Beining Zhang

AI/ML Engineer & Researcher building trustworthy AI systems for Finance and Health.

Beining Zhang
About Industry experience Research experience Education Skills Beining's takeaway Talks & Activities
Section 3 of 7

Research experience

Trustworthy AI research across LLM evaluation, participatory design, and health applications.

Research 01
Research Collaborator · Harvard Health System Innovation Lab Nov 2024 – Present

Automated medical research classification at scale.

Outcomes
  • Built a few-shot LLM classifier (GPT-4o-mini + RF ensemble) automating classification of a 200,000-record cancer research dataset with 93% human-machine agreement — reducing manual labelling effort by over 80%.
  • Designed a precision-first abstention mechanism that eliminated human verification of positive decisions, enabling clinical experts to focus on genuinely ambiguous cases.
  • Co-authored: Public and philanthropic research funding, publications, and research networks for cancer in the Commonwealth and globally between 2016 and 2023: a comparative analysis, The Lancet Oncology 26(9), e466–e476, 2025.
  • Invited to the Lancet Commissioners meeting to advise on AI's role in global cancer control and research equity.
Research 02
Research Assistant · University of Southampton Jun 2024 – Present

AI agent evaluation, governance, and participatory design.

Focus
  • Achieved top ranking at the Concordia Contest 2024 with an LLM agent architecture for cooperative multi-agent interaction — published at NeurIPS Datasets and Benchmarks Track 2025.
  • Researching AI governance, explainability, and public engagement in participatory agentic system design — paper under review at ACM CHI.
Research 03
Cooperative AI Summer School 2025

Intensive research training in multi-agent evaluation and cooperation.

Work
  • Selected participant; developed evaluation metrics for exploitability in multi-agent systems.
Core themes
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Evaluation-first
Define what 'trustworthy' means, then measure it.
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Grounded generation
Retrieval and citations to constrain outputs in high-stakes settings.
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Participatory design
Design with the people affected — not just for them.
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