Saul Goldblatt
About Saul Goldblatt
Saul Goldblatt is a Lead Computer Vision Engineer with extensive experience in developing computer vision systems for agricultural robotics. He holds a Master's degree in Embedded Systems with Computer Vision from Kingston University and has worked in various roles across the technology sector, including positions at Archangel Imaging and Perceptual Robotics.
Work at Saga Robotics
Saul Goldblatt currently serves as the Lead Computer Vision Engineer at Saga Robotics, a position he has held since 2020. In this role, he focuses on developing computer vision systems specifically tailored for agricultural robotics. His work involves integrating advanced technologies to enhance the efficiency and effectiveness of robotic systems in agricultural applications.
Education and Expertise
Saul Goldblatt holds a Master of Science degree in Embedded Systems with Computer Vision from Kingston University, which he completed from 2015 to 2016. He also earned a Bachelor of Science degree in Physics and Mathematics (International) from the University of Leeds, studying from 2009 to 2013. His educational background provides a strong foundation for his expertise in computer vision, deep learning, and data engineering.
Professional Background
Saul Goldblatt has a diverse professional background in computer vision and engineering. He worked as a Computer Vision Lead at Archangel Imaging for eight months in 2020. Prior to that, he was the Lead Computer Vision Engineer at Perceptual Robotics from 2017 to 2019. His experience also includes a role as a Software Engineer at Micrima Limited and as a Technical Consultant at WRc plc.
Technical Skills and Specializations
Saul Goldblatt possesses extensive technical skills in deploying computer vision algorithms on robots using the Robot Operating System (ROS). He is experienced in training and deploying convolutional neural networks (CNNs) utilizing frameworks such as Pytorch, TensorRT, and Keras. His expertise extends to data engineering for deep learning problems, particularly within the industrial automation sector.