Yuxiao Cheng
I am Yuxiao Cheng (程宇笑), a Ph.D. student in the Department of Automation, Tsinghua University, advised by Prof. Jinli Suo.
My research focuses on AI for Healthcare, specifically on causal modelling, AI Agents, and multi-modal learning in digital healthcare.
I received my B.E. in Department of Automation from Tsinghua University in 2022 and am currently pursuing my Ph.D. in the Department of Automation, advised by Prof. Jinli Suo.
I won the National Scholarship (国家奖学金) in 2024.
I have published papers in top-tier venues including The Lancet Digital Health, Nature Biomedical Engineering, Nature Communications, PNAS, ICLR, and AAAI, with 8+ first/co-first author papers and 20+ co-authored papers.
🔥 News
- 2025.11: 🎉 One paper accepted to AAAI 2026, including “COGS: A Causal Representation Learning Framework for Out-of-Distribution Generalization in Time Series”.
- 2025.09: 🎉 My project “OpenLens AI: Fully Autonomous Research Agent for Health Infomatics” is launched and prompted by medias including 量子位.
- 2025.09: 🎉 Our paper “Causal deep learning for real-time detection of cardiac surgery-associated acute kidney injury” is published by The Lancet Digital Health.
- 2025.07: 🎉 Our paper “A generative model uses healthy and diseased image pairs for pixel-level chest X-ray pathology localization” is published in Nature Biomedical Engineering.
🧠 Research Interests
- Causal Discovery and Explainable AI in Healthcare
- AI Agents and LLMs in Healthcare
📝 Selected Publications
Qin Zhong*, Yuxiao Cheng*, Zongren Li*, Dongjin Wang*, Chongyou Rao, Yi Jiang, et al.
(I am the lead contributor for AI algorithm.)
A causal deep learning approach that combines neural networks with causal discovery to develop a reliable and generalizable model to predict a patient’s risk of developing CSA-AKI.
CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery
Yuxiao Cheng*, Ziqian Wang*, Tingxiong Xiao, Qin Zhong, Jinli Suo, Kunlun He
Official Website ｜ Generation Code🧑💻 ｜ Dataset Download
A novel pipeline capable of generating realistic time-series along with a ground truth causal graph that is generalizable to different fields.
CUTS+: High-dimensional Causal Discovery from Irregular Time-series
Yuxiao Cheng*, Lianglong Li*, Tingxiong Xiao, Zongren Li, Jinli Suo, Kunlun He, Qionghai Dai
Code🧑💻
Increasing scalability of neural causal discovery on high-dimensional irregular data.
CUTS: Neural Causal Discovery from Irregular Time-Series Data
Yuxiao Cheng*, Runzhao Yang*, Tingxiong Xiao, Zongren Li, Jinli Suo, Kunlun He, Qionghai Dai
Code🧑💻
EM-Style joint causal graph learning and missing data imputation for irregular temporal data
Kaiming Dong*, Yuxiao Cheng*, Kunlun He, Jinli Suo
A generative model that leverages paired healthy–diseased X-rays for interpretable pathology localization.
Nature Communications
Supervised multi-frame dual-channel denoising enables long-term single-molecule FRET under extremely low photon budget
PNAS Sharing Massive Biomedical Data at Magnitudes Lower Bandwidth Using Implicit Neural Function
(Co-first authors marked with *)
🎖 Honors and Awards
- 2024: 🏆 National Scholarship (国家奖学金), Tsinghua University
- 2023: Outstanding Student Award, Department of Automation
- 2022: Excellent Undergraduate Thesis Award
🎓 Education
- 2022.09 – Present: Ph.D. in Automation, Tsinghua University (Advisor: Prof. Jinli Suo)
- 2018.09 – 2022.07: B.Eng. in Automation, Tsinghua University
💻 Internships and Research Visits
- 2026.01 – 2026.06 (planned): Visiting Researcher at KTH Royal Institute of Technology, Stockholm, Sweden.
- 2024.07 – 2024.08: Research Intern, Suzhou Automotive Research Institute, real-time detection algorithms with LiDAR.
- 2021.07 – 2021.08: Product Manager Intern, Xiaomi Inc., photo deblurring based on deep learning.
💬 Academic Services
- Reviewer for ICLR 2025, Pattern Recognition, IEEE Internet of Things Journal.