Elizabeth A. Barnes Named Inaugural Dalton Family Chair in Environmental Data Science & Sustainability
In fall of 2025, Boston University appointed Elizabeth A. (Libby) Barnes as the inaugural Dalton Family Chair in Environmental Data Science & Sustainability and Professor of Computing & Data Sciences and of Earth & Environment. Barnes is a scientist whose research spans the intersection of AI, data science, and Earth systems—advancing prediction of climate and extreme weather on timescales from days to decades.
“Libby brings an extraordinary combination of technical expertise, creativity, and collaborative spirit to BU,” said Azer Bestavros, Associate Provost in the Faculty of Computing & Data Sciences (CDS). “As a leader working across AI and climate prediction, she exemplifies the interdisciplinary ethos that defines CDS and strengthens BU’s role in tackling urgent global issues.”
Through her dual appointment at BU, Barnes will help position the University at the forefront of climate science and AI innovation—building tools, insights, and collaborations that advance both scientific understanding and societal resilience.
“I love doing interdisciplinary work, and CDS is structured in a way I haven’t seen anywhere else. Data science here is truly a connector—it’s in the DNA of this place, not just a buzzword,” she said. “My work sits at the intersection of AI, environmental, and climate science, and now I’ll be just a few doors away from computer scientists, statisticians, and climate experts. It feels like the home I was meant to find.”
“I love doing interdisciplinary work, and CDS is structured in a way I haven’t seen anywhere else. Data science here is truly a connector—it’s in the DNA of this place.”
Barnes’ overarching research goal is to responsibly harness AI to anticipate human–Earth system futures in support of a thriving society. By focusing on explainable AI, she seeks to gauge trust in machine learning models while uncovering new insights into the forces shaping our climate. Her research is especially timely as extreme weather events intensify worldwide and the demand for reliable, actionable forecasts grows.
“Trust is key. Explainability helps us understand how an AI system makes decisions—so we can judge whether to trust its results. If we understand the decision process, we can also fix errors, and sometimes even discover new science,” she said. “If an AI model predicts something better than traditional methods, I want to know why—because that ‘why’ might reveal patterns or relationships we’ve overlooked.”
Asked how CDS’s environment will advance her work, Barnes added:
“I often say I’m parallelizing my work—there’s only one of me, and I can’t hold degrees in computer science, statistics, and climate science all at once. At CDS, I don’t have to. I can send my students down the hall to collaborate with experts in statistics or machine learning,” she said. “Already, one of my students is working with CDS faculty to develop methods that could advance both climate science and statistics at the same time. And it works both ways—my group can bring new datasets, ideas, and questions to others in CDS.”
“I often say I’m parallelizing my work—there’s only one of me, and I can’t hold degrees in computer science, statistics, and climate science all at once. At CDS, I don’t have to. I can send my students down the hall to collaborate with experts in statistics or machine learning.”
Barnes has been widely recognized for her research and teaching throughout her career. In January 2025, she received the Presidential Early Career Award for Scientists and Engineers (PECASE) from President Biden, one of the nation’s highest honors for early-career researchers. She is also a Fellow of both the American Geophysical Union (AGU) and the American Meteorological Society (AMS) and has received the AGU Macelwane Medal, the AGU Turco Lectureship, and the AMS Clarence Leroy Meisinger Award. Her dedication to advancing climate science has also been supported through an NSF CAREER Award and multiple honors from Colorado State University, where she was twice named Outstanding Professor of the Year by graduate students.
At CDS and BU’s Department of Earth & Environment, Barnes will be teaching courses that merge AI, data science, and climate prediction—such as Deep Learning for Weather and Climate Prediction—while leading research on AI explainability, extreme weather forecasting, and long-term climate modeling.
“This course will explore forecasting on timescales from days to decades—students might predict rainfall tomorrow and also drought risk 10 years from now. The same AI tools often apply across those timescales, which is exciting for both CDS and Earth & Environment students,” she said.
Barnes and her cohort of PhD students bring a wealth of expertise in climate science, AI, and predictive modeling, further strengthening CDS’s capacity to tackle urgent environmental challenges.
“They’re all passionate about both data science and Earth science, so they’ll fit naturally into the CDS community,” she added. “I expect them to collaborate widely, whether that’s sharing climate prediction expertise with colleagues or working with others to apply novel statistical and AI approaches to environmental data.
In this Q&A, Barnes shares why explainable AI is key to building trust and unlocking discoveries.
Q&A
Much of your work focuses on anticipating the next 30+ years of the human-coupled Earth system. What are the most pressing questions in this space right now?
The next few decades are critical for decisions about climate and society. Traditionally, climate science has focused on either short-term weather or long-term (century-scale) projections. But we also need reliable predictions for the next 1–30 years, when major policy and planning decisions will be made. One big challenge is integrating physical Earth system models with social and economic models—we haven’t really cracked that yet. AI could be the “glue” that finally helps merge those worlds.
Climate change, extreme weather, and predictability are front-page issues. How can data science and AI help address these urgent global challenges?
AI can improve forecasts, extend warning times for extreme events, and speed up expensive climate simulations. It can also help us analyze rare extreme events more effectively by generating realistic simulations. The faster we can run models and test scenarios, the more questions we can answer in a given year—and the more prepared we can be. With that said, we are still figuring out if these AI models are as good as we hope they will be! This requires in-depth knowledge of both how AI and the earth system work.
You’ve brought your research group and PhD students with you. How will their work fit into CDS, and what opportunities do you see for collaboration?
My research group of graduate students, postdocs, and research scientists moved with me. They’re all passionate about both data science and Earth science, so they’ll fit naturally into the CDS community. I expect them to collaborate widely, whether that’s sharing climate prediction expertise with colleagues or working with others to apply novel statistical and AI approaches to environmental data.
Beyond the bio—what’s something about you that might surprise people?
I have only one item on my bucket list: seeing a rocket launch in person. I’m even bringing a six-foot art piece of the Saturn V rocket to my office. To me, rocket launches and space exploration represent the best of humanity’s scientific advancements—people from many disciplines working together toward an extraordinary goal that could not be accomplished by any one team. That spirit of collaboration is exactly what excites me about CDS.
By Maureen McCarthy