Hello, I'm a Researcher

Specializing in Statistics, Health Informatics, and Trustworthy AI

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About Me

I am a researcher passionate about statistical analysis and data science. I focus on developing innovative statistical methods and algorithms to solve real-world problems.

My research interests include:

  • Causal Inference
  • Trustworthy AI
  • Reinforcement Learning
  • Health Informatics

R

Python

C++

SQL

My Publications

For a complete list of publications, visit my Google Scholar profile

Generalized Linear Markov Decision Process

Sinian Zhang*, Kaicheng Zhang*, Ziping Xu, Tianxi Cai, Doudou Zhou (2025) (*Equal Contribution)

arXiv preprint arXiv:2506.00818

This paper presents a novel framework for Generalized Linear Markov Decision Processes, extending classical reinforcement learning methods with advanced statistical modeling techniques for complex decision-making scenarios.

Reinforcement Learning Markov Decision Process Statistical Learning
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DrKGC: Dynamic Subgraph Retrieval-Augmented LLMs for Knowledge Graph Completion across General and Biomedical Domains

Yongkang Xiao, Sinian Zhang, Yi Dai, Huixue Zhou, Jue Hou, Jie Ding, Rui Zhang (2025)

arXiv preprint arXiv:2506.00708

DrKGC introduces a dynamic subgraph retrieval approach that augments large language models for knowledge graph completion, achieving state-of-the-art performance across both general and biomedical domains.

Large Language Models Knowledge Graph Completion Retrieval-Augmented Generation
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Wasserstein Transfer Learning

Kaicheng Zhang*, Sinian Zhang*, Doudou Zhou, Yidong Zhou (2025) (*Equal Contribution)

arXiv preprint arXiv:2505.17404

This work introduces a novel transfer learning framework based on Wasserstein distance, enabling efficient knowledge transfer across different domains with theoretical guarantees and practical applications.

Transfer Learning Optimal Transport Machine Learning Theory
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Advancing the Use of Longitudinal Electronic Health Records: Tutorial for Uncovering Real-World Evidence in Chronic Disease Outcomes

Feiqing Huang, Jue Hou, Ningxuan Zhou, Kimberly Greco, Chenyu Lin, Sara Morini Sweet, Jun Wen, Lechen Shen, Nicolas Gonzalez, Sinian Zhang, et al. (2025)

Journal of Medical Internet Research

This tutorial provides a comprehensive framework for leveraging longitudinal EHR data to uncover real-world evidence in chronic disease outcomes, bridging the gap between clinical research and practice with advanced analytics.

Electronic Health Records Real-World Evidence Chronic Disease
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FuseLinker: Leveraging LLM's Pre-trained Text Embeddings and Domain Knowledge to Enhance GNN-based Link Prediction on Biomedical Knowledge Graphs

Yongkang Xiao, Sinian Zhang, Huixue Zhou, Mingchen Li, Han Yang, Rui Zhang (2024)

Journal of Biomedical Informatics

FuseLinker integrates large language model embeddings with graph neural networks to improve link prediction on biomedical knowledge graphs, demonstrating superior performance in drug discovery and disease gene identification tasks.

Knowledge Graphs Large Language Models Biomedical Informatics
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The Wreaths of Khan: Uniform Graph Feature Selection with False Discovery Rate Control

Jiajun Liang, Yue Liu, Doudou Zhou, Sinian Zhang, Junwei Lu (2024)

arXiv preprint arXiv:2403.12284

This paper proposes a novel method for uniform graph feature selection with rigorous false discovery rate control, addressing the multiple testing problem in network analysis with theoretical guarantees.

Graph Analysis Feature Selection Statistical Testing
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Generate Analysis-ready Data for Real-world Evidence: Tutorial for Harnessing Electronic Health Records with Advanced Informatic Technologies

Jue Hou, Rachel Zhao, Jessica Gronsbell, Yucong Lin, Clara-Lea Bonzel, Qingyi Zeng, Sinian Zhang, Brett K. Beaulieu-Jones, Griffin M. Weber, Thomas Jemielita, et al. (2023)

Journal of Medical Internet Research

A comprehensive tutorial demonstrating how to generate analysis-ready datasets from EHR data using state-of-the-art informatics technologies for real-world evidence generation.

Health Informatics Data Science Real-World Evidence
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A Post-processing Machine Learning for Activity Recognition Challenge with OpenStreetMap Data

Shiyao Huang, Junliang Lyu, Sinian Zhang, Ruiying Tang, Huan Xiao, Yuanyuan Zhang, Xiaoling Lu (2023)

Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2023 ACM International Symposium on Wearable Computers

This work presents a novel post-processing machine learning approach for activity recognition using OpenStreetMap data, improving location-based activity prediction in ubiquitous computing systems.

Activity Recognition Ubiquitous Computing Spatial Data Analysis
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Teaching Experience

Teaching Assistant, Data Science Practice

Renmin University of China

Beijing, China

January 2024 - June 2024

Teaching Assistant, Probability Theory

Renmin University of China

Beijing, China

August 2023 - December 2023

Academic Service

Conference Reviewer

ICLR 2026

Journal Reviewer

Available upon request

Honors & Awards

2024

Dean's PhD Scholars Award

University of Minnesota, Twin Cities

2023

Scholarship for Academic Excellence

Renmin University of China

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