Hypergraphs extend graphs by allowing hyperedges to connect any number of nodes, naturally capturing higher-order relationships. This talk presents three lines of work that leverage hypergraphs for optimization. First, we formulate team formation--assigning agents to tasks under energy constraints--as a constrained hypergraph discovery problem, maximizing resilience via the algebraic connectivity of the hypergraph Laplacian. A constrained simulated annealing algorithm outperforms greedy baselines on scientific collaboration datasets. Second, we show that edge-dependent vertex weight hypergraphs offer a richer representation for domains such as single-cell RNA sequencing, where random walks on a hypergraph (with cells as nodes and genes as hyperedges) yield cell embeddings that improve clustering compared to standard co-expression graphs. Third, we introduce HypOp, a distributed learning-based solver for constrained combinatorial optimization that models problems as constraint hypergraphs and uses hypergraph neural networks to find solutions. HypOp achieves competitive performance with significantly lower runtime than simulated annealing and gradient descent baselines, scales through federated distributed training, and supports transfer learning across different optimization problems on the same graph. Together, these results demonstrate that rethinking optimization through hypergraphs enables more expressive representations, more resilient solutions, and more scalable algorithms.

Bio: Tina Eliassi-Rad is a Professor of Computer Science and The Inaugural Joseph E. Aoun Chair at Northeastern University. She is also a core faculty member at Northeastern's Network Science Institute. In addition, she is an external faculty member at the Santa Fe Institute and the Vermont Complex Systems Institute. Prior to joining Northeastern, Tina was an Associate Professor of Computer Science at Rutgers University; and before that she was a member of technical staff and principal investigator at Lawrence Livermore National Laboratory. Tina earned her Ph.D. in Computer Sciences (with a minor in Mathematical Statistics) at the University of Wisconsin-Madison. Her research is at the intersection of data mining, machine learning, and network science. She has over 150 peer-reviewed publications (including a few best paper and best paper runner-up awards); and has given over 300 invited talks and 14 tutorials. Tina's work has been applied to personalized search on the World-Wide Web, statistical indices of large-scale scientific simulation data, fraud detection, mobile ad targeting, cyber situational awareness, drug discovery, democracy and online discourse, and ethics in machine learning. Her algorithms have been incorporated into systems used by governments and industry (e.g., IBM System G Graph Analytics), as well as open-source software (e.g., Stanford Network Analysis Project). Tina received an Outstanding Mentor Award from the U.S. Department of Energy's Office of Science in 2010, became an ISI Foundation Fellow in 2019, was named one of the 100 Brilliant Women in AI Ethics in 2021, received Northeastern University's Excellence in Research and Creative Activity Award in 2022, was awarded the Lagrange Prize in 2023, and was elected Fellow of the Network Science Society in 2023.

Web page: https://eliassi.org/