Coupling of spins and light can enable photon-mediated scaling and control in quantum technologies, as demonstrated in trapped atom, ion, and solid-state spin qubits. Molecular analogs of such systems hold […]
Understanding single-cell heterogeneity in biological systems is considered the holy grail of biomedicine. However, conventional single-cell analysis methods are constrained by the destructive readout process of DNA barcodes and the […]
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 […]
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 […]
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 […]
Polymers are ubiquitous in both synthetic and biological materials and underlie technologies as diverse as surfactants, adhesives, proteins and DNA. One of the defining features of all polymeric materials is […]
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 […]
In this guest lecture, we will cover two recent research thrusts from the RobotiXX lab in taking RoboRacer off-road: high-speed off-road navigation and wheeled mobility on vertically challenging terrain. For […]
The compute demands of AI and robotics continue to rise due to the rapidly growing volume of data to be processed; the increasingly complex algorithms for higher quality of results; […]
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 […]
