LabCompass

Beatrice Bevilacqua

Computer Science · Purdue University West Lafayette

OpenAlex
Early career · publishing since 2020Rising activity
Advanced Graph Neural NetworksGraph Theory and AlgorithmsTopic ModelingComplex Network Analysis TechniquesNeural Networks and Applications

Publications

31

Citations

159

Est. group size

—

Recurring co-author estimate

Active years

7

Publishing since 2020

Research summary
AI-generated

Beatrice Bevilacqua works on graph neural networks (machine learning methods designed to process data structured as graphs, such as molecules or networks), focusing on making these models more expressive, robust, and generalizable. Her recent work includes designing new normalization and activation techniques for graph models, building 'graph foundation models' that work across different types of graph data, and applying machine learning to predict how cells respond to biological perturbations. This combination suggests interests spanning core graph learning theory and its application to computational biology.

Graph Neural NetworksGraph Representation Learning and SymmetryNeural Algorithmic ReasoningGraph Foundation ModelsMachine Learning for Computational Biology

Publication output has grown over the past decade, rising from no recorded outputs before 2020 to a steady multi-paper-per-year pace since 2021, with a notable increase in 2026 entries.

Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026

Publication cadence
Publications per year over the last 10 years — averaging 5.2/year recently
1718192020: 1 publication202021: 4 publications212022: 3 publications222023: 6 publications232024: 5 publications242025: 4 publications252026: 8 publications826
Recent publications
Publishes in
  • arXiv (Cornell University)×18
  • Zenodo (CERN European Organization for Nuclear Research)×5
  • IEEE Access×1
  • Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences×1
  • bioRxiv (Cold Spring Harbor Laboratory)×1
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This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.

Last updated Jul 20, 2026.

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