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Highlights
Healthcare machine learning researcher with 8+ years of experience
EB1-A visa approved (US)
1st author Cell paper, 2019
Creator of causallib
- an open-source Python package for causal inference.
750+ stars and 100+ forks on Github.
Received an IBM Research Accomplishment award (2023)
Co-inventor on 3 US patents
PyData conference speaker, lecturer, and podcast interviewee
Causal inference, machine learning, deep learning, statistics, data viz, Python
About me
Highly skilled in causal inference, machine learning, (Bayesian) statistics, and data visualization. An applied researcher and data scientist, I spend my time between building reusable tools for research and putting them into use. Advocating Clean Code for research code. Strong preference for eclectic, collaborative environments.
Experience
2017 – present |
Research Staff Member
Causal Machine Learning for Healthcare and Life Science, IBM-Research, Israel
Creator of causallib – a one-stop-shop open-source Python package for flexible causal inference modeling.
- Received an IBM Research Accomplishment award (2023)
Client project leader from start to finish: translating vague clinicians’ questions into concrete statistical hypotheses, answering them, and communicating the findings
Led and designed a drug repurposing software asset, applying high-throughput causal inference to observational healthcare data
Managing a team of 5 researchers.
Leading the scientific pipeline, system design, and visualization app
Generating 100s of hypotheses in minutes
Serving 4 external engagements with top pharma clients, bringing millions in revenue
Individual Contributor (IC)
Causal inference consultant for projects in the US, UK, France, Japan, Kenya, South Africa, and Switzerland
Led global strategy at IBM Research for causality in drug discovery
- Oversaw research of adaptive experimentation using Bayesian inference
Mentored 10+ students and interns
Onboarding lead, onboarding 10+ researchers
Published 10+ papers and issued 3 US patents
2024:
Deconfounding transformer-based large language models for biological sequence, overcoming batch effects across and within diverse data sources
GLM-ification of deep learning models, bringing established biostatistics concepts into transformer-based deep learning models
2025:
- Quantum Advantage task force member [2025]: developing and testing quantum algorithms for combinatorial optimization problems
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2022 |
Principle Statistician
Laboratory for Gait & Neurodynamics, Ichilov Hospital
- Bayesian hierarchical/multilevel models and causal inference for gait analysis in multiple sclerosis patients
- Bayesian multilevel models for hurdle models of repeated patients’ measurements
- Formal causal inference with DAGs for minimizing inessential tests, saving over 3 hours of unnecessary tests by clinicians per patient.
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2016 – 2017 |
Teaching Assitant
The School of Computer Science, Hebrew University
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2015 – 2016 |
Research Associate / Computational Biologist
Institue for Medical Research Israel-Canada, Hadassah Hospital
- Developing novel methodologies for finding high-resolution protein-RNA interactions using high-volume data
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Education
2016 – 2019 |
M.Sc. in Computer Science and Computational Biology
Faculty of Science, the Hebrew University of Jerusalem, Israel
Thesis: quantifying the utility of embryo selection using genomic prediction of traits
published in Cell
Predicting physical traits from DNA (GWAS) using classical, machine learning, and deep learning methods
Pioneering the effects of prediction-based embryo selection in IVF
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2013 – 2016 |
B.Sc. in Computer Science and Computational Biology
Faculty of Science, the Hebrew University of Jerusalem, Israel
- Dean’s List of Academic Excellence (2016)
- Research scholarship from IMRIC (2016)
Bachelor’s thesis published in Nucleic Acids Research |
Skills
Programming skills |
Python scientific stack (fluent)
- Pandas, Numpy, Scikit-Learn, Statsmodels, PyTorch (lightning), Bambi, PyMC, Arviz, Matplotlib, Seaborn (objects). Altair, Streamlit, cvxpy, Pydantic, Hydra,
R (when needed)
Git + GitHub
Continuous development (Travis, GitHub Actions)
Linux and remote development (Cloud/AWS + Jupyter lab / VS Code)
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Languages |
Fluent English
Native Hebrew
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General |
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Awards
2023 |
IBM-Research Accomplishment
For my work on causallib and research engagement with the Cleveland Clinic Foundation |
2019 |
Best of RSNA
For the paper Predicting Breast Cancer by Applying Deep Learning to Linked Health Records and Mammograms, published in Radiology |
2019 |
Best Talk: Israeli Population Genetics Meeting
For the paper Screening Human Embryos for Polygenic Traits has Limited Utility |
2019 |
Featured Theory of the issue (Cell)
For the paper Screening Human Embryos for Polygenic Traits has Limited Utility |
2016 |
Dean’s list of academic excellence |
Publications
May go out of date. Please see my Google Scholar page for the most up-to-date information.