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ASSET Seminar: “Paths to AI Accountability” (Sarah Cen, Massachusetts Institute of Technology)

Raisler Lounge (Room 225), Towne Building 220 South 33rd Street, Philadelphia, PA, United States

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 should we regulate AI and when should we abstain? What degree of transparency is needed to monitor AI systems? These questions are all concerned with […]

CIS Seminar: “Rethinking Data Use in Large Language Models”

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

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 models, centered around how they use the very large text corpora they are trained on. First, I will describe our efforts to understand how these […]

Women in Data Science @ Penn

Jon M. Huntsman Hall 3730 Walnut Street, Philadelphia, PA, United States

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 campus. A celebrated interdisciplinary event, WiDS @ Penn welcomes academic, industry, and student speakers from across the data science landscape to celebrate its diversity, both […]

ESE & CIS Spring Seminar – “Beyond the black box: characterizing and improving how neural networks learn”

Raisler Lounge (Room 225), Towne Building 220 South 33rd Street, Philadelphia, PA, United States

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 issues as robust reasoning and alignment remain challenging. My research opens up the neural network black box with mathematical and statistical analyses of how networks […]

ASSET Seminar: “Enforcing Right to Explanation: Algorithmic Challenges and Opportunities” (Himabindu Lakkaraju, Harvard University)

Raisler Lounge (Room 225), Towne Building 220 South 33rd Street, Philadelphia, PA, United States

ABSTRACT: As predictive and generative models are increasingly being deployed in various high-stakes applications in critical domains including healthcare, law, policy and finance, it becomes important to ensure that relevant stakeholders understand the behaviors and outputs of these models so that they can determine if and when to intervene. To this end, several techniques have […]

CIS Seminar: “Accessible Foundation Models: Systems, Algorithms, and Science”

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

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 studying, adapting, or otherwise using these models for anything beyond static API queries. In this talk, I will present research that significantly lowers these barriers […]

ASSET Seminar: “Mathematical Foundations for Physical Agents” (Max Simchowitz, Massachusetts Institute of Technology, CSAIL)

Raisler Lounge (Room 225), Towne Building 220 South 33rd Street, Philadelphia, PA, United States

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 talk, we will develop mathematical foundations, from the ground up, for how to carry out this vision. We will begin our investigation by examining linear […]

ESE & CIS Spring Seminar – “Towards Transparent Representation Learning”

Raisler Lounge (Room 225), Towne Building 220 South 33rd Street, Philadelphia, PA, United States

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. Decisions made by deep learning models are often difficult to interpret, and the underlying mechanisms remain poorly understood. Given that deep learning models operate as […]

CIS Seminar: “Decision Making with Internet-Scale Knowledge”

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

Machine learning models pretrained on internet data have acquired broad knowledge about the world but struggle to solve complex tasks that require extended reasoning and planning. Sequential decision making, on the other hand, has empowered AlphaGo’s superhuman performance, but lacks visual, language, and physical knowledge about the world. In this talk, I will present my […]