Coby Maron
About Coby Maron
Coby Maron is a Deep Learning Algorithm Developer with extensive experience in image recognition and statistical analysis. He has worked at Trax Image Recognition and Goji LTD, contributing to the development of image segmentation applications and scene-classification tools.
Work at Trax
Coby Maron currently serves as a Deep Learning Algorithm Developer at Trax Image Recognition, a position he has held since 2018. In this role, he has designed and developed Scene-Classification tools and implemented continuous online network performance monitoring for Image Segmentation applications. Prior to his current position, he worked as a Data Scientist at Trax from 2015 to 2018, where he led the end-to-end development of Image Segmentation applications, including automated training and deployment to production.
Previous Experience at Goji LTD
Before joining Trax, Coby Maron worked at Goji LTD as an EM Engineer from 2011 to 2015. During his four years at Goji, he contributed to various engineering projects, enhancing his expertise in the field. His experience at Goji laid the foundation for his later work in image recognition and deep learning.
Education and Expertise
Coby Maron earned a Master's Degree in Electromagnetic Radiation and Relativistic Electromagnetism from Ben Gurion University, completing his studies from 2011 to 2013. He also holds a Bachelor of Science (B.Sc.) in Electrical and Computer Engineering from the same institution, which he completed from 2005 to 2010. His academic background supports his technical skills in deep learning and image processing.
Teaching Experience
Coby Maron served as a Teaching Assistant (T.A.) at Ben Gurion University from 2009 to 2012. In this role, he assisted in the educational development of students, contributing to their understanding of complex engineering concepts. His teaching experience complements his technical expertise.
Technical Contributions
Throughout his career, Coby Maron has developed automated tools for statistical analysis of label data to enhance network training. His contributions to the field of deep learning and image segmentation demonstrate his commitment to advancing technology in these areas.