Joshua Dean
About Joshua Dean
Joshua Dean is a Computer Vision Engineer specializing in advanced algorithms for UAV applications, currently employed at Sentera since 2020. He focuses on integrating computer vision with UAV technology to enhance agricultural data collection and applies reinforcement learning to improve sim-to-real transfer for UAVs.
Work at Sentera
Joshua Dean has been employed at Sentera since 2020, serving as a Computer Vision Engineer. In this role, he focuses on developing advanced computer vision algorithms specifically for unmanned aerial vehicle (UAV) applications. His work aims to enhance the integration of computer vision with UAV technology, particularly in the context of agricultural data collection. Prior to his current position, he completed a two-month internship at Sentera in 2020, where he gained foundational experience in computer vision engineering.
Education and Expertise
Joshua Dean earned a Bachelor of Science degree in Computer Science from the University of Minnesota-Twin Cities, completing his studies from 2017 to 2020. His academic background provides him with a strong foundation in computer science principles, which he applies in his current role as a Computer Vision Engineer. His research interests include the application of reinforcement learning techniques to improve the sim-to-real transfer for UAVs, showcasing his expertise in both computer vision and machine learning.
Background
Joshua Dean has a background in computer vision and UAV technology. He began his career in this field during his studies at the University of Minnesota-Twin Cities, where he developed a strong interest in the intersection of computer science and aerial technology. His early experience as a Computer Vision Engineering Intern at Sentera allowed him to apply theoretical knowledge in a practical setting, leading to his current role as a Computer Vision Engineer.
Research Focus
Joshua Dean's research is centered on applying reinforcement learning to enhance the sim-to-real transfer for unmanned aerial vehicles (UAVs). This area of study is critical for improving the performance and reliability of UAV applications in various fields, including agriculture. His focus on this innovative approach reflects his commitment to advancing the capabilities of computer vision in UAV technology.