Raffy Xie honored with 2026 Departmental Citation in Data Science Undergraduate Studies

When Yuzi (Raffy) Xie first arrived at UC Berkeley as a chemistry major, he was eager to use his scientific knowledge to make a real environmental impact. Then he discovered data science, and with it, the possibility to connect chemical processes to systems-level patterns across entire regions.

Xie’s academic achievement at the intersection of these fields has earned him the honor of receiving the 2026 UC Berkeley Departmental Citation – the highest departmental honor for a single graduating senior – in both Data Science Undergraduate Studies and Chemistry

Xie’s journey took shape during his sophomore year, when he joined Professor Ronald C. Cohen’s research group working with the Berkeley Environmental Air-quality & CO₂ Network (BEACO₂N), a large-scale system that collects air quality data across the Bay Area every few seconds. He found that nearly everyone in the group relied on data science methods to turn the enormous datasets generated by the network into a coherent picture of air quality. 

As he progressed through the data science major, Xie built the computational and statistical foundation needed to work at this larger scale. Courses in optimization, statistical inference, stochastic processes, and machine learning gave him the tools to model complex environmental systems.

“The major doesn’t have a chemistry domain emphasis,” Xie reflected, “but that exposed me to the math emphasis. I learned about stochastic processes and optimization, which actually became fundamental in my research.”

That interdisciplinary training became especially important in Xie’s undergraduate research on low-cost methane sensing, where the central challenge was not only chemical accuracy, but also scalability and access. Traditional methane analyzers can cost roughly $75,000 each, making deployment difficult for many communities and researchers. Because of this, Xie investigated whether sensors costing closer to $20 could still be calibrated to reliably measure methane in the atmosphere.

Rather than treating the sensor as a black box, Xie developed a calibration framework that combined chemical modeling with data cleaning and optimization methods. 

“It’s not only about improving predictive performance,” said Xie. “It’s also about building a model grounded in scientific understanding.”

This work also reinforced Xie’s growing interest in environmental justice. Communities near refineries, landfills and wastewater treatment facilities may face higher exposure to pollutants while lacking access to expensive monitoring technologies. Low-cost sensors could help make methane emissions more visible and measurable across these sites, supporting earlier detection and mitigation of environmental harm.

That same commitment to accessibility appears in Xie’s outreach work through Bay Area Scientists Inspiring Students (BASIS), where he visits K–12 classrooms to lead hands-on lessons about atmospheric science and cloud formations.

“I kind of see making sensors low-cost and doing outreach in a parallel way,” Xie said. “On one side, you’re making sensors accessible for everyone. On the other side, you’re making science accessible for everyone.”

At Berkeley, Xie found strong support from mentors, graduate students, friends and family members who helped shape his growth as a researcher. He recalled arriving at his first group meeting as a shy sophomore surrounded by PhD students and faculty researchers, only to be greeted by a welcoming community.

“They never said my questions were stupid,” Xie said. “They just explained things and encouraged me to ask more.”

This fall, Xie will continue his interdisciplinary journey as a PhD student at Massachusetts Institute of Technology Institute for Data, Systems, and Society, where he plans to combine atmospheric chemistry, machine learning and large-scale computational analysis to study global greenhouse gas emissions and air pollution.

For Xie, the future of environmental science lies in exactly the kind of scale-shifting work Berkeley encouraged him to pursue.

“Data science is a really powerful tool,” he said. “It can help so many different fields, especially scientific research, but also societal problems.”