Scholar
Fengqing Jiang
Google Scholar ID: kTXY8P0AAAAJ
University of Washington
Citations & Impact
Citations
949
H-index
11
i10-index
12
Publications
20
Co-authors
11
list available
Publications
Resume (English only)
Academic Achievements
- - 2025 Invited research talk at UGA CSSI 8000: Red-Teaming for LLM Agents.
- - 2025 Best Paper Award at Agents4Science for BadScientist paper.
- - 2025 Best Honorable Mention at ICLR 2025 BiAlign Workshop for SafeChain paper.
- - Conference Reviewer: ICML 2024/2025, NeurIPS 2024, ICLR 2025/2026, AAAI 2025/2026, AISTATS 2025/2026, ACL 2025/2026
- - Journal Reviewer: IEEE TIFS, TNNLS
- - Volunteer: NAACL 2025
- - Organizer: NeurIPS 2024 Competition, CLAS: The Competition for LLM and Agent Safety; NeurIPS 2023 Competition, Trojan Detection Challenge 2023 (LLM Edition)
- - Student Advisory Board Member of ACTION NSF AI Institute, 2023 - Current
Research Experience
- - Snowflake AI, Research Intern, Sept. 2025 - Current
- - Microsoft Research, Research Intern, June 2025 - Sept. 2025
- - Amazon, Applied Scientist Intern, June 2024 - Sept. 2024
- - Intel AI, Machine Learning Engineer Intern, Mar. 2022 - June 2022
Education
- - University of Washington, Department of Electrical and Computer Engineering, Ph.D. Student, 2022 - present
- - University of Illinois Urbana-Champaign, B.S. in Electrical Engineering (with highest honor), Minor in Computer Science, 2018 - 2022
- - Zhejiang University, B.Eng. in Electrical Engineering, 2018 - 2022
Background
- Currently a 4th-year Ph.D. student at the University of Washington under the supervision of Prof. Radha Poovendran. Also works with Prof. Luyao Niu and Prof. Bill Yuchen Lin at UW, as well as with Prof. Bo Li at UIUC and Prof. Zhen Xiang at the University of Georgia. Research interests lie in building better AGI systems, with recent focus on LLM/Agent/LMM, covering post-training & alignment, safety & security, reinforcement learning, agentic training, reasoning, and data synthesis. Additionally, has worked on Machine Learning Security and Federated Learning.
Miscellany
- Personal interests not mentioned.
