About

I am a PhD student in Biostatistics at the University of Minnesota, Twin Cities, advised by Sandra Safo and Jue Hou in the Division of Biostatistics and Health Data Science. I am also supervised by Ju Sun (Computer Science & Engineering). Before Minnesota, I received my B.S. in Statistics from Renmin University of China.

My research develops statistically principled methods for transferable and trustworthy machine learning and deep learning, with applications spanning biomedical, imaging, and engineering domains.

Research Interests

• Transfer Learning — estimators that keep their statistical guarantees when distributions shift or evidence must be pooled across sites, spanning distributional transfer, federated causal estimation, and reinforcement learning.
• Uncertainty Quantification — deciding when a model should abstain rather than guess, via selective prediction with explicit risk–coverage trade-offs.
• Biomedical & Health Data Science — computational and AI methods for electronic health records, biomedical knowledge graphs, medical imaging, and multi-platform biomarker data.

Publications

^* denotes equal contribution. See also my Google Scholar profile.

Peer-Reviewed Publications

1. Antidiabetic Drug Associations With Heart Failure Outcomes: Real-World Evidence Study Using Electronic Health Records

   Elzbieta Jodlowska-Siewert, Yunhui Chen, Sinian Zhang, Jia Li, Robert Dellavalle, Rui Zhang, Jue Hou

   JMIR Diabetes 11 (2026): e85083

   Paper

2. Wasserstein Transfer Learning

   Kaicheng Zhang^*, Sinian Zhang^*, Doudou Zhou, Yidong Zhou

   Advances in Neural Information Processing Systems (NeurIPS), 2025

   Paper Code

3. 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

   Findings of the Association for Computational Linguistics: EMNLP 2025, 16432–16445

   Paper

4. 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.

   Journal of Medical Internet Research 27 (2025): e71873

   Paper

5. 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

   Journal of Biomedical Informatics 158 (2024): 104730

   Paper

6. 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, et al.

   Journal of Medical Internet Research 25 (2023): e45662

   Paper

7. 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

   Adjunct Proceedings of UbiComp/ISWC 2023, 557–562

   Paper

Preprints & Under Review

1. The Wreaths of KHAN: Uniform Graph Feature Selection with False Discovery Rate Control

   Jiajun Liang, Yue Liu, Doudou Zhou, Sinian Zhang, Junwei Lu

   Under review

   Paper Code

2. Aligning Language Models with Selective Prediction

   Gaoxiang Luo, Yifan Wu, Sinian Zhang, Aryan Deshwal, Ju Sun

   Under review

   Paper

3. Ensemble Selective Classification

   Sinian Zhang^*, Chongwei Chen^*, Guanchen Li, Ju Sun

   Under review

   Code

4. Predicting the Timing of First Sustained Cognitive Worsening in Alzheimer's Disease Using Real-World Clinical Data and Machine Learning

   Shruthi Venkatesh^*, Sinian Zhang^*, Wen Zhu, Michele Morris, Rocco Mercurio, Sarah B. Berman, Hansruedi Mathys, Abby L. Olsen, C. Elizabeth Shaaban, Shyam Visweswaran, Oscar L. Lopez, Tianxi Cai, Jue Hou, Zongqi Xia

   Under review

   Paper

5. Generalized Linear Markov Decision Process

   Sinian Zhang^*, Kaicheng Zhang^*, Ziping Xu, Tianxi Cai, Doudou Zhou

   Under review

   Paper

Software

• IntegMultiReg R package, MCMC sampler in C · 2026

  Integrative Bayesian multi-regression of multi-platform biomarkers, supporting continuous, binary and survival outcomes with missing platforms.

  CRAN

• PyNILE & RNILE Python and R wrappers · 2025

  Wrappers around the NILE Java library for clinical narrative extraction; development guided as a mentor.

  Python R

Talks & Presentations

Talks

• 7th ICSA-Canada Chapter Symposium · McGill University, Montreal, Canada · Aug 2026
  Federated Adaptive Causal Estimation with High-Dimensional Covariates (FACE-HD)

Conference Abstracts

• Predicting the Timing of Cognitive Decline in Alzheimer's Disease Using Real-world Clinical Data and Machine Learning (P10-12.009)
  Shruthi Venkatesh, Sinian Zhang, Oscar Lopez, Tianxi Cai, Jue Hou, Zongqi Xia · Neurology 106 (11_Supplement_1), 2026 Abstract

Service

Teaching & Mentoring

• May – Sep 2025 Research Mentor — Shuheng Kong & Conglin Ruan, master's students
  Guided development of Python and R versions of the NILE package for natural language processing.
• Jan – Jun 2024 Teaching Assistant, Data Science Practice
  Renmin University of China
• Aug – Dec 2023 Teaching Assistant, Probability Theory
  Renmin University of China
• Jun – Sep 2023 Research Mentor — Brittany Wang, high school student
  Supervised research using real-world data to support an acute ischemic stroke (AIS) study.

Academic Service

• Conference Reviewer ICLR 2027 · AAAI 2027 · NeurIPS 2026 · ICLR 2026
• Journal Reviewer Journal of Intelligent Medicine and Healthcare

Honors & Awards

• 2026 1st Place, $3,000 — 7th Annual UMN Interdisciplinary Health Data Competition
  University of Minnesota, Twin Cities
• 2024 Dean's PhD Scholars Award, $5,000
  University of Minnesota, Twin Cities
• 2023 Scholarship for Academic Excellence
  Renmin University of China