Hans Matthew Riess

Research Scientist, Georgia Institute of Technology

Hans joined the faculty at the Georgia Institute of Technology in February 2025 where he is a Research Scientist II in the School of Electrical and Computer Engineering. He is affiliated with the Control, Optimization, and Robotics Engineering Lab (directed by Matthew Hale). In his research, Hans utilizes category theory and topology to pioneer advancements in complex systems, machine learning, and optimization. In 2016, Hans received a B.S. in pure mathematics from Duke University. In 2022, he received a Ph.D. in Electrical and Systems Engineering from the University of Pennsylvania where, with Robert Ghrist, he developed a network sheaf theory for algebraic lattices (pictured right). In 2022, he joined the autonomous systems lab at Duke University (directed by Michael M. Zavlanos).

Lattice

Publications

Preprints

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

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

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

Journal Publications

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

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.

Workshop

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

Thesis

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

Talk