Hugo Neu Corporation Sustainability Seminar Series: From Data to Decision: Leveraging AI to Accelerate Environmental Science and Climate Action

ABSTRACT

Artificial intelligence is fundamentally transforming how we understand, monitor, and respond to environmental challenges. Yet the promise of AI in environmental science remains underutilized—constrained by integration gaps, technical complexity, and the difficulty of translating model outputs into actionable decisions.

This talk explores how machine learning and AI are reshaping environmental science across three critical domains: precision environmental monitoring through earth observation, intelligent systems for climate resilience and energy management, and rapid response tools for environmental disasters. Through concrete examples, from algorithms that improved urban canopy detection by 35% to AI-powered post-disaster satellite analysis and intelligent energy consumption forecasting we'll examine how AI enables organizations to interpret environmental change with greater precision and act faster in the face of climate risk.

But technology alone is insufficient. The talk addresses the practical challenges of implementing AI in environmental contexts: designing systems that bridge the gap between complex models and clear policy decisions, managing stakeholder expectations, and ensuring that AI-driven solutions genuinely improve environmental outcomes rather than simply generating more data. Attendees will leave understanding how to evaluate AI opportunities in their own environmental and sustainability work, the critical questions to ask when deploying machine learning solutions, and how to build collaborative teams where scientists, engineers, and decision-makers work together to translate environmental insights into action.

BIOGRAPHY

Ryan Kmetz has over 12 years of experience at the intersection of climate resilience, geospatial analysis, and artificial intelligence, Ryan brings both strategic leadership and deep technical expertise to environmental challenges. As a subject matter expert in applied earth observation and AI, he has designed and implemented machine learning solutions that deliver measurable impact—including algorithms that improved urban forestry canopy segmentation accuracy by 35%, AI-powered disaster response tools for post-hurricane satellite imagery analysis and automated roof tarp detection, and novel unsupervised learning approaches for earth observation data.

Across roles spanning state government, higher education, and consulting, Ryan has built data systems and sustainability programs from the ground up. His work includes managing multi-million-dollar decarbonization portfolios, architecting GIS-based decision support tools, developing machine learning models for energy consumption optimization and demand forecasting, and integrating Python, large language models, and automation to scale environmental impact across organizations and communities. He has designed intelligent energy management systems that reduce operational costs while advancing organizational sustainability goals. His technical expertise spans climate resilience planning, remote sensing and geospatial analytics, machine learning and AI integration for energy systems and environmental monitoring, program and stakeholder management, and sustainability strategy. He is particularly skilled at translating complex technical insights into clear, actionable decisions that drive organizational change and environmental outcomes.

Motivated by a mission to use data and technology for meaningful environmental change, Ryan thrives in collaborative, cross-functional environments where science, policy, and engineering converge—especially when the work directly addresses climate risk and environmental justice.

Zoom Link:

Website:

https://www.stevens.edu/sustainability-seminar-series

Jointly Sponsored by:

HUGO NEU Corporation
WSP
HDR
NOMADICS
Brown and Caldwell
H2M Architects and Engineers
Dewberry

Contact:

Dr. Dibs Sarkar