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

November 2023

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Graph Laplacian Learning for Exponential Family Noise and Product Graphs
11/01/2023 - 15:30 to 16:45
 
 
 
 
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Statistical foundations of deep generative models
11/08/2023 - 15:30 to 16:30
 
 
 
 
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Assessing Portfolio Diversification via Two-Sample Graph Kernel Inference
11/15/2023 - 15:30 to 16:45
 
 
 
 
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ISM-UCL-UCSB-MQ WORKSHOP: Stochastic Modelling in Climate Risk: Financial Mathematics and Economics 11/21/2023 to 11/23/2023
 
 
 
 
 
 
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Parameter Restrictions for the Sake of Identification: Is There Utility in Asserting That Perhaps a Restriction Holds?
11/29/2023 - 15:30 to 16:45
 
 
 
 

Upcoming Seminars

Approximate Bayesian Computations to fit and compare insurance loss models

Patrick Laub is a lecturer at the UNSW School of Risk and Actuarial Studies in Sydney. Patrick's research focuses on computationally challenging problems in actuarial data science. His teaching covers artificial intelligence & machine learning courses, with a focus on risk and insurance applications. He holds a PhD in computational applied probability and degrees in software engineering and mathematics.

  1. December 6, 2023
  1. Seminar