Shiyuan Sean Zhang
Yesterday Today Tomorrow
Hi! I am Shiyuan (Sean) Zhang, a Visiting Research Assistant at the University of Southern California, with guidance of Prof. Jieyu Zhao. I earned my bachelor’s and master’s degrees in Statistics and Computer Science from the University of Illinois Urbana-Champaign, worked closely with Prof. Jiaqi Ma. My research interest focus on data-centric machine learning (e.g., data attribution), trustworthy NLP, LLM for recommendations, and vision-language models.
Outside of academics, I enjoy exploring trading, where I find the unpredictability of the markets both humbling and exhilarating. I also share my quiet late nights with Lulu, a white rag who has been a silent witness to many of my coding sessions and research explorations.
News
| May 30, 2025 | 🆕 ArXived a new preprint: Taming Hyperparameter Sensitivity in Data Attribution: Practical Selection Without Costly Retraining. This paper proposes a practical framework for selecting hyperparameters in data attribution methods. |
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| May 23, 2025 | 🎓 I received my Master’s degree in Computer Science from the University of Illinois Urbana-Champaign (UIUC)! Grateful for all the guidance and support throughout this journey. |
| May 21, 2025 | 🆕 Released a new preprint: TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models. Co-first authored with Zeqing Wang. This paper proposes a benchmark to evaluate the temporal reasoning ability of Vision-Language Models (VLMs). |
| Oct 15, 2024 | 📄 One paper Nuanced Multi-class Detection of Machine-Generated Scientific Text has been accepted to PACLIC 2024 as ORAL presentation! Looking forward to presenting it in Tokyo this December. 🔗 Read the paper on ACL Anthology |
| Sep 26, 2024 | 🌟 Our paper dattri: A Library for Efficient Data Attribution has been accepted as a Spotlight Paper at NeurIPS 2024! dattri provides a modular and scalable framework for training data attribution, supporting methods like Influence Functions, TracIn, and TRAK across different access settings. Grateful to work with such an amazing team! |