My name is Yixin (Elaine) Wan and I am a PhD student in Computer Science at UCLA. I am fortunate to be advised by Professor Kai-Wei Chang and have been a proud member of the UCLANLP research group. My research focuses on building trustworthy multimodal generative models: specifically, I have worked on improving the controllability, fairness, and factuality in text and image generation. I completed my B.S. in Applied Mathematics (double major in Economics) also at UCLA. Go Bruins!

Currently, I am a research intern at Tencent AI Lab in Bellevue, WA, working on controllable image editing. My mentor is Lei Ke and Wenhao Yu. Previously, I have interned at Amazon AGI and Microsoft Research Asia (MSRA).

News

• [2025/12] 📢 ⭐️ New Work on Motion Image Editing with Tencent AI Lab released! Check it out: MotionEdit: Benchmarking and Learning Motion-Centric Image Editing . Also check out more demos on our Project Page!
• [2025/09] I am humbled to be awarded the 2025 Amazon AI Fellowship 🎉!
• [2025/08] 3 papers on LLM Unlearning and Fairness accepted to EMNLP 2025 Findings 🎉! Check them out:
  • Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning
  • LUME: LLM Unlearning with Multitask Evaluations
  • Fact-or-Fair: A Checklist for Behavioral Testing of AI Models on Fairness-Related Queries
• [2025/06] Started my internship at Tencent AI Lab (Bellevue)!
• [2025/05] 2 long papers on Fairness accepted to ACL 2025 Main 🎉! Check them out:
  • The Male CEO and the Female Assistant: Evaluation and Mitigation of Gender Biases in Text-To-Image Generation of Dual Subjects
  • White Men Lead, Black Women Help? Benchmarking and Mitigating Language Agency Social Biases in LLMs
• [2025/05] Arrived at Albuquerque! I am serving as the Program Chair at NAACL 2025’s Trustworthy NLP (TrustNLP) Workshop.
  • I am also presenting my recent work on LLM unlearning with Amazon: Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning