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Chad Vidden

Professor
Mathematics & Statistics University of Wisconsin-La Crosse

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Chad Vidden

Professor

Mathematics & Statistics

Specialty area(s)

  • Intersections of mathematics and AI / big data
  • Machine learning, AI, and data science with applications in various fields of study
  • Applied mathematics, numerical analysis, and computational mathematics
  • Undergraduate research

Brief biography

I am currently a Professor of Mathematics at UWL where my interests include:

  • teaching undergraduate mathematics with special emphasis on applications,
  • collaborating with student researchers on data driven projects, 
  • the leading edge of machine learning and AI and mathematics involved,
  • and collaborating with local companies on research and student opportunities.

Education

Ph.D. Applied Mathematics, Iowa State University, 2013
B.S. Mathematics, Minnesota State University - Mankato, 2007

Career

Teaching history

MTH 150, 151, 207, 208, 308, 309, 310, 353, 371, 480, 498, STAT 145

Undergraduate Research Projects:

  • Numerical Methods for Differential Equations
  • Sports Ranking through Linear Algebra
  • Machine Learning
  • Exoplanet Detection through Fast Fourier Transform

Professional history

Professor of Mathematics, University of Wisconsin - La Crosse, July 2022 - current.

Associate Professor of Mathematics, University of Wisconsin - La Crosse, July 2018 - June 2022.

Assistant Professor of Mathematics, University of Wisconsin - La Crosse, August 2013 - June 2018.

Assistant Professor of Mathematics, University of Wisconsin - Platteville, August 2012 - May 2013.

Graduate Teaching Assistant, Iowa State University, August 2007 - May 2012.

Teaching Assistant, Minnesota State University - Mankato, August 2005 - May 2006.

Research and publishing

Textbook publication:

  1. From Data to Decision: A Handbook for the Modern Business Analyst by M. Vriens, S. Chen, and C. Vidden, Cognella Academic Publishing, December 2018. (second edition 2023)

Machine learning, data science, business, education:

  1. S. Floersch, C. Vidden, A. Jagim, et al. "Seasonal Changes in Match Demands and Workload Distribution in Collegiate Soccer Across Two Seasons". Journal of Strength and Conditioning Research, 2024.
  2. M. Vriens, N. Bosch, C. Vidden, and J. Talwar. "Prediction and profitability in market segmentation typing tools". Journal of Marketing Analytics, 2022.
  3. M. Vriens, C. Vidden, and N. Bosch. "The benefits of Shapley-value in key-driver analysis." Applied Marketing Analytics, 2021.
  4. M. Vriens, C. Vidden, and J. Schomaker. "What I see is what I want: Top-down attention biasing choice behavior," Journal of Business Research, 2020.
  5. M. Vriens and C. Vidden. "The Linux Compete strategy: An analytics case study." Applied Marketing Analytics, 2019.
  6. M. Vriens, S. Chen, and C. Vidden. "Mapping brand similarities: Comparing consumer online comments versus survey data." International Journal of Market Research, 2019.
  7. S. Chen, C. Vidden, N. Nelson, and M. Vriens. "Topic modelling for open-ended survey responses." Applied Marketing Analytics, 2018.
  8. M. Vriens, C. Vidden, S. Chen, and S. Kaulartz. "Assessing the impact of a brand crisis using big data: The case of the VW Diesel emission Crisis." DMA Annual Analytics Journal, 2017.
  9. C. Vidden, M. Vriens, and S. Chen. "Comparing clustering methods for market segmentation: A simulation study." Applied Marketing Analytics, 2016.

Numerical analysis, discontinuous Galerkin methods, applied mathematics:

  1. C. Vidden. "A new approach for admissibility analysis of the direct discontinuous Galerkin method through Hilbert matrices." Numer. Methods Partial Differential Equations, 2016.
  2. C. Vidden and J. Yan. "A new direct discontinuous Galerkin method with symmetric structure for nonlinear diffusion equations." Journal of Computational Mathematics, 2013.
  3. Invariant measures for hybrid stochastic systems (with X. Garcia, J. Kunze, T. Rudelius, A. Sanchez, S. Shao, E. Speranza). Involve, 2014.
  4. Trading cookies in a gambler's ruin scenario (with K. Jungjaturapit, T. Pluta, R. Rastegar, A. Roiterschtein, M. Temba, B. Wu). Involve, 2013.

Undergraduate research (mentored):

  1. S. Floersch. "Why the 2020 Dodgers are the Greatest Team of all Time, at Least Statistically." UWL Journal of Undergraduate Research, 2021.
  2. Q. Burzynski, L. Frank, Z. Nordstrom, J. Wolfe. "Profit Optimization." UWL Journal of Undergraduate Research, 2018. (joint with S. Chen)
  3. J. Meyers, D. Morrison. "Math Department Schedule Optimization." UWL Journal of Undergraduate Research, 2016. (joint with S. Chen)

Kudos

published

Marco Vriens, Marketing and Chad Vidden, Mathematics & Statistics, co-authored the article "The benefits of the Shapley Value for key drivers analysis" in Applied Marketing Analytics and was accepted for publication by Henry Stewart. Linear (and other types of) regression are often used in what is referred to as ‘driver modelling’ in customer satisfaction studies. The goal of such research is often to determine the relative importance of various sub-components of the product or service in terms of predicting and explaining overall satisfaction. Driver modelling can also be used to determine the drivers of value, likelihood to recommend, etc. A common problem is that the independent variables are correlated, making it difficult to get a good estimate of the importance of the ‘drivers’. This problem is well known under conditions of severe multicollinearity, and alternatives like the Shapley-value approach have been proposed to mitigate this issue. This paper shows that Shapley-value may even have benefits in conditions of mild collinearity. The study compares linear regression, random forests and gradient boosting with the Shapley-value approach to regression and shows that the results are more consistent with bivariate correlations. However, Shapley-value regression does result in a small decrease in k-fold validation results.

Submitted on: Jan. 11, 2021

published

Marco Vriens, Marketing and Chad Vidden, Mathematics & Statistics, co-authored the article "What I see is what I want: Top down attention biasing choice behavior" in Journal of Business Research published on Sept. 9, 2019 by Elsevier.

Submitted on: Jan. 22, 2020

published

Marco Vriens, Marketing and Chad Vidden, Mathematics & Statistics, co-authored the article "The Linux Compete strategy: An analytics case study" in the journal, Applied Marketing Analytics, Vol. 5 Number 2, Pages 129-136 published on Sept. 1, 2019 by Henry Steward Publications.

Submitted on: Jan. 22, 2020

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