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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)

  • Machine Learning, AI, and Data Science with Applications in Business and Education
  • Numerical Analysis and Computational Mathematics for Solving Partial Differential Equations

Brief biography

I recieved a PhD from Iowa State University in Applied Mathematics in 2012. Prior to that I recieved a bachelors degree from Minnesota State University - Mankato in Mathematics and Computer Science.

I am currently an Associate Professor of Mathematics at the UWL where my interests include:

  • teaching undergraduate mathematics with special emphasis on applications,
  • collaborating with student researchers on Machine Learning and Data Science projects, 
  • researching cutting machine learning techniques and applying ideas to business and education,
  • 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, 301, 353, 371, 480, 498

Independent Study Projects:

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

Professional history

Associate Professor of Mathematics, University of Wisconsin - La Crosse, August 2013 - current.
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

  • From Data to Decision: A Handbook for the Modern Business Analyst by M. Vriens, S. Chen, and C. Vidden, Cognella Academic Publishing, December 2018. 

Machine Learning, data science, business, education:

  • S. Floersch, A. Jagim, and C. Vidden. "Data Driven Training Optimization for Division III Women's Soccer" (in progress, in collaboration with UWL soccer)
  • M. Vriens, N. Bosch, C. Vidden, and J. Talwar. "Prediction and profitability in market segmentation typing tools". Journal of Marketing Analytics (2022).
  • S. Floersch and C. Vidden, "Why the 2020 Dodgers are the Greatest Team of all Time, at Least Statistically." UWL Journal of Undergraduate Research, XXIV, 2021.
  • M. Vriens, C. Vidden, and N. Bosch. "The benefits of Shapley-value in key-driver analysis." Applied Marketing Analytics 6.3 (2021): 269-278.
  • M. Vriens, C. Vidden, and J. Schomaker. "What I see is what I want: Top-down attention biasing choice behavior," Journal of Business Research, Elsevier, vol. 111(C) (2020): 262-269.
  • M. Vriens, and C. Vidden. "The Linux Compete strategy: An analytics case study." Applied Marketing Analytics 5.2 (2019): 129-136.
  • M. Vriens, S. Chen, and C. Vidden. "Mapping brand similarities: Comparing consumer online comments versus survey data." International Journal of Market Research 61.2 (2019): 130-139.
  • Q. Burzynski, L. Frank, Z. Nordstrom, J. Wolfe, S. Chen, and C. Vidden. "Profit Optimization." UWL Journal of Undergraduate Research, XXI, 2018.
  • S. Chen, C. Vidden, N. Nelson, and M. Vriens, "Topic modelling for open-ended survey responses." Applied Marketing Analytics 4.1 (2018): 53-62.
  • 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): 107-118.
  • C. Vidden, M. Vriens, and S. Chen. "Comparing clustering methods for market segmentation: A simulation study." Applied Marketing Analytics 2.3 (2016): 225-238.
  • J. Meyers, D. Morrison, S. Chen, and C. Vidden, "Math Department Schedule Optimization." UWL Journal of Undergraduate Research, XIX, 2016.

Numerical analysis, numerical methods for partial differential equations, finite element methods, discontinuous Galerkin methods, applied mathematics:

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

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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