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The current project aims to improve spectrophotometric testing—a branch of chemical physics—in environmental applications by applying AI/ML techniques. Spectrophotometry is a powerful analytical technique based on the principle that every chemical absorbs light at specific wavelengths, enabling identification and quantification of hundreds of substances in chemistry, biology, and medicine (think color-change-based measurements in blood glucose monitors, chlorine test strips in swimming pools, or digital thermometers). However, each spectrophotometric application requires specialized, often expensive instruments called spectrophotometers, creating significant cost and logistical barriers.
The RAD Collaboratory Summer Undergraduate Research Fellow will co-lead the development and deployment of AI/ML models as alternatives to spectrophotometers to measure contaminants in water and wastewater, optimize image-based quantification algorithms, and support integration into field-ready tools (e.g., mobile apps).
The SURF Fellow's efforts will help generate preliminary data, research articles, and research proposals to secure long-term funding for undergraduate and graduate researchers. Given the federal priority on AI applications, this project is strongly aligned with federal agency funding goals, including the White House’s America’s AI Action Plan (pillars: Accelerate AI Innovation, Build American AI Infrastructure, and Lead in International AI Diplomacy and Security), NSF’s AI Institutes, and DOD’s environmental protection integration.
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