Paul Hegedus Email and Phone Number

Promotions: Hired as a data scientist in an Undergraduate Research Assistant role in January ‘15, promoted to Graduate Research Assistant in January ‘18, promoted to professional Research Associate in May ‘19, promoted to a Post Doc in May ‘22.Data Warehousing: Developed and managed a big-data infrastructure framework using scripting languages and GIS tools, generating 1.5+ TB of spatial and time-series data.ETL Workflows: Wrote APIs for the automation of data pipelines that performed data management, data transformation, dataset formatting, and exploratory data analysis, increasing data preparation efficiency by 300%. Modeling, Optimization & Deployment: Engineered and deployed predictive models using machine learning and AI that optimized chemical agricultural inputs based on the tradeoff between economics and the environment, yielding an average increase in farmer ROI of $30,000 per year and a decrease in pollution of 50%.Communications/Presentations: Interpreted results and translated modeling insights into actionable management recommendations; leading to over 35 presentations to external stakeholders at national and international conferences, over 50 presentations to internal stakeholders, 10 published journal articles and drafts, and earning 5 grants totaling $202,500.

Cross-Functional Collaboration: Worked extensively with Computer Science and Agronomy departments at multiple universities to concurrently design, distribute, and manage experiments for the OFPE and DIFM projects across 18 states and 5 countries, resulting in 255 successful field trials that supported research at 10 universities.Communication: Establish and maintain external communications with farmers and internal communications with collaborators to collect data and provide accessibility and security for thousands of data files per year to a global consortium of researchers, having an impact on global advances in data driven decision-making and adaptive management in the agricultural industry. Dashboard Development: Aid in the launch of web-based software and applications using Python and R-Shiny that create tools for precision agriculture management, with the goal of 100+ annual users observing increases in farm value.