E. Shin Oblander

Working Papers

• Dew, Ryan, Nicolas Padilla, Lan E-boy Luo, Shin Oblander, Asim Ansari, Khaled Boughanmi, Michael Braun, Fred M. Feinberg, Jia Liu, Thomas Otter, Longxiu Tian, Yixin Wang, and Mingzhang Yin (2024). "Probabilistic Machine Learning: New Frontiers for Modeling Consumers and Their Choices." Under review at International Journal of Research in Marketing.

  • SSRN pre-print

  • Code companion

• Oblander, Shin (2023). "Representation Learning for Behavioral Analysis of Complex Competitive Decisions." Working paper, draft available upon request.

Published and Forthcoming Papers

• Herzenstein, Michal, Sanjana Rosario, Shin Oblander, and Oded Netzer (2024). "The Language of (Non)replicable Social Science." Forthcoming at Psychological Science.

  • SSRN pre-print

  • Data and code

• Oblander, Shin and Daniel Minh McCarthy (2023). "Frontiers: Estimating the Long-Term Impact of Major Events on Consumption Patterns: Evidence from COVID-19." Marketing Science, 42(5), 839-852.

  • Published version

  • SSRN pre-print

  • Summary of substantive findings

  • Summary of methodology

  • Media coverage: The Wall Street Journal (twice), NPR/WABE All Things Considered (twice), New York Magazine, New York Post, Restaurant Business Magazine, The Economist

• McCarthy, Daniel Minh and Elliot Shin Oblander (2021). "Scalable Data Fusion with Selection Correction: An Application to Customer Base Analysis." Marketing Science, 40(3), 459-480. 

  • Published version

  • SSRN pre-print

  • Non-technical summary

  • Winner of 2019 Marketing Strategy Meets Wall Street Conference's Best Paper Award

• Oblander, Elliot Shin, Sunil Gupta, Carl F. Mela, Russell S. Winer, and Donald R. Lehmann (2020). "The Past, Present, and Future of Customer Management." Marketing Letters, 31(2), 125-136.

  • Published version

  • SSRN pre-print

• Oblander, Elliot, Sojung Carol Park, and Jean Lemaire (2016). "The Cost of High Suicide Rates in Japan and the Republic of Korea: Reduced Life Expectancies." Asia-Pacific Population Journal, 31(2), 21-44. 

  • Full text

  • Knowledge@Wharton coverage

• PhD Marketing (Quantitative track), Graduate School of Business, Columbia University (August 2018 - Present)

  • MPhil conferred February 2021

  • Advisors: Asim Ansari and Oded Netzer

  • Dissertation Committee: Olivier Toubia, David Blei, and Michael Woodford

  • Manager of CBS Quantitative Marketing Lab, member of Columbia Economics Cognition and Decision Lab

  • GPA 10.62/11.00

• BS Economics, The Wharton School of the University of Pennsylvania (August 2013 – May 2018)

  • Honors: Summa Cum Laude, Dean's Award for Excellence, Dean's List 2013-2018

  • Concentrations: Statistics and Actuarial Science

  • Minors: Mathematics and Japanese Studies

  • GPA 3.95/4.00

• Exchange Student, Faculty of Economics at Hitotsubashi University (March 2017 – August 2017)

  • GPA 4.30/4.30

Pedagogical Training

I have taken extensive training on evidence-based pedagogy and inclusive teaching practices through Columbia's Center for Teaching and Learning. I was the first Lead Teaching Fellow from the business school in 2020-2021 and became the first business school student to complete the Advanced track of the Teaching Development Program certification in 2023.

Instruction (Columbia Business School)

In 2020, I developed and taught a new course on probability theory and statistical estimation, called Statistical Modeling and Decision Making, as part of the core curriculum for the MS in Marketing Science program; I continued to teach this course in 2021 and 2022. In 2023, my course was recommended for approval by the CBS Curriculum and Instruction Committee to become a regularly offered course.

• Fall 2020, Fall 2021, Fall 2022: Statistical Modeling and Decision Making

  • Columbia Business School MS in Marketing Science course

  • Instructor rating: 4.7/5 (2020), 4.6/5 (2021), 4.4/5 (2022)

  • Some cherry-picked comments from past students:

    • "The passion and love you put into teaching and research are just inspirational, and you always find a way to make complex concepts accessible."

    • "[Shin] was helpful and always knew the answer to literally every question someone asked."

    • "Hands down one of the best stats teachers."

Teaching Assistantships (Columbia Business School)

• Fall 2021, Fall 2022: Analytics in Action, Professors Daniel Guetta and Brett Martin

  • MBA course

• Spring 2022: Customer Management, Professor Kinshuk Jerath

  • MS course

• Fall 2021: Applied Multivariate Statistics, Professor Kamel Jedidi

  • MS/PhD course

• Spring 2021: Pricing Strategies, Professor Asim Ansari

  • MS course

Teaching Assistantships (The Wharton School)

• Spring 2016, Summer 2016, Spring 2017, Summer 2017, Fall 2017, and Spring 2018: Applied Probability Models in Marketing, Professor Peter Fader

  • Spring 2016, Spring 2017, and Spring 2018: Undergraduate/MBA cross-listed course

  • Summer 2016, Summer 2017, and Fall 2017: Executive MBA course

• Fall 2015: Risk Management, Professor Gregory Nini

  • Undergraduate/MBA cross-listed course

• AMA-Sheth Foundation Doctoral Consortium Fellow, 2022.

• MSI Research Grant (for Estimating the Long-Term Impact of Major Events on Consumption Patterns: Evidence from COVID-19), 2021.

• Deming Center Doctoral Fellow, 2020-2021.

• Columbia Business School Amanda and Harold J Rudolph Fellow, 2020-2021.

• Columbia University Center for Teaching and Learning (CTL) Lead Teaching Fellow (LTF), 2020-2021.

• ISMS Doctoral Consortium Fellow, 2019.

• Marketing Strategy Meets Wall Street Conference's Best Paper Award (for Scalable Data Fusion with Selection Correction: An Application to Customer Base Analysis), 2019.

• Columbia University Provost Diversity Fellow, 2018.