Longxuan Yu (Ted)

About Me

Hi! I am Longxuan (Ted) Yu, a Ph.D. student (2025.09 – ) in the Department of Computer Science and Engineering at the University of California, Riverside (UCR). My advisor is Prof. Greg Ver Steeg.

Before joining UCR, I received my M.S. in Data Science from the University of California, San Diego (UCSD), where I worked with Prof. Haojian Jin on trustworthy AI and generative models. I obtained my B.Eng. in Computer Science from Hangzhou Dianzi University (HDU).

My current research interests lie in Diffusion Models, Representation Learning, and Information-theoretic Methods. I am particularly interested in the theoretical foundations of generative modeling, mutual information estimation, and how representation learning can support generalization across modalities and domains.

If you want to know more about my research or potential collaborations, feel free to email me at ylong030@ucr.edu.

Publications

arXiv 2026

Is Your Diffusion Sampler Actually Correct? A Sampler-Centric Evaluation of Discrete Diffusion Language Models

Luhan Tang, Longxuan Yu, Shaorong Zhang, Greg Ver Steeg

arXiv preprint, 2026.

arXiv Code
arXiv 2026

Generation Order and Parallel Decoding in Masked Diffusion Models: An Information-Theoretic Perspective

S Zhang, Longxuan Yu, R Brekelmans, L Tang, S Asif, GV Steeg

arXiv preprint arXiv:2602.00286, 2026.

arXiv
arXiv 2026

Thinking Out of Order: When Output Order Stops Reflecting Reasoning Order in Diffusion Language Models

Longxuan Yu, Y Fu, S Zhang, H Liu, GV Steeg, Y Dong

arXiv preprint arXiv:2601.22035, 2026.

arXiv
SIGCHI 2026

PrivacyAkinator: Articulating Key Privacy Design Decisions by Answering LLM-Generated Multiple-choice Questions

Qiyu Li, Yuen Sum Wong, Yuen Kei Wong, Longxuan Yu, Haojian Jin

ACM CHI, 2026.
NeurIPS 2025 (Workshop)

MMG: Mutual Information Estimation via the MMSE Gap in Diffusion

Longxuan Yu, Xing Shi, Xianghao Kong, Tong Jia, Greg Ver Steeg

Structured Probabilistic Inference & Generative Modeling@NeurIPS 2025 Workshop

arXiv
NAACL 2025

CausalEval: Towards Better Causal Reasoning in Language Models

Longxuan Yu, Delin Chen, Siheng Xiong, Qingyang Wu, Qingzhen Liu, Dawei Li, Zhikai Chen, Xiaoze Liu, Liangming Pan

NAACL, 2025.

arXiv Resources
ACM CCS 2024

Moderator: Moderating Text-to-Image Diffusion Models through Fine-grained Context-based Policies

Peiran Wang, Qiyu Li, Longxuan Yu, Ziyao Wang, Ang Li, Haojian Jin

ACM CCS, 2024. Distinguished Paper Award

arXiv Code

Academic Experience

2025.09 – present, Ph.D. Student, Department of Computer Science and Engineering, UCR
Advisor: Prof. Greg Ver Steeg Research Area: Diffusion models, Representation learning, Information-theoretic methods
2023.09 – 2025.06, Research Assistant, Halıcıoğlu Data Science Institute (HDSI), UCSD
Advisor: Prof. Haojian Jin
Project: Moderator: Moderating Text-to-Image Diffusion Models through Fine-grained Context-based Policies
Research Area: Diffusion-based generative modeling, Human-centered trustworthy AI

Education Experience

2025.09 – present, Ph.D., Computer Science, UCR, USA
2023.09 – 2025.06, M.S., Data Science, UCSD, USA
2019.09 – 2023.06, B.Eng., Computer Science, Hangzhou Dianzi University (HDU), China

Honors and Awards

2024, ACM CCS Distinguished Paper Award (Moderator: Moderating Text-to-Image Diffusion Models through Fine-grained Context-based Policies)
2023, Outstanding Graduate, Hangzhou Dianzi University
2022, National Scholarship