Shane T. Jensen

• Desen Lin, Shane T. Jensen, Susan M Wachter (2023), The price effects of greening vacant lots: how neighborhood attributes matter, Real Estate Economics, 51 (), pp. 573-610.
• J. Cui, Shane T. Jensen, J. Macdonald (2022), The effects of vacant lot greening and the impact of land use and business presence on crime, Environment and Planning B: Urban Analytics and City Science, 49 (), pp. 1147-1158.
• J. Macdonald, Viet Nguyen, Shane T. Jensen, C.C. Branas (2022), Reducing crime by remediating vacant lots: the moderating effect of nearby land uses, Journal of Experimental Criminology, 18 (), pp. 639-644.
• T. Tran, W. Tanner Porter, D.J. Salkald, M.A. Prusinki, Shane T. Jensen, Dustin Brisson (2021), Estimating disease vector population size from Citizen Science data, Journal of the Royal Society Interface, 18 (20210610).
• Colman Humphrey, Shane T. Jensen, Dylan Small, Rachel Thurston (2020), Analysis of urban vibrancy and safety in Philadelphia, Environment and Planning B: Urban Analytics and City Science, 47 (), pp. 1573-1587.
• Cecilia Balocchi and Shane T. Jensen (2019), Spatial modeling of trends in crime over time in Philadelphia, Annals of Applied Statistics, 13 (4), pp. 2235-2259.
• Jose Marcio Luna, Efstathios D. Gennatas, Lyle H. Ungar, Eric Eaton, Eric S. Diffenderfer, Shane T. Jensen, Charles B. Simone II, Jerome H. Friedman, Timothy D. Solberg, Gilmer Valdes (2019), Building more accurate decision trees with the additive tree, Proceedings of the National Academy of Sciences of the United States of America, 116 (40), pp. 19887-19893.
• Colman Humphrey, Dylan Small, Shane T. Jensen, Kevin Volpp, David A. Asch, Jingsan Zhu, Andrea B. Troxel (2019), Modeling Lottery Incentives for Daily Adherence, Statistics in Medicine, 38 (15), pp. 2847-2867.
• Namita Nandakumar and Shane T. Jensen (2019), Historical Perspectives and Current Directions in Hockey Analytics, Annual Review of Statistics and Its Applications, 6 (), pp. 19-36.
• Gilmer Valdes, Albert J. Chang, Yannet Interian, Kenton Owens, Shane T. Jensen, Lyle Ungar, Adam Cunha, Timothy D. Solberg, I-Chow Hsu (2018), Salvage HDR Brachytherapy: Multiple Hypothesis Testing Versus Machine Learning Analysis, International Journal of Radiation Oncology - Biology - Physics, 101 (3), pp. 694-703.
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Past Courses

• GCB7990 - Independent Study

  Independent study course

• GCB9950 - Dissertation

  Ph.D. students enroll in this course after passing their candidacy exam. They work on their dissertation full-time under the guidance of their dissertation supervisor and other members of their dissertation committee.

• STAT1020 - Intro Business Stat

  Continuation of STAT 1010 or STAT 1018. A thorough treatment of multiple regression, model selection, analysis of variance, linear logistic regression; introduction to time series. Business applications.

• STAT3990 - Independent Study

  Written permission of instructor and the department course coordinator required to enroll in this course.

• STAT4420 - Intro Bayes Data Analys

  The course will introduce data analysis from the Bayesian perspective to undergraduate students. We will cover important concepts in Bayesian probability modeling as well as estimation using both optimization and simulation-based strategies. Key topics covered in the course include hierarchical models, mixture models, hidden Markov models and Markov Chain Monte Carlo. A course in probability (STAT 4300 or equivalent); a course in statistical inference (STAT 1020, STAT 1120, STAT 4310 or equivalent); and experience with the statistical software R (at the level of STAT 4050 or STAT 4700) are recommended.

• STAT8990 - Independent Study

  Written permission of instructor, the department MBA advisor and course coordinator required to enroll.

• STAT9270 - Bayesian Statistics

  This graduate course will cover the modeling and computation required to perform advanced data analysis from the Bayesian perspective. We will cover fundamental topics in Bayesian probability modeling and implementation, including recent advances in both optimization and simulation-based estimation strategies. Key topics covered in the course include hierarchical and mixture models, Markov Chain Monte Carlo, hidden Markov and dynamic linear models, tree models, Gaussian processes and nonparametric Bayesian strategies.

• STAT9950 - Dissertation

  Dissertation

• JASA Reproducibility Award, 2023 Description

  American Statistical Association

• Wharton Undergraduate Teaching Excellence Award, 2021
• Wharton Teaching Excellence Award, 2020
• Wharton Teaching Excellence Award, 2019
• SABR Analytics Conference Research Award in Contemporary Baseball Analysis, 2016 Description

  for the paper “OpenWAR: an open source system for evaluating overall player performance in major league baseball.”

• Sports in Statistics Award for Contributions to the Statistics in Sports Community, American Statistical Association, 2011
• David W. Hauck Award for Excellence in Undergraduate Teaching, The Wharton School, 2009
• Leonard J. Savage Award for best thesis in Application Methodology from the International Society for Bayesian Analysis, 2005

Knowledge @ Wharton

• When AI Makes Financial Decisions, Knowledge @ Wharton - 10/13/2026
• What Does It Mean to Be ‘Happy’ at Work?, Knowledge @ Wharton - 10/13/2026
• How AI Planning Could Create Financial Market Fragility, Knowledge @ Wharton - 10/13/2026
• 2026 AI Adoption Report, Knowledge @ Wharton - 10/13/2026
• Who Controls a Company’s Values After It Is Sold?, Knowledge @ Wharton - 10/09/2026
• How Lucky Strike Entertainment Is Betting on Screen-Free Experiences, Knowledge @ Wharton - 10/08/2026
• Why People Would Rather Give Nothing Than Too Little, Knowledge @ Wharton - 10/08/2026
• The Data Behind College Football’s New Era, Knowledge @ Wharton - 10/07/2026
• AI Safety Has Reached a Critical Point, Knowledge @ Wharton - 10/07/2026
• AI’s Impact on Financial Services, Knowledge @ Wharton - 10/05/2026