IDEAS/STAT Optimization Seminar
Amy Gutmann Hall, Room 414 3333 Chestnut Street, Philadelphia, United StatesZoom link: https://upenn.zoom.us/j/98843354016
Zoom link: https://upenn.zoom.us/j/98843354016
Algorithms are increasingly integrated into various societal applications, often directly interacting with people and communities. This highlights the importance of understanding the interplay between algorithmic decisions and economic incentives when […]
Deep learning's success stems from the ability of neural networks to automatically discover meaningful representations from raw data. In this talk, I will describe some recent insights into how optimization […]
To meet net-zero carbon emissions targets by mid-century, up to a -fold increase in wind power capacity is required. Acceleration to this rate requires urgent improvements to efficiency and reliability […]
Conventional approaches to scientific discovery often prioritize building larger sensors, gathering more data, and scaling up computational power. In this talk, I will present a complementary perspective: extracting insights hidden […]
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 […]
As learning algorithms become increasingly capable of acting autonomously, it is important to better understand the behavior that results from their interactions. For example, a pervasive challenge in multi-agent learning […]
Understanding the dynamic behavior of complex biomolecules requires simplified models that not only make computations feasible but also reveal fundamental mechanisms. Coarse-graining (CG) achieves this by grouping atoms into beads, […]
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 […]
Team theory is a mathematical formalism for decentralized stochastic control problems in which a “team,” consisting of a number of members, cooperates to achieve a common objective. It was developed […]