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

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Diversity, Equity and Inclusion

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.

PSTAT Department Calendar

May 2024

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Improving understanding of life on earth through novel data and statistics
05/01/2024 - 15:30 to 16:45
 
 
Simulation-based Differentially Private Inference for Categorical Data
05/03/2024 - 15:30 to 16:30
 
 
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Transformer-Based Approaches for Time Series Forecasting
05/08/2024 - 15:30 to 16:45
 
 
 
 
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Stochastic Two-Point Boundary Value Problems and Application in Kyle-Back Model
05/29/2024 - 15:30 to 16:45
 
 
 
 

Announcements

Designing California's clean and climate resilient electricity grid for vulnerable communities

Professor Mike Ludkovski was awarded with California Climate Action Seed and...

Upcoming Seminars

Improving understanding of life on earth through novel data and statistics
Professor David Dunson. Professor Dunson is the Arts and Sciences Distinguished Professor of Statistical Science, Mathematics and Electrical & Computer Engineering at Duke University. His research focuses on developing statistical methods for complex and high-dimensional data. Particular themes of his work include the use of Bayesian hierarchical models, methods for learning latent structure in complex data, and the development of computationally efficient algorithms for uncertainty quantification.
 
  1. May 1, 2024 - 3:30pm to 4:45pm
  1. Annual Sobel Lecture
Simulation-based Differentially Private Inference for Categorical Data

Roberto Molinari is an Assistant Professor in the Department of Mathematics and Statistics at Auburn University. After an initial career in international political advisory, he obtained a PhD in Statistics at the University of Geneva (Switzerland) and successively had experience both as a statistical consultant for government and industry as well as a visiting professor at UCSB and Penn State University.

  1. May 3, 2024 - 3:30pm to 4:30pm
  1. Seminar
Transformer-Based Approaches for Time Series Forecasting

Dr. Thu Nguyen is an Assistant Professor in the Department of Math and Statistics at the University of Maryland, Baltimore County (UMBC). She obtained her PhD degree in Statistical Signal Processing from The Lille 1 University of Science and Technology in 2014, and her PhD degree in Applied Mathematics from Wayne State University in 2020.  Dr. Nguyen's research interests lie at the intersection of stochastic approximation, Monte Carlo methods, stochastic systems, applied mathematics, and their applications. Her current research focuses on designing and analyzing new stochastic approximation algorithms for stochastic networked systems, as well as developing and applying efficient Monte Carlo samplers for complex statistical problems. She is also interested in applying these techniques to problems in statistical learning, artificial intelligence, and machine learning. Since joining UMBC in 2020, Dr. Nguyen has expanded her research interests in the fields of machine learning and deep learning. Her research in this area revolves around the development of a novel class of deep learning-based models tailored to diverse time series modeling tasks.

  1. May 8, 2024 - 3:30pm to 4:45pm
  1. Department Seminar