Michael Stanley
About Michael Stanley
Michael Stanley is a Senior Machine Learning Engineer with extensive experience in computer vision and synthetic data generation. He has worked at various organizations, including Enigma Technologies and NYU Center for Data Science, and holds degrees from Duke University and New York University.
Current Role at Parallel Domain
Michael Stanley serves as a Senior Machine Learning Engineer at Parallel Domain, where he has been employed since 2023. In this role, he focuses on developing advanced 2D and 3D vision models that utilize synthetic data to enhance computer vision capabilities. His work involves cutting-edge techniques in 3D reconstruction, aimed at creating realistic synthetic environments for various applications.
Previous Experience at Enigma Technologies, Inc.
Prior to his current position, Michael worked as a Product Manager at Enigma Technologies, Inc. from 2017 to 2019. During his tenure in the Greater New York City Area, he contributed to product development and management, leveraging his expertise in data science and machine learning.
Educational Background
Michael Stanley holds a Bachelor of Science in Engineering (B.S.E.) with a focus on Mechanical Engineering and Material Science, as well as Economics from Duke University, where he studied from 2003 to 2007. He later earned a Master's degree in Data Science from New York University, completing his studies from 2019 to 2021. This educational foundation supports his work in machine learning and data analysis.
Research Experience at NYU Center for Data Science
From 2020 to 2021, Michael worked as a Researcher at the NYU Center for Data Science in New York City. His research focused on the application of synthetic data in improving computer vision models. He also held a position as a Researcher and Team Leader at the NYU Center for Urban Science + Progress during the same period, where he led projects that explored urban data applications.
Contributions to Computer Vision Research
Michael has made significant contributions to the field of computer vision, particularly in the area of cyclist detection. He co-authored a publication on the Open Synthetic Dataset for Improving Cyclist Detection and was part of a team that demonstrated the effectiveness of synthetic data using an off-the-shelf YOLOv3 model. His research has led to notable advancements in 2D bounding box detection for cyclists.