ESE Spring Seminar – “Can Robots Learn from Machine Dreams? – Robot Learning via GenAI-powered World Models”
Over the past decade, large-scale pre-training followed by alignment has revolutionized natural language processing and computer vision. Yet, robotics remains constrained by the scarcity of real-world data. In this talk, […]
MEAM Seminar: “Neural Operator for Scientific Computing”
Accurate simulations of physical phenomena governed by partial differential equations (PDEs) are foundational to scientific computing. While traditional numerical methods have proven effective, they remain computationally intensive, particularly for complex, […]
ESE Spring Seminar – “Generalization, Memorization, and Privacy in Trustworthy Machine Learning”
Machine learning is transforming numerous aspects of modern society, and its expanding use in high-stakes applications calls for responsible development. In this talk, I will present my research on the […]
IDEAS/STAT Optimization Seminar: “Negative Stepsizes Make Gradient-Descent-Ascent Converge”
Zoom link: https://upenn.zoom.us/j/98220304722 Abstract: Solving min-max problems is a central question in optimization, games, learning, and controls. Arguably the most natural algorithm is Gradient-Descent-Ascent (GDA), however since the 1970s, conventional wisdom […]
IDEAS/STAT Optimization Seminar: Resilient Distributed Optimization for Cyberphysical Systems
Zoom link: https://upenn.zoom.us/j/98220304722 Abstract: This talk considers the problem of resilient distributed multi-agent optimization for cyberphysical systems in the presence of malicious or non-cooperative agents. It is assumed that […]
IDEAS/STAT Optimization Seminar: “Gradient Equilibrium in Online Learning”
We present a new perspective on online learning that we refer to as gradient equilibrium: a sequence of iterates achieves gradient equilibrium if the average of gradients of losses along […]
IDEAS/STAT Optimization Seminar: “Stochastic-Gradient-based Algorithms for Solving Nonconvex Constrained Optimization Problems”
Zoom link: https://upenn.zoom.us/j/98220304722 Abstract I will present recent work by my research group on the design and analysis of stochastic-gradient-based algorithms for solving nonconvex constrained optimization problems, which may […]
IDEAS/STAT Optimization Seminar: “The Size of Teachers as a Measure of Data Complexity: PAC-Bayes Excess Risk Bounds and Scaling Laws”
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 […]
IDEAS/STAT Optimization Seminar: “Statistics-Powered ML: Building Trust and Robustness in Black-Box Predictions”
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 […]
IDEAS/STAT Optimization Seminar: “Data-Driven Algorithm Design and Verification for Parametric Convex Optimization”
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 […]