Clinical AI reliability · Causal reasoning · Computational medicine
Habib Latifizadeh, Ph.D.
My work combines biostatistics, causal inference, and mechanistic modeling across medical images, electronic health records, physiological data, and biological systems. I focus on patient-level reliability rather than average performance alone.
Laboratory for Computational Physiology
Project Co-Investigator & Visiting Scholar · 2026–2027
Lead Data Scientist & Biostatistician · Epilepsy Surgery Outcomes · 2026–2027
Postdoctoral Associate · Department of Biostatistics & Bioinformatics · 2024–Present
Selected research projects
Each page presents one question, the evidence available, my contribution, and the limits that shape interpretation.
I study whether the same patients or encounters are difficult for AI systems across structured clinical data and chest radiographs. The work centers on linkage, calibration, sensitivity analysis, uncertainty, and subgroup stability.
I led a retrospective analysis of the 5-SENSE focality score in 110 treated patients. The continuous score was associated with seizure freedom, but the transferred threshold had low specificity.
I develop simulation and alignment methods that connect maternal-placental-fetal CMV modeling with human-NHP immune-cell mapping under measurement and biological uncertainty.
I co-developed a data-based method for inferring cell-to-cell networks and used it to study how oncogenic CCN4 expression is associated with changes in tumor, immune, and stromal relationships.
Selected publications
H. Latifizadeh, A. C. Pirkey, A. Gould, D. J. Klinke II
First-author open framework for ensemble causal discovery, bootstrap stability, and edge-level uncertainty.
D. J. Klinke II, A. Fernandez, W. Deng, A. Razazan, H. Latifizadeh, A. C. Pirkey
Nature Communications. Computational analysis of how oncogenic gene expression changes tumor and immune-cell relationships.
H. Latifizadeh et al.
Statistical lead for a Duke study of the 5-SENSE focality score and postoperative seizure freedom.
Academic trajectory
My training moved from nonlinear systems and numerical methods to causal network inference, computational biology, biostatistics, and patient-level model evaluation.
Research experienceEditorial leadership
I serve on boards for npj Digital Medicine, iScience, and the International Journal of Modeling, Simulation, and Scientific Computing. I have reviewed more than 50 manuscripts.
Collaboration & contact
I welcome discussions on clinical AI evaluation, biostatistics, causal inference, computational biology, and mechanism-informed modeling.
Get in touch