Clinical AI reliability · Causal reasoning · Computational medicine
Habib Latifizadeh, Ph.D.
I examine that question through biostatistics, causal inference, and mechanistic modeling across medical images, electronic health records, physiological data, and biological systems. The emphasis is patient-level evidence: where errors concentrate, how uncertainty should be represented, and whether a model supports the decision being considered.
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
NSF NRT Research Fellowship · Digital Health in AI & Machine Learning · 2023–2024
Selected research projects
Each project page states the question, the available evidence, my contribution, and the limits on interpretation.
I am evaluating whether model failures concentrate in the same patients or encounters across structured clinical data and chest radiographs. The analysis centers on temporal 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, whereas the transferred threshold retained high sensitivity but low specificity.
I develop simulation and alignment methods for two related translation problems: maternal-placental-fetal CMV modeling and human-NHP immune-cell mapping under measurement and biological uncertainty.
I co-developed a method for inferring heterocellular networks from observational tumor data and used it to examine 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 preprint on ensemble causal structure learning, 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 is associated with local changes in tumor and immune-cell relationships.
H. Latifizadeh et al.
Statistical lead for a Duke analysis of the 5-SENSE focality score and postoperative seizure freedom.
Academic trajectory
My research trajectory extends from nonlinear systems and numerical methods to causal network inference, computational biology, biostatistics, and patient-level model evaluation.
Research experienceEditorial leadership
I serve on editorial or advisory boards for npj Digital Medicine, iScience, and the International Journal of Modeling, Simulation, and Scientific Computing, and I have reviewed more than 50 manuscripts.
Collaboration & contact
I welcome inquiries concerning clinical AI evaluation, biostatistics, causal inference, computational biology, and mechanism-informed modeling.
Get in touch