About

Our department aims to be a diverse community engaged in areas of education and research in Statistical Theory and Methods, Data Science, Actuarial Science, Financial Mathematics, and Applied Probability; our research collaborations represent a wide range of interdisciplinary fields including environmental science, computer science, and biomedical science. We are home to the UCSB Center for Financial Mathematics and Actuarial Research, an interdisciplinary research center providing leadership in quantitative finance. We also provide consulting services through our Data Science Consulting Laboratory: DataLab

At its core, our department views diversity and inclusion as critical within our mission to educate and prepare the future workforce of data scientists and quantitative thinkers. Increasing diversity across our field is essential in creating more productive, representative, and enriching outcomes as well as innovative solutions to critical problems. We recognize that historically, the job markets and academic communities in statistical theory and methods, financial mathematics, and actuarial science have been weighted toward racial, gender, and socioeconomically privileged communities. We are dedicated to correcting this imbalance in our own community.

Announcements

  • Image of Big Data book that was translated into Japanese

Big Data book has been published in Japanese. (Already in English, Spanish and Slovenian)

  • Picture of Wind Turbines

The UCSB Current recently ran an article highlighting Prof. Mike Ludkovski's research on...

  • National Science Foundation Logo

PSTAT Assistant Professor Mengyang Gu was awarded a new grant from the National Science...

Upcoming Events

  1. May 21, 2021 - 12:45pm to 4:30pm

UCSB Actuary Day, held each year in the Spring, raises awareness of actuarial profession on campus through presentations by practicing actuaries, including our own Alumni.

May 2021

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05/05/2021 - 15:30 to 16:30
 
 
 
 
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05/12/2021 - 15:30 to 16:30
 
 
 
 
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05/19/2021 - 15:30
 
 
05/21/2021 - 12:45 to 16:30
 
 
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05/26/2021 - 15:30 to 16:30
 
 
 
 
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Upcoming Seminars

  • A picture of Professor Rebecca Willett

Machine Learning and Inverse Problems: Deeper and More Robust

  1. May 19, 2021 - 3:30pm
  1. Annual Sobel Lecture
  • A picture of associate professor Simge Küçükyavuz

Mixed-Integer Convex Programming for Statistical Learning

  1. May 26, 2021 - 3:30pm to 4:30pm
  • A picture of Professor Paul Gustafson

Tales of Bayesian Inference from the Pandemic: Partial Progress via Partial Identification

  1. June 2, 2021 - 3:30pm to 4:30pm