Research

We leverage geospatial, thermodynamic and techno-economic modeling to understand the opportunities and implications of water sector electrification and decarbonization. By putting the "science of where" into the core of environmental modeling, we derive solutions that are adaptive to local conditions.

Frontier AI including large language models, chemical language models, and machine learning algorithms are changing how we interact with information. The water sector is information-rich yet slow toward adopting AI. We study how to implement trustworthy AI systems that minimize hallucination, streamline automation, and bring expert-level knowledge to every operator, engineer, and decision-maker. The nexus runs both ways: AI is a thirsty sector with ever-growing water needs, and using AI for sustainability means AI development itself should put sustainability at its core. We develop and assess technologies for securing alternative water sources that alleviate the competition between AI's water demand and existing freshwater supplies.

Used water is a resource in the wrong place. We study de facto and planned water reuse to understand where recycled water already sustains our supplies and where it can go further, and we analyze treatment processes that recover energy and resources from wastewater instead of discarding them. Beyond reuse, we explore unconventional sources to diversify supply where conventional infrastructure falls short.