ESE Spring Seminar – “Quantum Simulation of Electronic Materials with a Superconducting Qubit Array”
Arrays of coupled superconducting qubits are a compelling platform for analog quantum simulations of solid-state matter and many-body physics. These devices natively emulate the Bose-Hubbard model while offering a high […]
ESE Spring Seminar – “Towards quantum interconnects: entangling microwave and optical photonic qubits”
Modern computing and communication technologies, such as supercomputers and the internet, are based on optically-linked networks of information processors operating at microwave frequencies. An analogous architecture has been proposed for […]
IDEAS/STAT Optimization Seminar: Jason Altschuler
Zoom link: https://upenn.zoom.us/j/98220304722
IDEAS/STAT Optimization Seminar: Angelia Nedich
Zoom link: https://upenn.zoom.us/j/98220304722
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 […]
IDEAS/STAT Optimization Seminar: “ML for an Interactive World: From Learning to Unlearning”
The remarkable recent success of Machine Learning (ML) is driven by our ability to develop and deploy interactive models that can solve complicated tasks by understanding and adapting to the […]