Daniela Witten | |
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Alma mater | Stanford University (BS, PhD) |
Known for | An Introduction to Statistical Learning [1] |
Awards |
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Scientific career | |
Fields | |
Institutions | University of Washington |
Thesis | A penalized matrix decomposition, and its applications (2010) |
Doctoral advisor | Robert Tibshirani [3] |
Website | faculty |
Daniela M. Witten is an American biostatistician. She is a professor and the Dorothy Gilford Endowed Chair of Mathematical Statistics at the University of Washington. [4] [5] Her research investigates the use of machine learning to understand high-dimensional data. [2]
Witten studied mathematics and biology at Stanford University, graduating in 2005. She remained there for her postgraduate research, earning a master's degree in statistics in 2006. [6] [7] She was awarded the American Statistical Association Gertrude Mary Cox Scholarship in 2008. [8] Her doctoral thesis, A penalized matrix decomposition, and its applications was supervised by Robert Tibshirani. [3] [9] [10] She worked with Trevor Hastie on canonical correlation analysis. [11] She co-authored An Introduction to Statistical Learning in 2013. [1]
Witten applies statistical machine learning to personalised medical treatments and decoding the genome. [12] She uses machine learning to analyse data sets in neuroscience and genomics. [13] She is worried about increasing amounts of data in biomedical sciences. [14]
She was appointed to the University of Washington as Genentech Endowed Professor in 2010. [15] Witten contributed to the 2012 report Evolution of Translational Omics, which provided best practise in translating omics research into a clinic. [16] [17]
She is an associate editor for the Journal of the American Statistical Association . [18]
She was elected as a Fellow of the American Statistical Association in 2020. [19] She was named to the 2022 class of Fellows of the Institute of Mathematical Statistics, for "substantial contributions to the field of statistical machine learning, with applications to biology; and for communicating the fundamental ideas in the field to a broad audience". [20]
She was awarded an NIH Director's Early Independence Award in 2011. [21] She was awarded the American Statistical Association David P. Byar Young Investigator Award for her work Penalized Classification Using Fisher’s Linear Discriminant in 2011. [22] Her book An Introduction to Statistical Learning won a Technometrics Ziegel Award in 2014. [23] She won an Elle magazine Genius Award in 2012. [24] In 2013 she won an Alfred P. Sloan Foundation Fellowship. [25] She was named in the Forbes 30 Under 30 Science & Healthcare category in 2012, 2013 and 2014. [26] [27] [28] In 2015 Witten was awarded the Texas A&M University Raymond J. Carroll Young Investigator Award. [29] In 2018, she was named a Simons Foundation Investigator, [30] and in 2022, she received the COPSS Presidents' Award.
Daniela is the younger sister of Ilana B. Witten, the older sister of Rafael Witten, and the daughter of the physicists Chiara Nappi and Edward Witten. [31] She is married to software engineer Ari Steinberg. [32] [33]
Bradley Efron is an American statistician. Efron has been president of the American Statistical Association (2004) and of the Institute of Mathematical Statistics (1987–1988). He is a past editor of the Journal of the American Statistical Association, and he is the founding editor of the Annals of Applied Statistics. Efron is also the recipient of many awards.
The COPSS Presidents' Award is given annually by the Committee of Presidents of Statistical Societies to a young statistician in recognition of outstanding contributions to the profession of statistics. The COPSS Presidents' Award is generally regarded as one of the highest honours in the field of statistics, along with the International Prize in Statistics.
Robert Tibshirani is a professor in the Departments of Statistics and Biomedical Data Science at Stanford University. He was a professor at the University of Toronto from 1985 to 1998. In his work, he develops statistical tools for the analysis of complex datasets, most recently in genomics and proteomics.
Xihong Lin is a Chinese–American statistician known for her contributions to mixed models, nonparametric and semiparametric regression, and statistical genetics and genomics. As of 2015, she is the Henry Pickering Walcott Professor and Chair of the Department of Biostatistics at Harvard T.H. Chan School of Public Health and Coordinating Director of the Program in Quantitative Genomics.
Trevor John Hastie is an American statistician and computer scientist. He is currently serving as the John A. Overdeck Professor of Mathematical Sciences and Professor of Statistics at Stanford University. Hastie is known for his contributions to applied statistics, especially in the field of machine learning, data mining, and bioinformatics. He has authored several popular books in statistical learning, including The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Hastie has been listed as an ISI Highly Cited Author in Mathematics by the ISI Web of Knowledge. He also contributed to the development of S.
Bin Yu 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.
In statistics, the graphical lasso is a sparsepenalized maximum likelihood estimator for the concentration or precision matrix of a multivariate elliptical distribution. The original variant was formulated to solve Dempster's covariance selection problem for the multivariate Gaussian distribution when observations were limited. Subsequently, the optimization algorithms to solve this problem were improved and extended to other types of estimators and distributions.
Out-of-bag (OOB) error, also called out-of-bag estimate, is a method of measuring the prediction error of random forests, boosted decision trees, and other machine learning models utilizing bootstrap aggregating (bagging). Bagging uses subsampling with replacement to create training samples for the model to learn from. OOB error is the mean prediction error on each training sample xi, using only the trees that did not have xi in their bootstrap sample.
Amanda L. Golbeck is a statistician, social scientist, and academic leader. She is known for her book, Leadership and Women in Statistics, and her book on Elizabeth L. Scott, Equivalence: Elizabeth L. Scott at Berkeley. She is known for her pioneering definition of health numeracy.
Jeffrey Tullis Leek is an American biostatistician and data scientist working as a Vice President, Chief Data Officer, and Professor at Fred Hutchinson Cancer Research Center. He is an author of the Simply Statistics blog, and runs several online courses through Coursera, as part of their Data Science Specialization. His most popular course is The Data Scientist's Toolbox, which he instructed along with Roger Peng and Brian Caffo. Leek is best known for his contributions to genomic data analysis and critical view of research and the accuracy of popular statistical methods.
Hui Zou is currently a professor of statistics at the University of Minnesota.
Helena Chmura Kraemer is an American professor emerita of biostatistics at Stanford University. She is a fellow of the American Statistical Association.
Sharon Xiangwen Xie is a Chinese biostatistician and epidemiologist who studies neurodegenerative diseases. She is a professor of biostatistics in the Department of Biostatistics, Epidemiology, and Informatics at the University of Pennsylvania.
Jiayang Sun is an American statistician whose research has included work on simultaneous confidence bands for multiple comparisons, selection bias, mixture models, Gaussian random fields, machine learning, big data, statistical computing, graphics, and applications in biostatistics, biomedical research, software bug tracking, astronomy, and intellectual property law. She is a statistics professor, Bernard J. Dunn Eminent Scholar, and chair of the statistics department at George Mason University, and a former president of the Caucus for Women in Statistics.
Regina Nuzzo is a professor of statistics at Gallaudet University in Washington D.C., a liberal arts school for deaf and hard-of-hearing students. She also writes articles about the importance of statistical and science communication and is an advocate for people with disabilities in the science and technology field.
Andreas Buja is a Swiss statistician and professor of statistics. He is the Liem Sioe Liong/First Pacific Company professor in the Statistics department of The Wharton School at the University of Pennsylvania in Philadelphia, United States. Buja joined Center for Computational Mathematics (CCM) as a Senior Research Scientist in January 2020.
Ilana B. Witten is an American neuroscientist and professor of psychology and neuroscience at Princeton University. Witten studies the mesolimbic pathway, with a focus on the striatal neural circuit mechanisms driving reward learning and decision making.
Fredrick DuBois Bowman is an American statistician who is the Dean of the School of Public Health at the University of Michigan. His research applies statistical analysis to brain imaging to better understand Alzheimer's disease, schizophrenia and Parkinson's disease. Bowman is a member of the National Academy of Medicine, Fellow of the American Association for the Advancement of Science, and Fellow of the American Statistical Association.
Sherri Rose is an American biostatistician. She is an associate professor of health care policy at Stanford University, and once worked at Harvard University. A fellow of the American Statistical Association, she has served as co-editor of Biostatistics since 2019 and Chair of the American Statistical Association’s Biometrics Section. Her research focuses on statistical machine learning for health care policy.
Clarice Ring Weinberg is an American biostatistician and epidemiologist who works for the National Institute of Environmental Health Sciences as principal investigator in the Biostatistics and Computational Biology Branch. Her research concerns environmental epidemiology, and its combination with genetics in susceptibility to disease, including running the Sister Study on how environmental and genetic effects can lead to breast cancer. She has also published highly cited research on fertility.