About me
I am a Ph.D. student at ShalitLab at the Faculty of Data and Decision Sciences, Technion - Israel Institute of Technology. We specialize in machine learning and causal inference in healthcare.
My research has two primary objectives:
- Developing machine learning and causal inference methodologies which can be used in many scientific domains.
- Utilizing machine learning and causal inference techniques for healthcare applications to improve patient care.
I am a machine/deep learning researcher experienced in various data modalities such as Electronic Health Records (EHRs), time series data, time-to-event data, tabular data, and imaging.
Prior to my Ph.D. journey, I was a researcher at Segal Lab, Weizmann Institute of Science (2020-2021). Throughout the COVID-19 pandemic, most of my effort was to develop strategic tools for public-health officials and policymakers. We devised national symptoms surveys for identifying virus spread clusters, developed machine-learning models for predicting the future number of hospitalized patients and deaths, analysed what-if scenarios that project the consequences of various national strategies, and provided real-time assessment of vaccine effectiveness at the national level.
Before that, I was a deep learning researcher at Zebra Medical Vision (2018-2019), where I developed deep-learning algorithms for the automated analysis of medical imaging, primarily CT scans and X-rays. For example, early detection of vertebral compression fractures, which often remain undiagnosed, is crucial to initiate preventive treatments for osteoporosis. One of the main projects I worked on was the development of an automatic deep-learning-based system to identify vertebral compression fractures in computed tomography (CT) images. These algorithms are now being employed by healthcare providers to enhance population health.
I invite you to check out the dedicated pages for each of the main projects I worked on.