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ASSET Seminar: “From Data to Insights: Trustworthy Solutions for Imaging Problems”

Amy Gutmann Hall, Room 414 3333 Chestnut Street, Philadelphia, United States

Abstract:  Extracting insights from imaging data used to be straightforward: every component of imaging systems was engineered by humans, the analysis and interpretation of the collected data was driven by human understanding and experience, and only humans were responsible for the impact of the decisions stemming from such insights. Today, however, machine learning permeates every […]

IDEAS/STAT Optimization Seminar: “Data-Driven Algorithm Design and Verification for Parametric Convex Optimization”

Amy Gutmann Hall, Room 414 3333 Chestnut Street, Philadelphia, United States

Zoom link https://upenn.zoom.us/j/98220304722   Abstract We present computational tools for analyzing and designing first-order methods in parametric convex optimization. These methods are popular for their low per-iteration cost and warm-starting capabilities. However, precisely quantifying the number of iterations required to compute high-quality solutions remains a key challenge, especially in real-time applications. First, we introduce a […]

CIS Seminar: “Pareto-efficient AI systems: Expanding the quality and efficiency frontier of AI”

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

We have made exciting progress in AI by massive models on massive amounts of data center compute. However, the demands for AI are rapidly expanding. I identify how to maximize performance under any compute constraint, expanding the Pareto frontier of AI capabilities.   This talk builds up to an efficient language model architecture that expands […]

ASSET Seminar: “Algorithmic Stability for Trustworthy Machine Learning and Statistics”

Amy Gutmann Hall, Room 414 3333 Chestnut Street, Philadelphia, United States

Abstract: Data-driven systems hold immense potential to positively impact society, but their reliability remains a challenge. Their outputs are often too brittle to changes in their training data, leaving them vulnerable to data poisoning attacks, prone to leaking sensitive information, or susceptible to overfitting. Establishing fundamental principles for designing algorithms that are both stable—to mitigate these […]

IDEAS/STAT Optimization Seminar: “Statistics-Powered ML: Building Trust and Robustness in Black-Box Predictions”

Amy Gutmann Hall, Room 414 3333 Chestnut Street, Philadelphia, United States

Zoom link: https://upenn.zoom.us/j/98220304722 Abstract: Modern ML models produce valuable predictions across various applications, influencing people’s lives, opportunities, and scientific advancements. However, these systems can fail in unexpected ways, generating unreliable inferences and perpetuating biases present in the data. These issues are particularly troubling in high-stakes applications, where models are trained on increasingly diverse, incomplete, and […]

ASSET Seminar: “Controlling Language Models”

Amy Gutmann Hall, Room 414 3333 Chestnut Street, Philadelphia, United States

Abstract: Controlling language models is key to unlocking their full potential and making them useful for downstream tasks. Successfully deploying these models often requires both task-specific customization and rigorous auditing of their behavior. In this talk, I will begin by introducing a customization method called Prefix-Tuning, which adapts language models by updating only 0.1% of […]

IDEAS/STAT Optimization Seminar: “The Size of Teachers as a Measure of Data Complexity: PAC-Bayes Excess Risk Bounds and Scaling Laws”

Amy Gutmann Hall, Room 414 3333 Chestnut Street, Philadelphia, United States

Zoom link: https://upenn.zoom.us/j/98220304722 Abstract: We study the generalization properties of neural networks through the lens of data complexity.  Recent work by Buzaglo et al. (2024) shows that random (nearly) interpolating networks generalize, provided there is a small ``teacher'' network that achieves small excess risk. We give a short single-sample PAC-Bayes proof of this result and […]

Celebration of Community

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

The Cora Ingrum Center for Community and Outreach is planning its annual Celebration of Community gala to showcase Penn Engineering students, staff, and faculty in their multi-talented richness. The event will consist of guest speakers, performances, presentations from student groups, and a variety of cuisines. Do not hesitate to contact Dr. Ocek Eke (ocek@seas.upenn.edu) and […]

Frontiers in Science: Engineering RNA and AI

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

This free, half-day event for undergraduates is hosted by the National Science Foundation Artificial Intelligence-driven RNA BioFoundry (AIRFoundry). Led by experts from the University of Pennsylvania and University of Puerto Rico, Mayagüez, the event will highlight how AI can enhance RNA, with applications in medicine, agriculture and more. Students will also be able to tour cutting-edge labs and engage […]

AI Across Disciplines: A Penn Initiative

Amy Gutmann Hall, Auditorium 3333 Chestnut Street, Philadelphia, PA, United States

Join President J. Larry Jameson and Provost John L. Jackson, Jr. along with the Penn AI Council for the launch of Penn AI. Senior Vice Provost for Research Dawn Bonnell will lead a discussion with members of the Penn AI Council to explore AI’s societal implications and Penn’s potential to influence a more sustainable future. […]