Team

Yishai Sompolinsky, MD, MPH

Co-Director

Maternal–Fetal Medicine specialist and AI researcher. Leads the Labor-AI Lab, focusing on explainable, reliable AI decision-support tools in obstetrics and large-scale perinatal data integration.

Michal Lipschuetz, RN, PhD

Co-Director

Leads the academic and methodological direction of Labor-AI, with extensive experience in obstetric research, epidemiology, and bioinformatics, bridging clinical questions with rigorous data-driven methods.

Simcha Yagel, MD

Core Team

Senior academic leader in obstetrics and maternal–fetal medicine. Provides clinical and scientific leadership supporting the lab’s mission to translate data-driven methods into impactful obstetric care.

Joshua Guedalia, PhD

Core Team

Computer scientist specializing in clinical machine learning. Contributes to model development, validation, and translation of AI methods for real-world obstetric decision support.

Gabriella Gross

B.Sc., M.Sc. Bioinformatics candidate

BSc in Computer Science from the Hebrew University of Jerusalem, currently completing her MSc in Bioinformatics with the Labor-AI Lab. Focuses on database development and reinforcement learning frameworks for clinical decision support in obstetrics.

Ellie Leidner

M.Sc. Computer Sciences and MD candidate

Graduate of the prestigious joint BSc program in Medicine and Computer Science at the Hebrew University. Leads advanced image processing tasks in the lab, applying deep learning methods to medical imaging challenges.

Liav Feldman

MD candidate

Graduate of the prestigious joint BSc program in Medicine and Computer Science at the Hebrew University. Focuses on developing unsupervised machine learning models for pattern discovery in labor and delivery data.

Hadar Wolf

B.Sc. Computer Science student

B.Sc. student in Computer Science at the Hebrew University of Jerusalem. Contributes to the development and validation of real-time prediction models for labor and delivery outcomes.