About me
I am an AI researcher working at the intersection of machine learning, causal inference, and healthcare.
I completed my PhD at the Technion’s Faculty of Data and Decision Sciences under the supervision of Prof. Uri Shalit and Prof. Danny Eytan.
My research background includes:
- Zebra Medical Vision Ltd.: Prediction of clinical findings in CT scans using computer vision and deep learning.
- Segal Lab, Weizmann Institute of Science: COVID-19 response research, applying prediction and causal inference methods to tabular and time-to-event data.
- PhD, Technion: Heterogeneous treatment-effect estimation in time-to-event data, survival analysis, and physical AI — hybrid mechanistic–data-driven models that combine scientific knowledge with deep learning to model system dynamics.
I have experience with a variety of data modalities, including patients’ electronic health records, longitudinal time series, survival data, and medical imaging.
Selected research
Select a project title to read more.
- Hybrid models for intervention outcomes — Combining mechanistic models with neural networks to predict responses to time-dependent interventions, including under distribution shift. Preprint, 2026.
- Personalized treatment effects with censored outcomes — MISTR estimates how treatment benefits vary across patients when follow-up is incomplete. ICML, 2025.
- Survival analysis and PyDTS — Efficient methods and open-source tools for discrete-time outcomes with competing events, with an application to ICU length of stay. Biometrics and JOSS, 2025.
- COVID-19 response — National symptom surveys to identify outbreak hotspots and prioritize testing, modeling of hospital demand and mortality, and studies of population-level vaccine impact. Nature Communications and Nature Medicine, 2021.
- Fracture detection in CT — Deep learning for identifying vertebral compression fractures in routine CT imaging. MICCAI, 2020.