Penn Engineering Commencement 2023: Doctoral Ceremony
Celebrate Penn Engineering’s 2023 Doctoral Graduates. Additional information is available on the Penn Engineering Commencement website.
CIS Seminar: “Collaborative, Communal, & Continual Machine Learning”
Pre-trained models have become a cornerstone of machine learning thanks to the fact that they can provide improved performance with less labeled data on downstream tasks. However, these models are […]
Reflections by 50 Years of Women CIS Faculty
Please join Ruzena Bajcsy, Susan Davidson, Stephanie Weirich and Linh Phan for a panel discussion. Reception to Follow ABSTRACT: Women have always been part of computing at Penn, from the […]
CIS Seminar: “Designing Provably Performant Networked Systems”
As networked systems become critical infrastructure, their design must reflect their new societal role. Today, we build systems with hundreds of heuristics but often do not understand their inherent and […]
CIS Seminar: ” Foundations of Responsible Machine Learning”
Algorithms make predictions about people constantly. The spread of such prediction systems has raised concerns that machine learning algorithms may exhibit problematic behavior, especially against individuals from marginalized groups. This […]
CIS Seminar: ” E=Graphs for Next-Gen Programming Language Tools”
Building a state-of-the-art program optimizer, synthesizer, or verifier is still a gargantuan task for even programming language (PL) experts. Much of this challenge stems from the fact that term rewriting, […]
CIS Seminar: “The Design of a General-Purpose Distributed Execution System”
Scaling applications with distributed execution has become the norm. With the rise of big data and machine learning, more and more developers must build applications that involve complex and data-intensive […]
CIS Seminar: “Privacy-Preserving Accountability Online”
Technologies that enable confidential communication and anonymous authentication are important for improving privacy for users of internet services. Unfortunately, encryption and anonymity, while good for privacy, make it hard to […]
ASSET Seminar: Machine Learning: A Data-Centric Perspective, Aleksander Madry (Massachusetts Institute of Technology)
ABSTRACT: The training data that modern machine learning models ingest has a major impact on these models’ performance (as well as failures). Yet, this impact tends to be neither fully […]
ASSET Seminar: Lockout: Sparse Regularization of Neural Networks, Gilmer Valdes (UCSF)
ABSTRACT: Many regression and classification procedures fit a function f(x;w) of predictor variables x to data 〖{x_i,y_i}〗_1^N based on some loss criterion L(y,f(x;w)). Often, regularization is applied to improve accuracy […]