Women in Data Science @ Penn
The Wharton School and Penn Engineering are proud to host the fifth annual Women in Data Science (WiDS) @ Penn Conference on February 8-9, 2024, on the University of Pennsylvania’s […]
Evolution of Data Storytelling: Women in Data Science x Penn Museum Tour + Workshop
Join us for an exciting kick-off event at the Penn Museum as part of the Women in Data Science (WiDS) @ Penn conference, where the past meets the future in a guided tour and storytelling workshop.
ESE & CIS Spring Seminar – “Towards Transparent Representation Learning”
Machine learning models trained on vast amounts of data have achieved remarkable success across various applications. However, they also pose new challenges and risks for deployment in real-world high-stakes domains. […]
ESE & CIS Spring Seminar – “Beyond the black box: characterizing and improving how neural networks learn”
The predominant paradigm in deep learning practice treats neural networks as “black boxes”. This leads to economic and environmental costs as brute-force scaling remains the performance driver, and to safety […]
CIS Seminar: “Accessible Foundation Models: Systems, Algorithms, and Science”
The ever-increasing scale of foundation models, such as ChatGPT and AlphaFold, has revolutionized AI and science more generally. However, increasing scale also steadily raises computational barriers, blocking almost everyone from […]
CIS Seminar: “Rethinking Data Use in Large Language Models”
Large language models (LMs) such as ChatGPT have revolutionized natural language processing and artificial intelligence more broadly. In this talk, I will discuss my research on understanding and advancing these […]
ASSET Seminar: “Bridging the Gap Between Deep Learning Theory and Practice” (Micah Goldblum, New York University)
ABSTRACT: Despite the widespread proliferation of neural networks, the mechanisms through which they operate so successfully are not well understood. In this talk, we will first explore empirical and theoretical […]
ASSET Seminar: “Making Machine Learning Predictably Reliable” (Andrew Ilyas, Massachusetts Institute of Technology)
ABSTRACT: Despite ML models’ impressive performance, training and deploying them is currently a somewhat messy endeavor. But does it have to be? In this talk, I overview my work on […]
ASSET Seminar: “Paths to AI Accountability” (Sarah Cen, Massachusetts Institute of Technology)
ABSTRACT: In the past decade, we have begun grappling with difficult questions related to the rise of AI, including: What rights do individuals have in the age of AI? When […]
ASSET Seminar: “Mathematical Foundations for Physical Agents” (Max Simchowitz, Massachusetts Institute of Technology, CSAIL)
ABSTRACT: From robotics to autonomous vehicles, machine learning agents deployed in the physical world (“physical agents”) promise to revolutionize endeavors ranging from manufacturing to agriculture to domestic labor. In this […]