with existing algorithms usually demanding constant deliberative replanning in the presence of unanticipated conditions. Moreover, even […]

CIS Seminar: ” Exterminating bugs in real systems”

Zoom - Email CIS for link cherylh@cis.upenn.edu

Software is everywhere, and almost everywhere, software is broken. Some bugs just crash your printer; others hand an identity thief your bank account number; still others let nation-states spy on dissidents and persecute minorities. This talk outlines my work preventing bugs using a blend of programming languages techniques and systems design. First, I'll talk about […]

Spring 2021 GRASP SFI: “Safe and Data-efficient Learning for Robotics”

Zoom

Abstract: For successful integration of autonomous systems such as drones and self-driving cars in our day-to-day life, they must be able to quickly adapt to ever-changing environments, and actively reason about their safety and that of other users and autonomous systems around them. Even though control-theoretic approaches have been used for decades now for the […]

CBE Seminar: “Metal-Organic Frameworks as Tunable Platforms for Gas Storage, Chemical Separations and Catalysis”

Zoom - Email CBE for link

Abstract Metal-organic frameworks (MOFs) are a versatile class of nanoporous materials synthesized in a “building-block” approach from inorganic nodes and organic linkers.  By selecting appropriate building blocks, the structural and chemical properties of the resulting materials can be finely tuned, and this makes MOFs promising materials for applications such as gas storage, chemical separations, sensing, […]

CIS Seminar: “The Measurement and Mismeasurement of Trustworthy ML”

Zoom - Email CIS for link cherylh@cis.upenn.edu

Across healthcare, science, and engineering, we increasingly employ machine learning (ML) to automate decision-making that, in turn, affects our lives in profound ways. However, ML can fail, with significant and long-lasting consequences. Reliably measuring such failures is the first step towards building robust and trustworthy learning machines. Consider algorithmic fairness, where widely-deployed fairness metrics can […]