Dan Starr
About Dan Starr
Dan Starr is a Senior Machine Learning Engineer with extensive experience in developing machine learning frameworks and applications. He has worked at notable institutions such as UC Berkeley and Los Alamos National Laboratory, and co-founded a machine learning startup acquired by GE Digital.
Work at Aquabyte
Dan Starr has been employed at Aquabyte as a Senior Machine Learning Engineer since 2021. In this role, he focuses on developing and implementing machine learning models that enhance the company's capabilities in the aquaculture industry. His current projects involve aggregating IoT sensors and APIs, as well as applying models to both image and time-series datasets. This work contributes to the optimization of fish farming practices through data-driven insights.
Education and Expertise
Dan Starr earned a Bachelor of Science degree in Physics and Astronomy from the University of Washington, where he studied from 1996 to 2001. His academic background provides a strong foundation for his expertise in machine learning and analytics. He has developed a framework for productionizing machine learning models, which is utilized in consulting work, demonstrating his ability to apply theoretical knowledge to practical applications.
Background
Dan Starr has a diverse professional background in software development and data analytics. He worked at the Los Alamos National Laboratory as a Software Developer from 2001 to 2003 and later at the Harvard-Smithsonian Center for Astrophysics for five months in 2004. He also served as a Data Analyst and Python Programmer at the Gemini Observatory from 2004 to 2006. Prior to his current role, he was a Data Analytics Software Engineer at UC Berkeley from 2006 to 2012.
Achievements
Dan Starr co-founded an enterprise machine learning startup that was acquired by GE Digital, highlighting his entrepreneurial experience in the technology sector. He has significant experience in developing machine learning and analytics applications that are productionized and KPI observable. His work has included applying machine learning to various temporal data problems, including real-time time-series challenges in both astronomy and commercial applications.