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CIS Seminar: “Realizing the Promise of Language-level Security in Real Systems”

Levine 307 3330 Walnut Street, Philadelphia, PA, United States

Promises are cheap. Software vendors routinely describe their offerings as “secure”, but few are based on designs that can guarantee even the most basic security properties. To address this problem, services like Cloudflare, Android, and Firefox are increasingly relying on languages like Rust and WebAssembly to provide safety by design. But these promises too can […]

CIS Seminar: “Privacy, Copyright, and Data Integrity: The Cascading Implications of Generative AI”

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

The rapid adoption of generative AI has created a cycle where personal information cascades perpetually: from people to models to applications and online platforms, then back through scrapers into the system. Simple blanket rules such as "don't train on this data" or "don't share sensitive information" are inadequate, as we face training data scarcity while […]

CIS Seminar: “Intelligence Augmentation for Scientific Researchers”

Special location for this talk: 105  Amy Gutmann Hall Recent advances in Artificial Intelligence are powering revolutionary interactive tools that will transform the very nature of the scientific enterprise. We describe several large-scale projects at the Allen Institute for AI aimed at developing open models, agentic platforms, and novel interaction paradigms in order to amplify […]

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 […]

CIS Seminar: “Next Generation Operating Systems for the Cloud”

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

Modern datacenters must handle an ever-growing array of real-time and data-intensive workloads, such as interactive web services and AI models, that demand both low latency and high throughput. However, traditional operating systems introduce significant I/O overhead, degrading performance and reducing efficiency. A common solution is to let applications directly communicate with hardware, bypassing the operating […]

CIS Seminar: “Probabilistic Experimental Design for Petascale DNA Synthesis”

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

Generative modeling offers a powerful paradigm for designing novel functional DNA, RNA and protein sequences. In this talk, I introduce probabilistic experimental design methods to efficiently manufacture samples from generative models of biomolecules in the real world. These algorithms merge computational techniques for approximate sampling with physical randomness. I also develop tools to rigorously evaluate […]

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 […]