Distinguished Lecture Seminar — Tina Eliassi-Rad

  • By: jorgema

The Computer Science Department invites you to join us for an upcoming Distinguished Lecture Seminar featuring Tina Eliassi-Rad.

Lecture Details

Title: Rethinking Optimization through Hypergraphs
Date: Friday, April 3
Time: 11:00 AM – 12:30 PM
Location: STO B50

About the Talk

Abstract: 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.

Speaker Bio

Tina Eliassi-Rad is the Inaugural Joseph E. Aoun Professor at Northeastern University. She is also 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 a member of the technical staff at Lawrence Livermore National Laboratory. She earned her Ph.D. in Computer Sciences (with a minor in Mathematical Statistics) at the University of Wisconsin-Madison. Tina works at the intersection of AI and network science and is interested in the impact of science and technology on society. Her algorithms have been integrated into systems used by governments, industry, and open-source software. 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.

We encourage all students, faculty, and staff to attend this exciting and insightful lecture.

 

 

View all posts