An internationally respected University of California, Berkeley, statistician and National Academy of Sciences member renowned for her work on statistical inference, machine learning and interdisciplinary research will visit the Texas A&M University campus next week as the guest speaker for the biennial Emanuel Parzen Prize Lecture Series, sponsored by the Texas A&M Department of Statistics.
Bin Yu, Chancellor’s Professor in the Departments of Statistics and Electrical Engineering & Computer Sciences at Berkeley, will present an 11:10 a.m. public lecture, “Iterative Random Forests,” Thursday (Sept. 5) in the Stephen W. Hawking Auditorium within the George P. and Cynthia Woods Mitchell Institute for Fundamental Physics and Astronomy as recipient of the 2018 Emanuel and Carol Parzen Prize for Statistical Innovation. The presentation is free and open to the public.
The Parzen Prize is named for Emanuel Parzen, longtime distinguished professor of statistics at Texas A&M who passed away February 6, 2016. Established through the Texas A&M Foundation and first awarded in 1994 to recognize Parzen’s 65th birthday, it is presented in even-numbered years to North American statisticians in recognition of outstanding careers in the discipline and profession of statistics. In addition to an invitation to deliver the Parzen Prize Lecture, recipients receive a citation and a $1,000 honorarium plus travel expenses.
Yu is cited “for innovative, influential and outstanding research in algorithm and theory of statistical machine learning and casual inference.”
The day after she delivers the Parzen Prize Lecture, Yu will serve as one of four keynote speakers for the 2019 Conference on Advances in Data Science: Theory, Methods and Computation, set for Friday (Sept. 6) in Hawking Auditorium. The event is sponsored by the Texas A&M Institute for Applied Mathematics and Computational Science and Texas A&M Institute of Data Science.
An elected fellow of both the NAS (2014) and American Academy of the Arts and Sciences (2013), Yu is widely respected for her work in empirical processes for dependent data, information theory, signal processing, high dimensional statistical inference, boosting, sparse modeling, computational neuroscience and remote sensing. Her research group at Berkeley is interested in extracting meaningful and useful information from wide-ranging data sources, including neuroscience, systems biology, remote sensing and signal processing. They are renowned for their interdisciplinary work with scientists from genomics, neuroscience and precision medicine.
In order to augment empirical evidence for decision-making, Yu and her group investigate methods, algorithms and associated statistical inference problems, including dictionary learning, nonnegative matrix factorization (NMF), EM and deep learning (CNNs and LSTMs) and heterogeneous effect estimation in randomized experiments (X-learner). Their recent algorithms include staNMF for unsupervised learning, iterative Random Forests (iRF) and signed iRF (s-iRF) for discovering predictive and stable high-order interactions in supervised learning, contextual decomposition (CD) and aggregated contextual decomposition (ACD) for phrase or patch importance extraction from an LSTM or a CNN. Yu has published more than 150 refereed articles in top-tier statistical, engineering, computational and other journals.
Born in Harbin, China, Yu received a bachelor of science in mathematics from Peking University and earned both master’s and Ph.D. degrees in statistics from UC Berkeley in 1987 and 1990, respectively. After beginning her independent academic career in 1990 an assistant professor of statistics at the University of Wisconsin-Madison, Yu returned in 1993 to Berkeley Statistics, where she was chair from 2009 to 2012. She was a member of Technical Staff at Bell Labs-Lucent from 1998 to 2000 while on leave from Berkeley, as well as a Guggenheim Fellow and a past president of the Institute of Mathematical Statistics (IMS) from 2013 to 2014.
Yu is an elected fellow of the American Statistical Association, the IMS, the Institute of Electrical and Electronics Engineers and the American Association for the Advancement of Science. She is a founding co-director of the Microsoft Research Asia (MSR) Lab at Peking University as well as a member of the scientific advisory board at the Alan Turning Institute in the United Kingdom. Her many awards include the Committee of Presidents of Statistical Societies’ E.L. Scott Award (2018), the IMS’ Rietz Lectureship (2016) and the Bernoulli Society’s Tukey Memorial Lectureship (2012).
Emanuel Parzen, who joined the Texas A&M Statistics faculty in 1978, retired in 2009 as distinguished professor emeritus of statistics but remained active in his research. In 1994, he was awarded the Samuel S. Wilks Memorial Medal of the American Statistical Association “for outstanding research in time-series analysis, especially for his innovative introduction of reproducing kernel spaces, spectral analysis and spectrum smoothing; for pioneering contributions in quantile and density quantile functions and estimation; for unusually successful and influential textbooks in probability and stochastic processes; for excellent and enthusiastic teaching and dissemination of statistical knowledge; and for a commitment to service on society councils, government advisory committees and editorial boards.” In 2005, Parzen received the Gottfried E. Noether Award “for a lifetime of outstanding achievements and contributions in the field of nonparametric statistics, both in research and teaching.”
Carol Parzen, who earned her master’s of science in adult extension education from Texas A&M in 1981, has had diverse careers in the community. She retired from the Texas A&M Mays College of Business Administration and Graduate School of Business, where she served as assistant director of the CBA Fellows Program. The Parzens were married in 1959 and have two children — Sara Schandelson, a librarian who resides in Boca Raton, Fla., and Michael Parzen, a senior lecturer of statistics in the Harvard University Department of Statistics — and six grandchildren.
For more information regarding the Parzen Prize Lecture Series, please contact the Department of Statistics at (979) 845-3141 or view event information online.
Contact: Shana K. Hutchins, (979) 862-1237 or firstname.lastname@example.org