Associate Professor Department of Mathematics and Statistics Undergraduate and Graduate Data Science Programs University of San Francisco
Email: jdwilson4 (at) usfca (dot) edu Twitter: @ThisIsJDWilson
Orcid: http://orcid.org/0000-0002-2354-935X
Google Scholar Page
Github Page
I am an Associate Professor of statistics and data science at the University of San Francisco in the Department of Mathematics and Statistics and the Undergraduate and Graduate Programs of Data Science. I received my Ph.D in the Department of Statistics and Operations Research under the advisement of Professors Shankar Bhamidi and Andrew Nobel at the University of North Carolina at Chapel Hill.
I have a broad background and interests in statistics, biostatistics, and data science with expertise in the modeling and analysis of complex network and brain imaging data. I am driven by the data - I seek to use my strengths in mathematical and computational statistics, network analysis, random graph theory, and statistical machine learning to make sense of patterns from brain imaging studies and social media, while furthermore demystifying complex models like contemporary deep learning models. My research has been supported by the National Science Foundation and the National Institutes of Health. See my Research Overview for more details of my current work or my relatively up-to-date CV.
I also work to ensure that individuals and businesses use data-driven analyses in their everyday decision making. I co-founded StatZhub Consulting to help make statistical and analytical thinking accessible and personalized to fit anyone’s needs. See my consulting website to learn more.
upcoming Talks
The AI and Data Science Symposium, Institute for Artificial Intelligence and Data Science at the University of Buffalo. Buffalo, NY. March, 2025.
I will speak at, and am helping organize the Quality and Productivity Research Conference (QPRC), University of Washington, WA. June, 2025.
World Statistics Conference (WSC), The Hague, Netherlands. October, 2025.
Videos of Past Presentations
<Community Detection in Multilayer Networks with Heterogeneous Community Structure>, Network Science Institute at Northeastern University. February, 2018 (1 hour)
<Identifying Interpretable Functional Characteristics of the Brain from Resting State fMRI using multi-node2vec>, Data Science Meetup at the University of San Francisco. March, 2019 (1 hour)
<Statistical Models for Integrating Functional Connectivity with sMRI and PET Brain Imaging Data>, Department of Statistics, University of Victoria. November, 2021 (1 hour)
<Interpretable Network Representation Learning with Principal Component Analysis>, Department of Statistics, University of California at Santa Cruz. April, 2022 (1 hour)
<Liberals, Conservatives, and the Political Brain: fMRI studies of Political Ideology>. Center for Neuropolitics Lecture Series, University of California at Irvine. January, 2023 (1.5 hours; with Skyler Cranmer, Zhong-Lin Lu, and Seo Eun Yang.