As a Postdoctoral Associate at Duke University working in the lab of Michael Zavlanos, I lead efforts in the development of new theories, algorithms, and software for the analysis of networked autonomous systems, including the development of relevant data-driven and machine learning techniques. I interact with and supervise graduate and undergraduate students in our group working on relevant research topics.
My other research interests include multi-agent systems, geometric deep learning, lattice theory and applied topology.
Twitter Highlights
I'm excited to share our new paper w/ @mungowitz and @m_zavlanos introducing a decentralized mechanism for pricing and exchanging alternatives constrained by transaction costs: a thread 🧵 1/n pic.twitter.com/9MIQw0qsic
— Hans Riess (@hansmriess) April 18, 2023
parallel transport is the fundamental concept behind vector diffusion pic.twitter.com/xQS4TZPSEi
— Hans Riess (@hansmriess) February 28, 2023
what is a Laplacian? in the discrete domain where data is assigned to nodes of a network, maybe it's best defined as an operator that drives harmonic flow. in this video, nodes pass messages to neighbors, update their priors, and eventually reach consensus on their color. pic.twitter.com/XeQItqpjCT
— Hans Riess (@hansmriess) September 21, 2021