Phil Regulski

Phil Regulski has over 20 years of experience in data science, engineering and research. In 1998, they began their career as a research assistant at San Diego State University, conducting research on spatial and temporal distributions of nova in M-31 and operating the Mt. Laguna 40-inch telescope during observational research sessions. In 2000, they worked as an Optoelectronic Engineer at IPITEK, developing and creating prototype fiber optic (de)multiplexing technologies and real-time stress monitoring systems encased in Graphite-Epoxy Motor (GEM) rockets for the United States Air Force. In 2001, they began working at the University of Washington as a Research Meteorologist/Engineer 2, where they completed multiple studies for local clients demonstrating effectiveness of new meteorological technologies as part of a larger cost-benefit analysis study, developed and maintained operational weather forecasting tools, graphics, and products, and conducted numerical weather prediction model and forecasting research. Phil then became a Research Meteorologist/Engineer 3 at the University of Washington, managing a real-time numerical weather prediction (NWP) and data assimilation system (UW Real-Time Regional Ensemble Data Assimilation System) and creating a nowcasting precipitation estimation system for a local utility (Seattle Rainwatch). In 2011, they began working at Alstom Grid (acquired by GE) as a Software Engineer - Services, developing, validating, and implementing electrical load demand forecast models for the e-terra Energy Management System (EMS) application suite and leading the development of Alstom's power systems analytics software solution: e-terraloadforecast. In 2015, they became a Senior Data Analytics Engineer - R&D at GE Grid Solutions, where they developed forecasting solutions for the energy industry (renewables, DERs, electrical load forecasting, etc.) using advanced forecasting models and large data sets, prototyped, implemented, and validated predictive models (machine learning methods, advanced analytics and statistical models, exploratory analysis), and was designated Subject Matter Expert for load demand forecasts and renewables forecasting. Phil then became a Technical Forecasting Team Lead - R&D, driving product vision, leading customer workshops, defining/refining requirements, writing detailed functional design specs, resource estimation, and cost quoting, and leading a successful customer bid for an 80M, next-generation Grid Management System solution with focus on vast renewable market penetration. In 2021, they began working at Swell Energy as a Lead Data Scientist, developing industry disrupting, state-of-the-art ML solutions for Virtual Power Plant (VPP) operations and successfully launching Swell Energy's Hawaiian Electric VPP project controlled by GridAmp. Phil is currently the Director, Data Science at Blueprint Power, where they are engineering environmentally sustainable and positive change by bringing clean energy solutions, reduced carbon footprints and additional revenue streams to real estate clients. Phil is leading a globally distributed team of data scientists, modelers and ML developers that create industry disrupting forecasting, optimization and modeling solutions that increase energy flexibility and reliability for client's buildings, and is creating BPP's DS vision to turn customer data into actionable and revenue building insights through exploratory analysis, ML and visualization.

Phil Regulski received their Bachelor's degree in Astronomy and Astrophysics from San Diego State University in 2000, graduating Magna Cum Laude. Phil then went on to receive their Master of Science in Atmospheric Sciences and Meteorology from the University of Washington in 2003.