Daniel Barsky
About Daniel Barsky
Daniel Barsky is a Data Science Team Lead at Augury, with a strong background in machine learning and deep learning applications for machine health monitoring. He has previously worked at Technion - Israel Institute of Technology and Rafael Advanced Defense Systems, and holds a Master of Science in Machine Learning from Technion.
Work at Augury
Daniel Barsky has been employed at Augury since 2015, where he currently holds the position of Data Science Team Lead, a role he has occupied since 2021. In his capacity at Augury, he focuses on deep learning applications specifically for machine health monitoring. He also specializes in leveraging anomaly detection and time series algorithms tailored for both industrial and commercial sectors. His work involves utilizing signal processing techniques to develop insights related to machine health.
Education and Expertise
Daniel Barsky studied at the Technion - Israel Institute of Technology, where he earned a Master of Science (M.Sc.) in Machine Learning from 2012 to 2015. He also obtained a Bachelor of Science (B.Sc.) in Electrical and Electronics Engineering from the same institution, completing his studies from 2007 to 2011. Additionally, he participated in a Summer Internship at MIT in 2011, focusing on Computer Vision. His academic background supports his expertise in deep learning and signal processing.
Background
Before joining Augury, Daniel Barsky worked at Rafael Advanced Defense Systems as a Board & Logic Design Engineer from 2009 to 2012. He also served as a Teaching Assistant in the Introduction to Software Systems course at the Technion from 2012 to 2014 and as a Youth Laboratory Instructor in the Science-Seeking Youth Department at the Technion from 2008 to 2009. These roles contributed to his foundational knowledge and skills in engineering and data science.
Achievements
Daniel Barsky has developed a specialization in deep learning applications for machine health monitoring throughout his career. His work at Augury involves significant contributions to the development of algorithms for anomaly detection and time series analysis. He has also applied signal processing techniques to enhance machine health insights, which are critical in both industrial and commercial applications.