Presentation Abstract: There is a significant interest in developing ML algorithms whose final predictions can be explained in domain-specific terms that are understandable to a human. Providing such an “explanation” […]
*This will be a HYBRID Event with in-person attendance in Levine 307 and Virtual attendance via Zoom. In this talk, I will provide a brief introduction about our recent progress […]
For functional materials that are in a nascent stage, such as the antiferromagnetic spintronics, quantum information storage, and new semiconducting compounds, it is not clear what will be the high-performance […]
This is a hybrid event with in-person attendance in Wu and Chen and virtual attendance via Zoom. Dr. James Pikul Simon Kim, AIA (via Zoom) Dr. Rahul Mangharam Dr. Robert Stuart […]
Abstract: In a combinatorial auction there are m items, and each of n players has a valuation function v_i which maps sets of items to non-negative reals. A designer wishes […]
Effective control of epidemics, individualized medicine, and new drugs with virologic response-dependent dose and timing require, among other things, simple, inexpensive, multiplexed molecular detection platforms suitable for point of care […]
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
Ordered sets model signals such as binary relations, concepts, partitions, rankings, matchings, events, as well as other taxa of information, temporal, hierarchical, relational, or, in general, logical in nature. We […]
The study of the early universe requires deep high-resolution maps of the sky at millimeter and submillimeter. This requires the development of state-of-the-art cryogenic receivers and custom built telescopes. These […]
Nanolattices exhibit attractive mechanical, energy conversion, and optical properties, but it is challenging to fabricate nanolattices in large scale while maintaining the dense hierarchical nanometer features that enable their properties. […]
ABSTRACT Traditional convolutional networks exhibit unprecedented robustness to intraclass nuisances when trained on big data. Generalization with respect to geometric transformations has been achieved via expensive data augmentation. It has been shown recently that […]