I am a Ph.D. candidate in Computer Science and Engineering at The Ohio State University (OSU). I’m fortunately working with Prof. Ping Zhang in AIMed (Artificial Intelligence in Medicine) Lab. My research lies at the intersection of deep learning and causal inference, with applications in healthcare and biomedicine. I am particularly interested in estimating treatment effects to derive evidence-based, actionable healthcare decisions using real-world patient data.
*: Equal Contributions; †: My Mentee
Teach multimodal LLMs to comprehend electrocardiographic images
Ruoqi Liu*, Yuelin Bai*, Xiang Yue, Ping Zhang
arXiv 2024
[Project Page][arXiv][Code][HF Data]
Treatment effect estimation with multiple treatments and multiple outcomes for antihypertensive drug combinations
Ruoqi Liu, Lang Li, Ping Zhang
medRxiv 2024
[medRxiv]
A deep subgrouping framework for precision drug repurposing via emulating clinical trials on real-world patient data
Seungyeon Lee†, Ruoqi Liu, Feixiong Cheng, Ping Zhang
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2025 (Acceptance rate: 62/285 = 22%, applied data science track)
CURE: A deep learning framework pre-trained on large-scale patient data for treatment effect estimation
Ruoqi Liu, Pin-Yu Chen, Ping Zhang
Patterns, 2024
[Paper] [Code]
Selected media coverage: OHIO STATE NEWS, Clinical Research News, MedicalXpress, AZoRobotics
KG-TREAT: Pre-training for treatment effect estimation by synergizing patient data with knowledge graphs
Ruoqi Liu, Lingfei Wu, Ping Zhang
AAAI Conference on Artificial Intelligence (AAAI), 2024 (Acceptance rate: 2342/9862 = 23.7%, main track, oral presentation)
[Paper][Code]
MMMU: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi
Xiang Yue, Yuansheng Ni, Kai Zhang, Tianyu Zheng, Ruoqi Liu, Ge Zhang, Samuel Stevens, Dongfu Jiang, Weiming Ren, Yuxuan Sun, Cong Wei, Botao Yu, Ruibin Yuan, Renliang Sun, Ming Yin, Boyuan Zheng, Zhenzhu Yang, Yibo Liu, Wenhao Huang, Huan Sun, Yu Su, Wenhu Chen
IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), 2024 (Award Candidate Paper, Oral: 24/11,532=0.2%)
[Paper]
Heterogeneous treatment effect estimation with subpopulation identification for personalized medicine in opioid use disorder
Seungyeon Lee†, Ruoqi Liu, Wenyu Song, Ping Zhang
IEEE International Conference on Data Mining (ICDM), 2023 (Acceptance rate: 200/1003 = 19.9%, short paper, oral presentation)
[Paper]
Estimating treatment effects for time-to-treatment antibiotic stewardship in sepsis
Ruoqi Liu, Katherine Hunold, Jeffrey Caterino, Ping Zhang
Nature Machine Intelligence, 2023 (Impact factor: 25.898)
[Paper] [Code]
Selected media coverage: OHIO STATE NEWS, HealthITAnalytics, MIT Technology Review Chinese Edition. Featured on AMIA 2024 Informatics Summit Artificial Intelligence and Data Science Year-in-Review
Data imputation for clinical trial emulation: A case study on impact of intracranial pressure monitoring for traumatic brain injury
Zhizhen Zhao†, Ruoqi Liu, Jonathan Groner, Henry Xiang, Ping Zhang
American Medical Informatics Association Informatics Summit (AMIA Summit), 2023
[Paper]
Deconfounding actor-critic network with policy adaptation for dynamic treatment regimes
Changchang Yin, Ruoqi Liu, Jeffrey Caterino, Ping Zhang
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022 (Acceptance rate: 254/1695 = 15.0%, research track)
[Paper]
A computational framework for identifying age risks in drug-adverse event pairs
Zhizhen Zhao†, Ruoqi Liu, Lei Wang, Lang Li, Chi Song, and Ping Zhang
American Medical Informatics Association Informatics Summit (AMIA Summit), 2022
[Paper]
A deep learning framework for drug repurposing via emulating clinical trials on real world patient data
Ruoqi Liu, Lai Wei, Ping Zhang
Nature Machine Intelligence 3:68–75, 2021 (Impact factor: 25.898)
[Paper] [Code]
Selected media coverage: IN BRIEF of Nature Reviews Drug Discovery, Headline of OHIO STATE NEWS, Top 5% of all research ouputs scores by Altmetric
Estimating individual treatment effects with time-varying confounders
Ruoqi Liu, Changchang Yin, Ping Zhang
IEEE International Conference on Data Mining (ICDM), 2020 (Acceptance rate: 91/930 = 9.8%, regular paper, oral presentation)
[Paper] [Code]
Identifying sepsis subphenotypes via time-aware multi-modal auto-encoder
Changchang Yin, Ruoqi Liu, Dongdong Zhang, Ping Zhang
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2020 (Acceptance rate: 216/1279 = 16.9%, research track, oral presentation)
[Paper] [Code] [Video]
Clinical connectivity map for drug repurposing: using laboratory tests to bridge drugs and diseases
Qianlong Wen†*, Ruoqi Liu*, Ping Zhang
BMC Medical Informatics and Decision Making 21:263, 2021
[Paper] [Code]
Towards early detection of adverse drug reactions: combining pre-clinical drug structures and post-market safety reports
Ruoqi Liu, Ping Zhang
BMC Medical Informatics and Decision Making 19:279, 2019
[Paper] [Code]
Predicting drug-disease associations by using similarity constrained matrix factorization
Wen Zhang, Xiang Yue, Weiran Lin, Wenjian Wu, Ruoqi Liu, Feng Huang, Feng Liu
BMC Bioinformatics 19:233, 2018
[Paper] [Web Server][Code]
Predicting drug-disease associations and their therapeutic function based on the drug-disease association bipartite network
Wen Zhang, Xiang Yue, Feng Huang, Ruoqi Liu, Yanlin Chen, Feng Huang, Chunyang Ruan
Methods 145:51-59, 2018
[Paper]
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