Hans Riess

Research Scientist, Georgia Tech

riess@gatech.edu
RSS 440A

Bio

Dr. Hans Riess is an applied mathematician and 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, machine learning, and optimization. At Georgia Tech, Hans also works closely with 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 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 Institute of Technology.

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Publications

Journal Publications

Ghrist, R., Lopez, M., North, P.R., Riess, H. (2025). Categorical diffusion on weighted lattices. Submitted.

Battiloro, C., Wang, Z., Riess, H., Di Lorenzo, P., Ribeiro, A. (2023). Tangent bundle convolutional learning: from manifolds to cellular sheaves and back, in IEEE Transactions on Signal Processing.

Ghrist, R., & Riess, H. (2022). Cellular sheaves of lattices and the Tarski Laplacian, in Homology, Homotopy and Applications, 24(1), 325-345.

Catanzaro, M. J., Curry, J. M., Fasy, B. T., Lazovskis, J., Malen, G., Riess, H., Wang, B., & Zabka, M. (2020). Moduli spaces of morse functions for persistence, in Journal of Applied and Computational Topology, 4(3), 353-385.

Conference Proceedings

Hanks, T., Riess, H., Cohen, S., Gross, T., Hale, M., Fairbanks, J. (2025). Distributed multi-agent coordination over cellular sheaves. Submitted.

Konti, X., Riess, H., Giannopoulos, M., Shen, Y., Pencina, M., Economou-Zavlanos, N., Zavlanos, M. (2024). Distributionally robust clustered federated learning: a case study in healthcare, to appear in 63rd IEEE Conference on Control and Decision Systems (CDC), Milan.

Riess, H., Henselman-Petrusek, G., Munger, M., Ghrist, R., Bell, Z., & Zavlanos, M. (2023). Network preference dynamics using lattice theory, in 2024 American Control Conference, Toronto.

Hayhoe, M., Riess, H., Preciado, V., & Ribeiro, A. (2023). Transferable hypergraph neural networks via spectral similarity, in Second Learning on Graphs Conference, virtual.

Riess, H., Munger, M., & Zavlanos, M. (2023). Max-Plus synchronization in decentralized trading systems, in 2023 IEEE 62nd Conference on Decision and Control (CDC), Singapore.

Battiloro, C., Wang, Z., Riess, H., Di Lorenzo, P., & Ribeiro, A. (2022). Tangent bundle filters and neural networks: from manifolds to cellular sheaves and back, in 2023 Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Riess, H., Ghrist, R. (2022). Diffusion of information on networked lattices by gossip, in IEEE 61st Conference on Decision and Control (CDC), Cancun, Mexico, 5946-5952.

Riess, H., Kantaros, Y., Pappas, G., & Ghrist, R. (2021). A Temporal logic-based hierarchical network connectivity controller, in 2021 Proceedings of the Conference on Control and its Applications, virtual.

Riess, H., & Hansen, J. (2020). Multidimensional persistence module classification via lattice-theoretic convolutions. In NeurIPS Workshop on Topological Data Analysis and Beyond.

Preprints

Ghrist, R., Gould, J., Lopez, M., Riess, H. (2025). Clearing sections of lattice liability networks.

Riess, H., Veveakis, M., Zavlanos, M. (2024). Path signatures and graph neural networks for slow earthquake analysis: better together?.

Parada-Mayorga, A., Riess, H., Ribeiro, A., & Ghrist, R. (2020). Quiver signal processing (QSP).

Thesis

Riess, H. (2022). Lattice theory in multi-agent systems. Doctor of Philosophy, University of Pennsylvania.

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