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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About me
About me
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Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 1
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conferences
Conferences
2022 - Machine Learning in Healthcare (MLHC) - PyDTS: A Python Package for Discrete-Time Survival Analysis with Competing Risks 2021 - 15th NATO Operations Research \& Analysis - COVID 19 data analysis and modeling - keynote
2021 - NATO webinar - Israel’s Best Practices in Enhancing Resilience – the Case of Covid-19
2020 - ICML HSYS (Spotlight) - Who should we test for COVID-19? A triage model from national symptom surveys
2020 - ICML HSYS - Rapid deployment of a nationwide symptoms survey - framework and applications
2020 - MICCAI - 3D convolutional sequence to sequence model for VCF identification in CT
2019 - X International Conference of the Georgian Mathematical Union - Buckling of columns with non-ideal boundary conditions
portfolio
Portfolio item number 1
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Portfolio item number 2
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projects-covid
Data-Driven COVID-19 Rapid Response
The World Health Organization (WHO) declared the outbreak of corona-virus disease 2019 (COVID-19) a public health emergency of international concern on 30 January 2020 and a pandemic on 11 March 2020. At that time, there were many unknowns and there was an urgent need to shed light on the nature and impact of virus, and to assist public health officials and policymakers. In the following I provide a brief overview of the projects I worked on as a researcher at Prof. Eran Segal’s lab at the Weizmann Institute of Science, aimed to develop strategic tools and provide data-driven insights for the public and policy makers.
projects-pydts
Discrete-time Survival Analysis with Competing Risks
Time-to-event analysis (survival analysis) is used when the outcome of interest is the time until a pre-specified event occurs. Sometimes, the event can be one of several distinct event types, known as competing risks (events).
projects-vcf
Vertebral Compression Fracture Identification from Computed Tomography
An osteoporosis-related fracture occurs every three seconds worldwide, affecting one in three women and one in five men aged over 50. The early detection of at-risk patients facilitates effective and well evidenced preventative interventions, reducing the incidence of major osteoporotic fractures. In this study (Chettrit and Meir et al., 2020), published in proceedings of MICCAI (2020) conference, we present an automatic system for identification of vertebral compression fractures (VCF) on Computed Tomography (CT) images. VCF are a medical condition where one or more vertebrae in the spine collapse. This condition, which often remains undiagnosed, can serve as an early indicator for osteoporosis and can initiate treatment.
publications
Publications
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talks
Talks
2021 - 15th NATO Operations Research \& Analysis - COVID 19 data analysis and modeling - keynote 2021 - NATO webinar - Israel’s Best Practices in Enhancing Resilience – the Case of Covid-19 2020 - ICML HSYS (Spotlight) - Who should we test for COVID-19? A triage model from national symptom surveys 2020 - ICML HSYS - Rapid deployment of a nationwide symptoms survey - framework and applications 2020 - MICCAI - 3D convolutional sequence to sequence model for VCF identification in CT 2019 - X International Conference of the Georgian Mathematical Union - Buckling of columns with non-ideal boundary conditions
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
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