Douglas Holman
About Douglas Holman
Douglas Holman is a Staff Machine Learning Engineer at Flock Safety, specializing in computer vision and natural language processing.
Title
Douglas Holman is currently a Staff Machine Learning Engineer at Flock Safety. He previously served as Machine Learning Manager at the same company from 2021 to 2022.
Education and Expertise
Douglas Holman holds a Master's degree in Computer and Information Technology from the University of Pennsylvania. He also has an Honors Bachelor of Science in Biochemistry and Biophysics from Oregon State University and has also attended Stanford University where he studied Economics and Human Physiology. His specialized fields are computer vision and natural language processing, with robust knowledge in applied mathematics, specifically algebraic geometry and computational algebraic topology. He is also adept in reinforcement learning and probabilistic graphical models.
Professional Background
Douglas Holman has extensive experience in machine learning and data science roles. He worked at Booz Allen Hamilton as an Associate, Lead Data Scientist from 2020 to 2021 in the San Francisco Bay Area. Before that, he was a Senior Machine Learning Engineer at Modzy from 2020 to 2021. Additionally, he has held roles as a Data Scientist at Zymergen, Inc., a Deep Learning Researcher at Oregon State University's Hendrix Lab, and a Genetics Researcher in the Burke lab at the same university. He began his professional journey as a Neuroscience Graduate Program Summer Fellow at Oregon Health and Science University in 2017.
Company
Flock Safety is where Douglas Holman currently applies his skills as a Staff Machine Learning Engineer. He previously led efforts in machine learning management at the company. Flock Safety is known for implementing technology in public safety to help reduce crime.
Research Interests
Douglas Holman has a keen interest in unsupervised learning and artificial intelligence. His work often touches on areas such as computer vision, natural language processing, and unsupervised learning methodologies. He applies his knowledge to build and refine machine learning models, balancing theoretical insights with practical applications.