Professional Overview
Water and Environmental Data Scientist with a PhD and 7+ years of experience developing hybrid physics–machine-learning models for watershed hydrology, reservoirs, and water quality. I build scalable data pipelines, digital twins, and decision-support tools that turn monitoring and climate scenarios into practical strategies for resilient systems.
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Led ML emulator development for Upper Mississippi River hydrologic simulations
- Built hybrid physics–ML models and automated environmental data workflows
- Integrated watershed and reservoir models for climate- and land-use scenarios
- Led research in smart water-quality monitoring and pollution analytics
- Advanced digital twins, contaminant transport, and hydrologic data assimilatio
Key Achievements / KPIs
- Built ML emulators of water-quality models, cutting run time by over 90%
- Developed real-time predictive models for contaminant surveillance, 97% accuracy
- Authored 30+ peer-reviewed papers on water-quality and hydrologic modeling
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built hybrid physics–ML models and automated environmental data workflows
- Led research in smart water-quality monitoring, pollution analytics, and climate
- Advanced digital twins, contaminant transport, and hydrologic data assimilation
- Supervised graduate research in hydroinformatics and environmental data science
- Co-investigator on funded industry partnership projects
Key Achievements / KPIs
- Modeled climate and land-use effects on watershed hydrology and water quality
- Assessed pathogens and emerging contaminants to protect public health
- Built efficient AI/ML emulators for water-quality prediction and infrastructure
- Developed data-driven tools and digital twins for resilient water-system managem
Areas of Expertise
Data Science & Analytics
Extract business insights from raw data using statistical modelling, predictive analytics, A/B testing, and interactive dashboards.
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.