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James Scott - The University of Texas at Austin, McCombs School of Business. Austin, TX, US

James Scott

Associate Professor, Department of Information, Risk, and Operations Management | The University of Texas at Austin, McCombs School of Business


Improving statistical analysis and enabling new data discoveries benefiting society


Areas of Expertise (15)

Artifical Intelligence Decision Analysis AI Algorithms Consumer Behavior Data Analytics in Science and Medicine Bayesian Methods Decision Theory Probability and Statistics Statistical Analysis Social Marketing Data Analytics Big Data Data Security Machine learning and Artificial Intelligence for Autonomous Systems Data Analysis and Data Mining


James Scott, co-author of AIQ: How People and Machines Are Smarter Together, is a statistician and data scientist looking for better ways to solve problems that have frustrated industry professionals and researchers. His research interests include modern computational methods, Artificial Intelligence, and in Bayesian inference, including recent work on data-augmentation schemes for Bayesian computation; scalable algorithms; multiple testing and high-dimensional screening problems; prior choice in hierarchical models; and Bayesian methods in machine learning.

Scott is an associate professor in the department of information, risk, and operations management at the McCombs School of Business, The University of Texas at Austin. In 2013 he won the National Science Foundation CAREER award for his project, “Bringing Richly Structured Bayesian Models into the Discrete-Data Realm via New Data-Augmentation Theory and Algorithms."

"My goal is not to solve the marketing problem, or the finance problem, or the sentiment analysis problem," he says. "My goal is to look at all of those problems and see a common mathematical structure, some common principle that ties together a whole range of data sets and questions."

Scott received the Savage Award in 2010 for his dissertation on Bayesian statistics, “Bayesian Adjustment for Multiplicity.” The International Society for Bayesian Analysis presents the award annually to only two outstanding doctoral dissertations in the world.

His recent collaborative projects have involved applications in healthcare, security, and neuroscience. He has also done work in linguistics, political science, infectious disease, astronomy, and molecular biology.



James Scott Publication



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Education (3)

Duke University: Ph.D., Statistics 2009

University of Cambridge: M.A.St., Mathematics 2005

The University of Texas at Austin: B.Sc., Mathematics and Plan II Honors 2004

Media Appearances (6)

Sweden actually protects its residents' data. America should take note.

Fortune  online


The ideas behind AI may be surrounded by a force field of technical jargon, but they’re surprisingly simple. How does AI work? Why does it depend so strongly on data? When and where does it go wrong? I promise you the answers are within your reach—and if you care about the world, few questions are more urgent today.

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According to science, this is the most dangerous time to give birth

Yahoo Style UK  online


Speaking about the findings, Dr James Scott, an associate professor of statistics told The Sun: “There are all sorts of studies about the timing of deliveries, but what nobody had looked at before is whether there is some kind of proxy for how fatigued the doctors are.”

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LIFE OR DEATH This is the most dangerous time to give birth for mum AND baby, experts warn

The Sun  online


Dr James Scott, an associate professor of statistics, said: “There are all sorts of studies about the timing of deliveries, but what nobody had looked at before is whether there is some kind of proxy for how fatigued the doctors are.

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Birth risks rise at this point in a doctor’s day

Futurity  online


James Scott, an associate professor of statistics at the McCombs School of Business at the University of Texas at Austin, says that he and his fellow researchers hypothesized that the total number of hours worked during a shift may be a more important predicator of adverse outcomes than whether the delivery occurred during the weekend or in the middle of the night.

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Latin American women report Zika virus alerts as reason for seeking abortion

"News Medical - Life Sciences & Medicine "  online


In November 2015, the Pan American Health Organization (PAHO) issued an epidemiological alert highlighting the risks of Zika. As more governments and health organizations began to respond, the researchers -- which also included co-authors James Scott from the University of Texas at Austin, Rebecca Gomperts and Marc Worrell from Women on Web, and Catherine Aiken from the University of Cambridge -- became interested in investigating the effects these health alerts had on women.

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An Asset-Pricing Model for the Contagion Age: Polson and Scott

BloombergBusiness  online


The financial crisis and the meltdown in Europe have exposed the deficiencies of traditional asset-pricing models, particularly their inability to account for the effect of contagion from one market to another.

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Articles (6)

James G. Scott Citations Google Scholar


Listing of top scholarly works by James G. Scott.

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Management of Fetal Malposition in the Second Stage of Labor: a Propensity Score Analysis American Journal of Obstetrics and Gynecology


Seeking to determine the factors associated with selection of rotational instrumental vs cesarean delivery to manage persistent fetal malposition, and to assess differences in adverse neonatal and maternal outcomes following delivery by rotational instruments vs cesarean delivery.

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Nonparametric Bayesian Testing for Monotonicity Biometrika


This paper studies the problem of testing whether a function is monotone from a nonparametric Bayesian perspective. Two new families of tests are constructed.

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The Bayesian Bridge Journal of the Royal Statistical Society


We propose the Bayesian bridge estimator for regularized regression and classification.

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Bayes and Empirical-Bayes Multiplicity Adjustment in the Variable-Selection Problem The Annals of Statistics


This paper studies the multiplicity-correction effect of standard Bayesian variable-selection priors in linear regression.

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Feature-Inclusion Stochastic Search for Gaussian Graphical Models Journal of Computational and Graphical Statistics


We describe a serial algorithm called feature-inclusion stochastic search, or FINCS, that uses online estimates of edge-inclusion probabilities to guide Bayesian model determination in Gaussian graphical models.

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