Hans Matthew Riess

Research Scientist, Georgia Institute of Technology

riess@gatech.edu
Office 440A, Techology Square Research Building (TSRB)

Hans joined the faculty at the Georgia Institute of Technology in February 2025, where he serves as 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 leverages category theory and topology to drive innovations in complex systems, machine learning, and optimization.

In 2016, Hans earned a B.S. in Pure Mathematics from Duke University. He later completed his Ph.D. in Electrical and Systems Engineering at the University of Pennsylvania in 2022, working under Robert Ghrist to develop a network sheaf theory for algebraic lattices (see image). Following his doctorate, he joined the Autonomous Systems Lab at Duke University, directed by Michael M. Zavlanos, in 2022.

Algebraic Lattice

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: netter 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.

Talk