Tina Eliassi-Rad

About

Tina Eliassi-Rad is the inaugural Joseph E. Aoun Professor at Northeastern University, based in Boston. She is a core faculty member at Northeastern's Network Science Institute and the Institute for Experiential AI, as well as an external faculty member at the Santa Fe Institute and the Vermont Complex Systems Center. Her research is at the intersection of data mining, machine learning, and network science. She has authored more than 100 peer-reviewed publications, including best paper awards, and has delivered over 200 invited talks and 14 tutorials. Eliassi-Rad's work has been applied to various domains such as personalized searches 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, including IBM System G Graph Analytics, as well as open-source software like the Stanford Network Analysis Project. She has served as program co-chair for major conferences including the ACM International Conference on Knowledge Discovery and Data Mining, the International Conference on Network Science, and the International Conference on Computational Social Science. Her accolades include an Outstanding Mentor Award from the US Department of Energy's Office of Science, being named an ISI Foundation Fellow, recognition as one of the…

Selected publications

• Improving the generalizability of protein-ligand binding predictions with AI-Bind

  Nature Communications · 2023-04-08 · 153 citations

  articleOpen access

  Identifying novel drug-target interactions is a critical and rate-limiting step in drug discovery. While deep learning models have been proposed to accelerate the identification process, here we show that state-of-the-art models fail to generalize to novel (i.e., never-before-seen) structures. We unveil the mechanisms responsible for this shortcoming, demonstrating how models rely on shortcuts that leverage the topology of the protein-ligand bipartite network, rather than learning the node featu…

  PublisherOA PDFDOI

• Measuring algorithmically infused societies

  Nature · 2021 · 132 citations

  Senior author
  DOI

• Human-AI coevolution

  Artificial Intelligence · 2024-11-13 · 49 citations

  articleOpen access

  Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature. Recommender systems and assistants play a prominent role in human-AI coevolution, as they permeate many facets of daily life and influence human choices through online platforms. The interaction between users and AI results in a potentially endless feedback loop, wh…

  PublisherDOI

• A Survey on Hypergraph Mining: Patterns, Tools, and Generators

  ACM Computing Surveys · 2025-02-20 · 30 citations

  reviewOpen access

  Hypergraphs, which belong to the family of higher-order networks, are a natural and powerful choice for modeling group interactions in the real world. For example, when modeling collaboration networks, which may involve not just two but three or more people, the use of hypergraphs allows us to explore beyond pairwise (dyadic) patterns and capture groupwise (polyadic) patterns. The mathematical complexity of hypergraphs offers both opportunities and challenges for hypergraph mining. The goal of h…

  PublisherDOI

• Distributed constrained combinatorial optimization leveraging hypergraph neural networks

  Nature Machine Intelligence · 2024-05-30 · 21 citations

  article
  PublisherDOI

Labs

• Rad LabPI

Awards & honors

• Outstanding Mentor Award from the US Department of Energy's…
• ISI Foundation Fellow (2019)
• One of the 100 Brilliant Women in AI Ethics (2021)
• Northeastern University's Excellence in Research and Creativ…
• Lagrange-CRT Foundation Prize (2023)

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