I am Yingjia Wan, a second-year PhD student at UCLA, advised by Prof. Elisa Kreiss in the Computation and Language for Society (Coalas) Lab and the UCLA NLP group.
- Research Interests: My research spans several interconnected themes, including reasoning and evaluation in large language models (LLMs), AI for mathematics (particularly autoformalization in Lean 4), multimodal reasoning (VLMs), factuality, and reliable AI systems more broadly.
- Collaboration: I feel incredibly grateful to have learned from some outstanding AI researchers and mentors such as: Prof. Elisa Kreiss, Prof. Zhijiang Guo, Dr. Ivan Vulić, Dr. Jianqiao Lu, Dr. Yinya Huang, and Dr. Zhengying Liu.
I am always open for a chat or collaboration. Feel free to drop me an email!
University of California, Los Angeles (PhD), 2025-current
University of Cambridge (Master's), 2022-2023
University of Macau (Bachelor's), 2017-2021

We introduce GAPA (Gender Associations of Physical Attributes), a dataset of 316 physical attributes paired with 14,706 human gender-association ratings, and show that seemingly “objective” physical descriptions still carry systematic gender associations that 16 evaluated LLMs only partially and unevenly recover.

FormalRx is a diagnostic evaluation framework for autoformalization built on the SCI Error Taxonomy (28 error categories). It supports alignment verdicts, error categorization, localization, and correction, and introduces FormalRx-8B and the FormalRx-Test benchmark.

FaStfact is a multi-agent pipeline for evaluating long-form generation factuality that achieves the highest alignment with human evaluation and time/token efficiency among existing baselines. An annotated FaStfact-bench is also open-sourced.

FormalAlign aims to address a long-lasting challenge in autoformalization - the absence of an effective alignment evaluation metric. It accurately assesses the semantic and logical alignment between the natural language and formal theorems.