Hi there! I’m Xiaoxuan Li. I am a Ph.D. student in Biomedical Engineering at Tulane University since Fall 2024, advised by Prof. Yu-Ping Wang. I am also a member of the Multiscale Bioimaging and Bioinformatics Laboratory (MBB) at Tulane University. My research develops statistically grounded deep learning methods for complex, repeated-measures neuroimaging and multimodal health data, with an emphasis on subject-specific variability, latent and nonlinear mixed-effects modeling, and interpretable, data-efficient biomarker discovery.

Before joining Tulane, I received my B.Eng. in Automation from Sichuan University, where I worked on psychiatric neuroimaging and individualized functional connectivity analysis in chronic insomnia and related disorders.

🧠 Research Interests

• Statistical deep learning for complex repeated-measures medical data, with emphasis on subject-specific variability, missingness, and uncertainty.
• Multimodal integration of neuroimaging, genetics, and behavioral data for mental health and neurodevelopment.
• Interpretable and data-efficient methods for deriving individual-level biomarkers from incomplete or costly-to-acquire measurements.

🔥 News

• 2026.11: 🎯 Preparing a lightning talk and poster proposal for the IMSI workshop From Connectomics to Brain Dynamics: Ordinary Differential Equations and Beyond.
• 2026.07: ✨ Incoming participant, Summer Institute on Reproducible Research in Brain Imaging Genetics (RR-BIG), University of Minnesota School of Public Health.
• 2026.04: 📌 Presented a poster at the Tulane Research, Innovation, & Creativity Summit (TRICS) on nonlinear mixed-effects modeling for multi-paradigm fMRI.
• 2026.03: 🎤 Presented a lightning talk and poster at MCBIOS on nonlinear mixed-effects modeling for multi-paradigm fMRI.
• 2026.03: 📍 Presented a poster at the Brain Institute Retreat on nonlinear mixed-effects modeling for multi-paradigm fMRI.
• 2026.01: 🎉🎉 One paper, Personalized Functional Network Connectivity Abnormalities in Chronic Insomnia Disorder, was accepted to Psychoradiology.
• 2024.08: 🎉🎉 One paper, Functional Connectome Gradient Predicts Clinical Symptoms of Chronic Insomnia Disorder, was accepted to Progress in Neuro-Psychopharmacology and Biological Psychiatry.

📝 Selected Publications

• X Li, Z Ding, Y.-P. Wang, Learning Subject-Aware Latent Mixed-Effects Models for Multi-Condition fMRI (in preparation for submission to ICLR 2026).
• X Li, G Zhang, G Qu, A Orlichenko, Z Ding, …, Modeling Complex Effects and Individual Variability in Multi-Paradigm fMRI with Nonlinear Mixed Models (Submitted).
• X Li, Y Zhai, T Qin, Z Zhang, Z Zhao, H Deng, …, Personalized Functional Network Connectivity Abnormalities in Chronic Insomnia Disorder. Journal of Psychoradiology.
• A Orlichenko, S Ding, E Johns, Z Gu, X Tian, X Li, …, Intervention on Modifiable Lifestyle and Physiological Factors via Variational Autoencoder Reveals Changes in Functional Connectivity-Mediated Risk for Alzheimer’s Disease. medRxiv.
• J Wu, J Yang, Z Yuan, J Zhang, Z Zhang, T Qin, X Li, H Deng, L Gong, Functional Connectome Gradient Predicts Clinical Symptoms of Chronic Insomnia Disorder. Progress in Neuro-Psychopharmacology and Biological Psychiatry.

For a complete list of publications, please see my Google Scholar profile.

🎤 Presentations & Talks

• 2026.11 (Incoming): IMSI Workshop, From Connectomics to Brain Dynamics: Ordinary Differential Equations and Beyond — Lightning talk and poster proposal: Learning Subject-Aware Latent Mixed-Effects Models for Multi-Condition fMRI.
• 2026.04: Tulane Research, Innovation, & Creativity Summit (TRICS) — Poster presentation: Modeling Complex Effects and Individual Variability in Multi-Paradigm fMRI with Nonlinear Mixed Models.
• 2026.03: MCBIOS — Lightning talk and poster presentation: Modeling Complex Effects and Individual Variability in Multi-Paradigm fMRI with Nonlinear Mixed Models.
• 2026.03: Brain Institute Retreat — Poster presentation: Modeling Complex Effects and Individual Variability in Multi-Paradigm fMRI with Nonlinear Mixed Models.

🧪 Selected Research Training

• 2026.07 (incoming): Summer Institute on Reproducible Research in Brain Imaging Genetics (RR-BIG), Participant, University of Minnesota School of Public Health, Minneapolis, MN. Training in reproducible analysis of large-scale brain imaging genetics data, with emphasis on imaging, behavioral, and cohort-based data integration.
• 2023.07 – 2023.08: Laboratory of Brain Imaging and Pattern Recognition, Visiting Student, University of Electronic Science and Technology of China (UESTC), with Prof. Huafu Chen, Chengdu, China. Intensive training in medical image analysis and neuroimaging methods.

🏅 Honors and Awards

• 2023: First Prize Scholarship, Sichuan University.

📖 Education

• 2024.09 – 2029 (expected): Ph.D. in Biomedical Engineering, Tulane University, New Orleans, LA, USA.
• 2020.09 – 2024.06: B.Eng. in Automation, Sichuan University, Chengdu, China.