Jie Fu
Also known as 付杰
Jie Fu is a research scientist at IQuest Research whose AI work spans neural-network design, language models and transformer reasoning. He earned computer-science degrees at the University of Otago and the National University of Singapore, then held postdoctoral roles at Mila under Chris Pal and Yoshua Bengio. His 2021 quaternion paper won an ICLR Outstanding Paper Award; his 2024 language-model paper received NAACL’s Outstanding Paper Award, and another 2024 paper received a NeurIPS Spotlight. A 2025 ACL Oral paper investigates state-tracking circuits inside transformers.
Timeline
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July 2011–July 2017
Degrees at Otago and NUS
He earns an MSc in computer science at the University of Otago in 2012, then completes a PhD in computer science at the National University of Singapore under Tat-Seng Chua in 2017.
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September 2017–February 2022
Postdoctoral work at Mila
He holds an IVADO postdoctoral role with the Quebec AI Institute (Mila) and Polytechnique Montreal from September 2017 to December 2020 under Chris Pal. In January 2021, he starts a postdoctoral role at Mila and the University of Montreal under Yoshua Bengio.
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2021
A quaternion paper wins at ICLR
In 2021, he coauthors “Beyond Fully-Connected Layers with Quaternions,” which parameterizes hypercomplex multiplications so models learn multiplication rules from data. The paper receives the ICLR Outstanding Paper Award.
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2022
Running ahead of evolution
In 2022, “Running ahead of evolution,” an AI-based simulation for predicting future high-risk SARS-CoV-2 variants, is named an ACM Gordon Bell COVID finalist.
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February 2022–August 2024
Research roles at BAAI and HKUST
He works as a researcher at BAAI on deep learning and large language models from February 2022 to August 2023. From August 2023 to August 2024, he is a visiting scholar at Hong Kong University of Science and Technology, also working on deep learning and large language models.
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2024
Emergent modularity and stacked transformers
In 2024, he coauthors “Unlocking Emergent Modularity in Large Language Models,” which wins NAACL’s Outstanding Paper Award; its method lets standard language models be fine-tuned as mixture-of-experts counterparts without extra parameters. He also coauthors “Stacking Your Transformers,” which reports faster training, lower loss and stronger results on eight NLP benchmarks.
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August 2024–January 2026
Shanghai AI Lab and state tracking
He joins Shanghai AI Lab as a research scientist in August 2024, working on safe AI and automated reasoning, and serves as an adjunct assistant professor at Shanghai Innovation Institute from March 2025 to January 2026. His 2025 ACL Oral paper, “Finite State Automata Inside Transformers with Chain-of-Thought,” identifies late-layer MLP neurons as key to tracking world state.
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Since January 2026
Research scientist at IQuest
He joins IQuest Research as a research scientist in January 2026 and focuses on safe AI.
Career
- Since 2026IQuest ResearchResearch Scientist
- 2025–2026Shanghai Innovation InstituteAdjunct Assistant Professor
- 2024–2026Shanghai Artificial Intelligence LaboratoryResearch Scientist
- 2023–2024Hong Kong University of Science and TechnologyVisiting Scholar
- 2022–2023Beijing Academy of Artificial IntelligenceResearcher
- 2021–2022Université de MontréalPostdoctoral Researcher
- 2017–2022MilaPostdoctoral Researcher
- 2017–2020Polytechnique MontréalPostdoctoral Researcher
Education
- 2017National University of SingaporePhD
- 2012University of OtagoMSc
Sources and credits
Photo: We have no published photo of Jie Fu yet. Send us a free-licensed photo.
- Big AI Dream | Jie Fu
- Big AI Dream | Jie Fu
- Beyond Fully-Connected Layers with Quaternions: Parameterization of Hypercomplex Multiplications with $1/n$ Parameters
- The paper co-authored by Shuai Zhang wins the ICLR 2021 outstanding paper award
- Unlocking Emergent Modularity in Large Language Models - ACL Anthology
- Stacking Your Transformers: A Closer Look at Model Growth for Efficient LLM Pre-Training
- Finite State Automata Inside Transformers with Chain-of-Thought: A Mechanistic Study on State Tracking - ACL Anthology


