🧑‍🔬 Ce Zhang

🏛️ Affiliations

🔬UK Centre for Ecology & HydrologyWallingford · United Kingdom2023–2026Employment 2023–2026 · Fellow in UK Centre for Ecology and Hydrology, Biodiversity and Land Usecurrent🎓University of BristolBristol · United Kingdom2023–2026Employment since 2023 · Member, Cabot InstituteEmployment since 2026 · Academic , Senior Lecturer, School of Geographical Sciencescurrent🎓ETH ZurichZurich · Switzerland2016–2026 · 189 papers🎓University of Wisconsin–MadisonMadison · United States2011–2016 · 24 papers🎓University of ChicagoChicago · United States2023–2026 · 21 papers🎓Stanford UniversityStanford · United States2014–2016 · 16 papers🎓Peking UniversityBeijing · China2008–2010 · 6 papers🔬Institute for Computing PlatformsZurich · Switzerland2022–2026 · 4 papers🤝TogetherLacey · United States2024–2025 · 3 papers🎓University of ZurichZurich · Switzerland2019 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 13 more institutions on single bylines are not listed.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time (2023 · 185 citations)
   Serving very large language models is slow, especially when generating tokens one at a time in latency-sensitive settings.
2. Mixture-of-Agents Enhances Large Language Model Capabilities (2024 · 116 citations)
   Different LLMs have complementary strengths, but scaling any single model further is very expensive. This paper asks whether several existing models can be combined to beat each of them individually. The authors first describe a property they call collaborativeness: a model usually writes a better answer when it can…
3. WebAggregator: Enhancing Compositional Reasoning Capabilities of Deep Research Agent Foundation Models (2026 · 1 citation)
   Deep Research agents need more than retrieval. They must combine scattered, heterogeneous evidence through multi-step reasoning. Existing training and evaluation resources mostly reward locating a single entity, so agents learn to call tools often instead of reasoning carefully.

All works

21 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

1. Holistic Evaluation of Language Models ↗arXiv:2211.09110 · Published in Transactions on Machine Learning Research (TMLR), 2023 · no summary yetPercy Liang et al.2022 · 1,001 citations
2. Towards Efficient Data Valuation Based on the Shapley Value ↗arXiv (Cornell University) · no summary yetRuoxi Jia et al.2019 · 191 citations
3. Deja Vu: Contextual Sparsity for Efficient LLMs at Inference TimeInternational Conference on Machine Learning (ICML)Zichang Liu, Jue Wang, Tri Dao, et al.2023 · 185 citations
4. A Principled Approach to Data Valuation for Federated Learning ↗Lecture notes in computer science · no summary yetTianhao Wang et al.2020 · 178 citations
5. Efficient task-specific data valuation for nearest neighbor algorithms ↗Proceedings of the VLDB Endowment · no summary yetRuoxi Jia et al.2019 · 176 citations
6. Mixture-of-Agents Enhances Large Language Model CapabilitiesarXiv:2406.04692Junlin Wang, Jue Wang, Ben Athiwaratkun, et al.2024 · 116 citations
7. CogniVal: A Framework for Cognitive Word Embedding Evaluation ↗Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL) · no summary yetNora Hollenstein et al.2019 · 86 citations
8. DL2: Training and Querying Neural Networks with Logic. ↗International Conference on Machine Learning · no summary yetMarc Fischer et al.2019 · 76 citations
9. Decentralized Training of Foundation Models in Heterogeneous Environments ↗arXiv:2206.01288 · no summary yetBinhang Yuan et al.2022 · 59 citations
10. Building a Large-scale Multimodal Knowledge Base System for Answering Visual Queries ↗arXiv (Cornell University) · no summary yetYuke Zhu et al.2015 · 45 citations
11. Scalability vs. Utility: Do We Have to Sacrifice One for the Other in Data Importance Quantification? ↗IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · no summary yetRuoxi Jia et al.2019 · 33 citations
12. Mechanistic Design and Scaling of Hybrid Architectures ↗arXiv (Cornell University) · no summary yetMichael Poli et al.2024 · 22 citations
13. Deep Learning for Recommender Systems ↗Springer eBooks · no summary yetShuai Zhang et al.2021 · 16 citations
14. CocktailSGD: Fine-tuning Foundation Models over 500Mbps Networks ↗Rare & Special e-Zone (The Hong Kong University of Science and Technology) · no summary yetBeidi Chen et al.2023 · 14 citations
15. Laughing Hyena Distillery: Extracting Compact Recurrences From Convolutions ↗neural information processing systems · no summary yetStefano Massaroli et al.2023 · 13 citations
16. Knowledge Router: Learning Disentangled Representations for Knowledge Graphs ↗Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · no summary yetShuai Zhang et al.2021 · 8 citations
17. Beat the long tail: Distribution-Aware Speculative Decoding for RL Training ↗arXiv (Cornell University) · no summary yetShao, Zelei et al.2025 · 6 citations
18. Switch Spaces: Learning Product Spaces with Sparse Gating ↗arXiv (Cornell University) · no summary yetShuai Zhang et al.2021 · 3 citations
19. Effective and Efficient Federated Tree Learning on Hybrid Data ↗arXiv (Cornell University) · no summary yetQinbin Li et al.2023 · 2 citations
20. WebAggregator: Enhancing Compositional Reasoning Capabilities of Deep Research Agent Foundation ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Rui Wang, Ce Zhang, Jun-Yu Ma, et al.2026 · 1 citation
21. When RL Meets Adaptive Speculative Training: A Unified Training-Serving System ↗arXiv (Cornell University) · no summary yetJunxiong Wang et al.2026

🤝 Co-authors here

12 of 21 co-authors with a page, the most papers together first.

• Jue Wang ×2
• Ben Athiwaratkun
• Binhang Yuan
• Boyang Xue
• Christopher Ré
• Dong Yu
• Haitao Mi
• Hongming Zhang
• Hongru Wang
• James Zou
• Jianshu Zhang
• Jun-Yu Ma

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• International Conference on Machine Learning (ICML) ×2: ICML 2023
• Annual Meeting of the Association for Computational Linguistics (ACL): ACL 2026
• Conference on Neural Information Processing Systems (NeurIPS)
• IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
• Lecture notes in computer science
• Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
• Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
• Proceedings of the VLDB Endowment
• Rare & Special e-Zone (The Hong Kong University of Science and Technology)
• Springer eBooks
• neural information processing systems

🔗 Cited by

Papers of our library cite their works 1,882 times (of the 149,439 papers whose bibliographies we read); 397 citing papers with a page here →.

Updated 2 November 2026

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