The halls of UC Berkeley’s statistics department have long been a crucible for intellectual ferment, but on a cool morning in March 2024, something quieter, more intimate unfolded there. Bin Yu, one of the leading thinkers at the intersection of statistics, machine learning, and data science, stood calmly as we prepared to make her portrait. Her son, a Cal student himself, helped with the session—adjusting equipment, suggesting locations, moving with the same thoughtful precision that defines much of his mother’s work. It was a scene at once domestic and monumental: a scientist at the height of her powers, grounded in family, yet fully immersed in some of the most complex challenges of our time.

Bin Yu’s career has been marked not just by intellectual breadth, but by a relentless curiosity that refuses disciplinary boundaries. Trained as a statistician, she has spent decades bridging the mathematical with the empirical, developing tools that now underpin everything from neuroscience to climate modeling to AI transparency. Her early work helped shape the Lasso method and wavelet thresholding—now foundational in high-dimensional data analysis. But Yu is not one to rest on elegant theorems. She has always been pulled toward the real world, toward messy data and the ethical implications of how we use it.

Her philosophy—what she calls Veridical Data Science—is not just a technical framework but a moral one. It emphasizes transparency, reproducibility, and what she terms the “predictability-stability-interpretability” trinity. In an era where machine learning models often operate as inscrutable black boxes, Yu’s work aims to illuminate rather than obscure. She is a translator between worlds: pure theory and practical application, mathematics and meaning.

Students describe her as demanding but generous, a professor who insists on rigor but who also brings snacks to lab meetings. She is, in many ways, a rare kind of mentor—one who teaches with equations but also with empathy. Her research group spans disciplines and continents, and she often works at the interface of industry and academia, collaborating with teams at Microsoft, Siemens, and various biomedical institutions.

Born in China during the Cultural Revolution, Yu experienced firsthand the power of education to transform lives. Her path took her from Peking University to UC Berkeley, where she earned her PhD and where she now serves as Chancellor’s Professor in both the Departments of Statistics and Electrical Engineering & Computer Sciences. The arc of her journey—from growing up in a society where intellectualism was suspect, to becoming a globally respected scientist shaping how we think about intelligence itself—is nothing short of remarkable.

And yet, in person, Bin Yu is unassuming. She speaks softly, listens intently, and has the kind of laugh that surprises you—a sudden break in her analytic focus, revealing a delight in the human side of things. During our photo session, she chatted about her students, her family, the joys and frustrations of interdisciplinary work. At one point, she gently teased her son about his posture, and he rolled his eyes in that way only college kids can. It was a small, lovely moment. A world-class scientist and a mom, wrapped into one.

Yu’s impact on science is already indelible, but she gives the sense that her most important work may still lie ahead. In a data-saturated world racing toward ever more complex models, we will need voices like hers—clear, thoughtful, unflinching—to guide us. Not just toward better answers, but toward better questions.

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October 7, 2022