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Bin Yu
Bin Yu
Bin Yu
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Bin Yu (Chinese: 郁彬) is a Chinese-American statistician. She is currently Chancellor's Professor in the Departments of Statistics and of Electrical Engineering & Computer Sciences at the University of California, Berkeley.[1][2]

Key Information

Biography

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Yu earned a bachelor's degree in mathematics in 1984 from Peking University, and went on to pursue graduate studies in statistics at Berkeley, earning a master's degree in 1987 and a Ph.D. in 1990. Her dissertation, Some Results on Empirical Processes and Stochastic Complexity, was jointly supervised by Lucien Le Cam and Terry Speed.[3]

After postdoctoral studies at the Mathematical Sciences Research Institute and an assistant professorship at the University of Wisconsin–Madison, she returned to Berkeley as a faculty member in 1993, was tenured in 1997, and became Chancellor's Professor in 2006. She also worked at Bell Labs from 1998 to 2000, while on leave from Berkeley, and has held visiting positions at several other universities. She chaired the Department of Statistics at Berkeley from 2009 to 2012, and was president of the Institute of Mathematical Statistics in 2014.[1][2][4] In 2023, she was awarded the COPSS Distinguished Achievement Award and Lectureship.

Research

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Yu's work spans many fields including statistics, machine learning, neuroscience, genomics, and remote sensing.[5] Her recent work has focused on data science, including frameworks for veridical data science[6][7] and interpretable machine learning.[8] Yu has received recent news coverage regarding investigations into the theoretical foundations of deep learning,[9] and work forecasting COVID-19 severity in the US.[10]

Other research topics include dictionary learning, non-negative matrix factorization (NMF), EM and deep learning (CNNs and LSTMs), and heterogeneous effect estimation in randomized experiments (X-learner).

Honors and awards

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Yu is a fellow of the Institute of Mathematical Statistics, the IEEE, the American Statistical Association, the American Association for the Advancement of Science, the American Academy of Arts and Sciences, and the National Academy of Sciences.[1][2][11][12][13] In 2012, she was the Tukey Lecturer of the Bernoulli Society for Mathematical Statistics and Probability.[1][2] In 2018, she was awarded the Elizabeth L. Scott Award. She was invited to give the Breiman lecture at NeurIPS 2019 (formally known as NIPS), on the topic of veridical data science.[14][15][16][17] In 2021, she was awarded an honorary doctorate by the University of Lausanne.[18] And in 2023, she received the COPSS distinguished achievement lecture.[19]

References

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Bin Yu

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Bin Yu is a Chinese-American statistician known for her pioneering contributions to statistical machine learning, interpretable artificial intelligence, and veridical data science through interdisciplinary applications in neuroscience, genomics, precision medicine, and beyond. She is the Chancellor's Distinguished Professor and Class of 1936 Second Chair in the Departments of Statistics and Electrical Engineering and Computer Sciences at the University of California, Berkeley, where she leads research on the theory, algorithms, and practice of statistical machine learning, causal inference, and high-dimensional data analysis. Born in China amid the Cultural Revolution, Yu faced early hardships in Harbin before discovering mathematics as a refuge and excelling under mentorship in secondary school. She earned her B.S. in mathematics from Peking University and her M.S. and Ph.D. in statistics from UC Berkeley, launching a career that included faculty positions at the University of Wisconsin-Madison and Yale University, technical work at Lucent Bell Labs, and leadership as chair of the UC Berkeley Statistics Department from 2009 to 2012. Yu's research has advanced key frameworks and methods, including the Predictability-Computability-Stability (PCS) approach to trustworthy data science, interpretable deep learning techniques such as iterative Random Forests and contextual decomposition, and tools for heterogeneous effect estimation in precision medicine. Her interdisciplinary collaborations have produced impactful results, such as predictive fMRI models enabling reconstruction of viewed movies from brain activity and analyses of spatial gene expression and treatment effects in genomics. She has been widely recognized for her influence, including election to the U.S. National Academy of Sciences and the American Academy of Arts and Sciences, a Guggenheim Fellowship, the COPSS Distinguished Achievement Award and Lecture, and service as president of the Institute of Mathematical Statistics. Yu's emphasis on cross-disciplinary teamwork and ethical, reliable data science has shaped modern approaches to machine learning and its applications.

Early life

Birth and family background

Bin Yu (Chinese: 郁彬) was born in Harbin, China, during the early years of the Cultural Revolution (around 1963, as she was three years old when it began in 1966). Her parents were intellectuals employed at an agricultural university in Harbin, which led to their persecution as "bad" elements. Her father was taken away by Red Guards, imprisoned, and died; she never saw him again. The family—including her mother, older sister, and devoted nanny—was sent to a farm near the Russian border (Xianglan) for re-education by farmers. After the Cultural Revolution began to wane, the family returned to Harbin, where her mother became a department chair at a university devoted to Chinese medicine. Yu later moved to Beijing in 1978. No exact birth date is publicly available.

Career

Bin Yu began her academic career after earning her Ph.D. in statistics from the University of California, Berkeley in 1990. She served as an Assistant Professor at the University of Wisconsin–Madison from 1990 to 1993. She joined the faculty at the University of California, Berkeley in 1993, received tenure in 1997, and took a leave from 1998 to 2000 to work as a Member of Technical Staff at Lucent Bell Labs. She also held a visiting faculty position at Yale University during her time at Wisconsin. Yu has been a professor at UC Berkeley since her return, becoming Chancellor's Professor in the Departments of Statistics and Electrical Engineering and Computer Sciences in 2006. She holds the Class of 1936 Second Chair in the College of Letters and Science. From 2009 to 2012, she served as Chair of the UC Berkeley Department of Statistics. She was President of the Institute of Mathematical Statistics from 2013 to 2014. She has also held roles including Distinguished Researcher in the Deep Learning Group at Microsoft Research and various visiting faculty positions at institutions such as MIT, ETH Zurich, and others.

Personal life

Bin Yu maintains a relatively private personal life, with limited publicly available details beyond her professional endeavors and early experiences. She was born in Harbin, China, amid the Cultural Revolution and faced early hardships there before discovering mathematics as a refuge and excelling under mentorship in secondary school. No verified information from reliable sources is available regarding other personal details such as current family status or hobbies. No filmography or acting credits are associated with Bin Yu, who is a statistician and academic researcher. This section does not apply.
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