I’m Robert Donohue — a data scientist and sustainability professional focused on building decarbonization. I’m the Co-Founder & CTO of Enverdex, where I lead development of Praevion, an optimization engine that helps building owners identify retrofit pathways that balance cost, carbon, and performance—and translate that into decision-ready recommendations.

I’m also completing a Master’s in Data Science at Tufts University, with an emphasis on Bayesian methods, statistical modeling, and optimization. This site is where I publish the thinking behind what I’m building—technical deep dives, project notes, and lessons learned moving from research concepts to production software.

What I’m building at Enverdex

At Enverdex, my work spans the full stack of a modern technical product:

• Optimization & ML: multi-objective optimization, surrogate modeling, and uncertainty-aware decision support
• Energy modeling workflows: OpenStudio / EnergyPlus pipelines and simulation orchestration
• Product engineering: building data ingestion → KPI evaluation → recommendations → user-facing tooling
• Investor-grade clarity: turning complex tradeoffs into simple outputs that support action

Experience in sustainability + analytics

In parallel with Enverdex, I work on applied sustainability and analytics—supporting retrofit planning and portfolio strategy, including operational vs embodied carbon tradeoffs, and financial exposure analysis under emerging building performance regulations.

What you’ll find on this site

I write for technical readers (founders, engineers, researchers, and climate-tech investors) who want substance:

• Bayesian modeling in real workflows (not just theory)
• Optimization systems that survive constraints, noise, and messy inputs
• Energy modeling + data science as one integrated engineering problem
• Notes from projects I’m shipping, experiments I’m running, and research I’m translating into product

Topics I’m most excited about

• Multi-objective optimization for real-world design decisions
• Decision-making under uncertainty (Bayesian thinking in production)
• Scaling simulation-driven workflows with practical software engineering
• Building decarbonization: cost, carbon, and feasibility

Let’s connect

If you’re building in climate-tech, energy systems, or applied ML/optimization and want to compare notes, collaborate, or talk product, please reach out!