About me

I am currently a final-year CSE Ph.D. candidate at University of California, Santa Cruz, recipient of Jack Baskin and Peggy Downes-Baskin Fellowship 2023-2024, fortunately advised by Prof. Yang Liu!

I am now on the job market seeking for Research Scientist / Faculty positions in Trustworthy/Responsible Machine Learning or Language Models. Please feel free to reach out if you have any open positions!

My research interests lie in trustworthy machine learning & large language models, i.e., robust learning under real-world constraints (label errors in human-generated data, class-imbalanced learning, group distributional robustness), data alignment in large language models, incentive design for data collection, and generative modeling.

Previously, I received my Master of Science degree (Data Science) at Brown University and B. S. degree in Honors Math & Honors Youth (Gifted Young) from Xi’an Jiaotong University.


Please feel free to contact me if you want to collaborate.

Email: jiahengwei(at)ucsc(dot)edu; Wechat: WJH_Derrick


[2024. 02] We have released a preprint on Measuring and Reducing LLM Hallucination without Gold-Standard Answers via Expertise-Weighting. In this paper, we propose Factualness Evaluations via Weighting LLMs (FEWL), the first hallucination metric that is specifically designed for the scenario when gold-standard answers are absent.

[2024. 01] We have released a preprint on Human-Instruction-Free LLM Self-Alignment with Limited Samples. In this paper, we propose an algorithm that can self-align LLMs iteratively without active human involvement.

[2023. 08] We delivered a hands-on tutorial on learning with noisy labels at IJCAI 2023.

[2023. 05] docta.ai is online. This is a library to help you understand and curate your data.

[2023. 05] One first-author paper accepted to KDD 2023.

[2023. 05] I am hornored to be selected as the only one 2023-24 Jack Baskin and Peggy Downes-Baskin Fellowship recipient.

[2023. 05] Invited talk from AI-Time.

[2023. 04] Invited talk from TMLR Young Scientist Seminar.

[2023. 03] Oral presentation at WSDM 2023 (Crowd Science Workshop).

[2023. 02] I will join ByteDance AI Lab for the Machine Learning (Research) Intern this summer.

[2023. 01] One first-author paper accepted to ICLR 2023 (work done at Google Brain).

[2022. 12] Joined CROSS as a Research Fellow.

[2022. 10] Invited talk from Domain Adaptation Team at University of Toronto.

[2022. 08] Invited talk from AI-Time.

[2022. 07] Oral presentation at ICML 2022 (Deep Learning: Robustness).

[2022. 07] One first-author paper accepted to ECCV 2022.

[2022. 06] Invited talk from AI-Time.

[2022. 05] One first-author paper accepted to ICML 2022 (Long Presentation, 2.1%).