AI Engineer & Researcher

Beining Zhang

AI/ML engineer & researcher building trustworthy LLM systems (evaluation, grounded generation, and participatory design).

Beining Zhang
Overview Industry experience Research experience Education Skills Beining's takeaway Talks & Activities

Publications

Peer-reviewed papers and workshop publications across LLM agents, evaluation, and applied AI.

Selected work first, then the full list — with quick visual cues for type and year.
You can also find my articles on my Google Scholar.

Journal Articles

Public and Philanthropic Research Funding, Publications and Research Networks for Cancer in the Commonwealth and Globally in 2016-2023: Comparative Analysis

Journal 2025

Published in The Lancet Oncology, 2025

Recommended citation: Du A, Brede M, McIntosh S, Zhang B, et al. (2025). "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.
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Conference Papers

Three Lessons from Citizen-Centric Participatory AI Design

Workshop 2026

Published in PARTICIPATE-AI: Exploring the Participatory Turn in Citizen-Centred AI (IUI '26 Workshop), 2026

Recommended citation: Schneiders et al. (2026). "Three Lessons from Citizen-Centric Participatory AI Design." In PARTICIPATE-AI Workshop at ACM IUI 2026.
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Multiagent systems based on large language models: a citizen-centric perspective

Workshop 2025

Published in Rethinking Multi-Agent Systems in the Era of LLMs (Workshop), University of Oxford, 2025

Recommended citation: Stein et al. (2025). "Multiagent systems based on large language models: a citizen-centric perspective." Rethinking Multi-Agent Systems in the Era of LLMs (Workshop).
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Evaluating Generalization Capabilities of LLM-Based Agents in Mixed-Motive Scenarios Using Concordia

Conference 2025

Published in NeurIPS 2025 Datasets and Benchmarks Track, 2025

Recommended citation: Smith et al. (2025). "Evaluating generalization capabilities of LLM-based agents in mixed-motive scenarios using Concordia." NeurIPS 2025 Datasets and Benchmarks Track.
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