Singh Center for Nanotechnology Annual User Meeting

Singh Center for Nanotechnology 3205 Walnut Street, Philadelphia, PA, United States

The 2022 Singh Center for Nanotechnology Annual User Meeting will be held on Thursday, October 13, 2022, in the Singh Center’s Glandt Forum. The purpose for this in-person meeting is to welcome the user community as we celebrate the acheivements of nanotechnology-enabled research and innovation at the University of Pennsylvania.  For more information:  http://singhnano.eventbrite.com/

MSE Seminar: “Additive Manufacturing of Compositionally Complex Alloys with Engineered Microstructures”

Wu and Chen Auditorium (Room 101), Levine Hall 3330 Walnut Street, Philadelphia, PA, United States

The increasing demand for structural metals has driven increasingly complex compositions, which bring critical challenges in processing of these materials. Additive manufacturing, also called 3D printing, is a disruptive technology for creating structural materials and components in a single print. In this talk, I will present our recent work on additive manufacturing of compositionally complex […]

Fall 2022 GRASP on Robotics: Victoria Webster-Wood, Carnegie Mellon University, “It’s Alive! Bioinspired and biohybrid approaches towards life-like and living robots”

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 Animals have long served as an inspiration for robotics. However, the adaptability, complex control, and advanced learning capabilities observed in animals are not yet fully understood, and therefore have not been fully captured by current robotic systems. Furthermore, […]

PICS Colloquium, “Sound Attenuation and the Vibrational Properties of Glasses”

PICS Conference Room 534 - A Wing , 5th Floor 3401 Walnut Street, Philadelphia, PA, United States

Abstract: Understanding of the universal low-temperature properties of glasses and why they differ from their crystalline counterparts requires the understanding of the vibrational properties of glasses. Due to recent advances of computational techniques, we are now able to study simulated glasses with a wide range of vibrational properties, which is essential to understanding their role […]

P.E.S.T.L.E. Orientation – October 14

https://upenn.zoom.us/j/96715197752

Join PESTLE for our Zoom Orientation session on Friday, October 14 at 4:00 pm! Please email us at pestle@seas.upenn.edu if you have any questions.

MEAM Seminar: “Exergy-based Methods as a Promising Modern Thermodynamic Evaluation and Optimization Tool”

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

Exergy-based methods are powerful tools for developing, evaluating, understanding, and improving energy conversion systems. In addition to conventional methods, advanced exergy-based analyses consider (a) the interactions among components of the overall system, and (b) the real potential for improving each important system component. The main role of an advanced analysis is to provide energy conversion […]

CIS Seminar: “Equilibrium Complexity and Deep Learning”

Wu and Chen Auditorium (Room 101), Levine Hall 3330 Walnut Street, Philadelphia, PA, United States

Deep Learning has recently made significant progress in learning challenges such as speech and image recognition, automatic translation, and text generation, much of that progress being fueled by the success of gradient descent-based optimization methods in computing local optima of non-convex objectives. From robustifying machine learning models against adversarial attacks to causal inference, training generative models, multi-robot interactions, […]

ASSET Seminar: New approaches to detecting and adapting to domain shifts in machine learning, Zico Kolter, Ph.D. (Carnegie Mellon University)

Levine 307 3330 Walnut Street, Philadelphia, PA, United States

ABSTRACT: Machine learning systems, in virtually every deployed system, encounter data from a qualitatively different distribution than what they were trained upon.  Effectively dealing with this problem, known as domain shift, is thus perhaps the key challenge in deploying machine learning methods in practice.  In this talk, I will motivate some of these challenges in […]