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This website is currently under construction.
Dr. Hans Riess is a mathematical engineer who currently serves as a Research Scientist in the School of Electrical and Computer Engineering at the Georgia Institute of Technology. Hans has established an independent research program which leverages category theory and algebraic topology to drive innovations in multi-agent systems, optimization, and artificial intelligence. At Georgia Tech, Hans works in the Control, Optimization, and Robotics Engineering (CORE) Lab, directed by Dr. Matthew Hale, which engages in research activities ranging from hands-on robotics to developing sophisticated mathematical optimization techniques. In 2016, Hans earned a B.S. in pure Mathematics from Duke University, where he completed the entire Ph.D.-level topology sequence. He later completed his Ph.D. in Electrical and Systems Engineering (ESE) at the University of Pennsylvania in 2022, working under the supervision of Professor Robert Ghrist to make advances in cellular sheaf theory. Following his doctorate, in 2022, he joined the Autonomous Systems Lab at Duke University, directed by Dr. Michael M. Zavlanos, and, in 2025, he joined the research faculty at the Georgia Tech.
Dr. Hans Riess is a Research Scientist in the School of Electrical and Computer Engineering at the Georgia Institute of Technology, where he directs an independent research program. His work leverages category theory and algebraic topology to drive innovations in multi-agent systems, optimization, and artificial intelligence.
Claudio Battiloro, Zhiyang Wang, Hans Riess, Paolo Di Lorenzo, Alejandro Ribeiro (2024). Tangent bundle convolutional learning: from manifolds to cellular sheaves and back. IEEE Transactions on Signal Processing 72, 1892-1909.
Robert Ghrist, Hans Riess (2022). Cellular sheaves of lattices and the Tarski Laplacian. Homology, Homotopy and Applications 24(1), 325-345.
Michael Catanzaro, Justin Curry, Brittany Fasy, Jānis Lazovskis, Greg Malen, Hans Riess, Bei Wang, Matthew Zabka (2020). Moduli spaces of Morse functions for persistence. Journal of Applied and Computational Topology 4(3), 335-385.
Tyler Hanks, Hans Riess, Samuel Cohen, Trevor Gross, Hatthew Hale, James Fairbanks (2025). Distributed multi-agent coordination over cellular sheaves, in 2025 IEEE 64th Conference on Decision and Control (to appear).
Xenia Konti, Hans Riess, Manos Giannopoulos, Yi Shen, Michael Pencina, Nicoleta Economou-Zavlanos, Michael Zavlanos (2024). Distributionally robust clustered federated learning: a case study in healthcare, in 2024 IEEE 63rd Conference on Decision and Control (CDC).
Hans Riess, Gergory Henselman-Petrusek, Michael Munger, Robert Ghrist, Zachary Bell, Michael Zavlanos (2024). Network preference dynamics using lattice theory, in 2024 American Control Conference.
Hans Riess, Michael Munger, Michael Zavlanos (2023). Max-plus synchronization in decentralized trading systems, in 2023 62nd IEEE Conference on Decision and Control (CDC).
Claudio Battiloro, Zhiyang Wang, Hans Riess, Paolo Di Lorenzo, Alejandro Ribeiro (2023). Tangent bundle filters and neural networks: from manifolds to cellular sheaves and back, in 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP).
Mikhail Hayhoe, Hans Riess, Michael Zavlanos, Victor Preciado, Alejandro Ribeiro (2023). Transferable hypergraph neural networks via spectral similarity, in The Second Learning on Graphs Conference.
Hans Riess, Robert Ghrist (2022). Diffusion of information on networked lattices by gossip, in 2022 IEEE 61st Conference on Decision and Control (CDC).
Hans Riess, Yiannis Kantaros, George Pappas, Robert Ghrist (2021). A temporal logic-based hierarchial network connectivity controller, in SIAM Conference on Control and its Applications.
Hans Riess, Jakob Hansen, Robert Ghrist (2020). Multidimensional persistence module classification via lattice-theoretic convolutions, in NeurIPS Topological Data Analysis and Beyond Workshop.
Robert Ghrist, Miguel Lopez, Paige Randall North, Hans Riess (2025). Categorical diffusion of weighted lattices.
Hans Riess, Manolis Veveakis, Michael Zavlanos (2024). Path signature and graph neural networks for slow earthquake analysis: better together?.
Alejandro Parada-Mayorga, Hans Riess, Robert Ghrist, Alejandro Ribeiro (2020). Quiver signal processing.
Hans Riess (2022). Lattice theory in multi-agent systems.
" The beauty of mathematics is that you can change the problem and the rules as you wish. It’s like trying to solve a computer game with unlimited cheat codes. "
Office 440A
Tech Square Research Building
85 5th St NW
Atlanta, Georgia 30332
United States of America
riess@gatech.edu