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Publications

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  • All HBS Web  (668)
    • News  (145)
    • Research  (427)
    • Events  (20)
    • Multimedia  (12)
  • Faculty Publications  (309)

Show Results For

  • All HBS Web  (668)
    • News  (145)
    • Research  (427)
    • Events  (20)
    • Multimedia  (12)
  • Faculty Publications  (309)
← Page 10 of 668 Results →

    Jeremy Yang

    Jeremy Yang is an Assistant Professor of Business Administration in the Marketing Unit at Harvard Business School. He teaches Marketing in the MBA required curriculum. He develops data products for... View Details
    Keywords: advertising; media; entertainment; information; consumer products
    • 10 Jul 2024
    • Video

    Inequality in the Digital Age | An Interview with Kalinda Ukanwa from the University of Southern California

    • Forthcoming
    • Article

    Human-Algorithm Collaboration with Private Information: Naïve Advice Weighting Behavior and Mitigation

    By: Maya Balakrishnan, Kris Ferreira and Jordan Tong
    Even if algorithms make better predictions than humans on average, humans may sometimes have private information which an algorithm does not have access to that can improve performance. How can we help humans effectively use and adjust recommendations made by... View Details
    Keywords: AI and Machine Learning; Analytics and Data Science; Forecasting and Prediction; Digital Marketing
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    Balakrishnan, Maya, Kris Ferreira, and Jordan Tong. "Human-Algorithm Collaboration with Private Information: Naïve Advice Weighting Behavior and Mitigation." Management Science (forthcoming). (Pre-published online March 24, 2025.)
    • February 2011 (Revised February 2012)
    • Case

    Online Marketing at Big Skinny

    By: Benjamin Edelman and Scott Duke Kominers
    Describes a wallet maker's application of seven Internet marketing technologies: display ads, algorithmic search, sponsored search, social media, interactive content, online distributors, and A/B testing. Provides concise introductions to the key features of each... View Details
    Keywords: Advertising Campaigns; Digital Marketing; Resource Allocation; Marketing Strategy; Performance Evaluation; Internet and the Web; Retail Industry
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    Edelman, Benjamin, and Scott Duke Kominers. "Online Marketing at Big Skinny." Harvard Business School Case 911-033, February 2011. (Revised February 2012.) (request a courtesy copy.)
    • 29 Aug 2013
    • News

    Computer Scientists Figured Out How To Execute The Perfect Reddit Submission

      Warnings and Endorsements: Improving Human-AI Collaboration in the Presence of Outliers

      1. Problem definition: While artificial intelligence (AI) algorithms may perform well on data that are representative of the training set (inliers), they may err when extrapolating on non-representative data (outliers). How can humans and algorithms work together to... View Details
      • 17 Jan 2020
      • News

      Review: Competing in the Digital Age

      • 2021
      • Working Paper

      First Law of Motion: Influencer Video Advertising on TikTok

      By: Jeremy Yang, Juanjuan Zhang and Yuhan Zhang
      This paper engineers an intuitive feature that is predictive of the causal effect of influencer video advertising on product sales. We propose the concept of m-score, a summary statistic that captures the extent to which a product is advertised in the most engaging... View Details
      Keywords: Influencer Advertising; Video Advertising; Computer Vision; Machine Learning; Advertising; Online Technology
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      Yang, Jeremy, Juanjuan Zhang, and Yuhan Zhang. "First Law of Motion: Influencer Video Advertising on TikTok." Working Paper, March 2021.
      • September 2020 (Revised February 2024)
      • Teaching Note

      Artea (A), (B), (C), and (D): Designing Targeting Strategies

      By: Eva Ascarza and Ayelet Israeli
      Teaching Note for HBS No. 521-021,521-022,521-037,521-043. This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and... View Details
      Keywords: Targeted Advertising; Targeting; Race; Gender; Diversity; Marketing; Customer Relationship Management; Prejudice and Bias; Analytics and Data Science; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States
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      Ascarza, Eva, and Ayelet Israeli. "Artea (A), (B), (C), and (D): Designing Targeting Strategies." Harvard Business School Teaching Note 521-041, September 2020. (Revised February 2024.)
      • Article

      Vungle Inc. Improves Monetization Using Big-Data Analytics

      By: Bert De Reyck, Ioannis Fragkos, Yael Grushka-Cockayne, Casey Lichtendahl, Hammond Guerin and Andrew Kritzer
      The advent of big data has created opportunities for firms to customize their products and services to unprecedented levels of granularity. Using big data to personalize an offering in real time, however, remains a major challenge. In the mobile advertising industry,... View Details
      Keywords: Big Data; Monetization; Data and Data Sets; Advertising; Mobile Technology; Customization and Personalization; Performance Improvement
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      De Reyck, Bert, Ioannis Fragkos, Yael Grushka-Cockayne, Casey Lichtendahl, Hammond Guerin, and Andrew Kritzer. "Vungle Inc. Improves Monetization Using Big-Data Analytics." Interfaces 47, no. 5 (September–October 2017): 454–466.
      • Research Summary

      Overview

      By: Himabindu Lakkaraju
      I develop machine learning tools and techniques which enable human decision makers to make better decisions. More specifically, my research addresses the following fundamental questions pertaining to human and algorithmic decision-making:

      1. How to build... View Details
      Keywords: Artificial Intelligence; Machine Learning; Decision Analysis; Decision Support
      • 14 Nov 2016
      • News

      Why Big Data Isn’t Enough

      • 12 Oct 2021
      • News

      AI Can Bring More Equity To The Workplace And Consumer Markets By Implementing These Conditions

      • August 2021 (Revised November 2024)
      • Case

      Intenseye: Powering Workplace Health and Safety with AI (A)

      By: Michael W. Toffel and Youssef Abdel Aal
      Intenseye was a Turkey-based technology startup that deployed machine learning algorithms to workplace camera feeds in order to identify unsafe worker actions and unsafe working conditions, in order to help improve worker safety. The case describes how Intenseye’s... View Details
      Keywords: Privacy; Product Development; Operations; Technological Innovation; Value Creation; Production; Distribution; Safety; Risk and Uncertainty; Technology Industry; Manufacturing Industry; Distribution Industry; Turkey; Middle East; United States
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      Toffel, Michael W., and Youssef Abdel Aal. "Intenseye: Powering Workplace Health and Safety with AI (A)." Harvard Business School Case 622-037, August 2021. (Revised November 2024.)
      • Article

      Adaptive Machine Unlearning

      By: Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi and Chris Waites
      Data deletion algorithms aim to remove the influence of deleted data points from trained models at a cheaper computational cost than fully retraining those models. However, for sequences of deletions, most prior work in the non-convex setting gives valid guarantees... View Details
      Keywords: Machine Learning; AI and Machine Learning
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      Gupta, Varun, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Chris Waites. "Adaptive Machine Unlearning." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
      • 25 Aug 2018
      • News

      Are Superstar Firms and Amazon Effects Reshaping the Economy?

      • October 2019
      • Article

      Making Sense of Recommendations

      By: Michael Yeomans, Anuj Shah, Sendhil Mullainathan and Jon Kleinberg
      Computer algorithms are increasingly being used to predict people's preferences and make recommendations. Although people frequently encounter these algorithms because they are cheap to scale, we do not know how they compare to human judgment. Here, we compare computer... View Details
      Keywords: Recommender Systems; Artificial Intelligence; Interpretability; Information Technology; Forecasting and Prediction; Decision Making; Attitudes
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      Yeomans, Michael, Anuj Shah, Sendhil Mullainathan, and Jon Kleinberg. "Making Sense of Recommendations." Journal of Behavioral Decision Making 32, no. 4 (October 2019): 403–414.

        Edward McFowland III

        Edward McFowland III is an Assistant Professor in the Technology and Operations Management Unit at Harvard Business School. He teaches the first-year TOM course in the required curriculum.

        Professor McFowland’s research interests – which lie at the... View Details

        • 2020
        • Working Paper

        Machine Learning for Pattern Discovery in Management Research

        Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The patterns identified by ML could be used as an observation for further inductive or abductive research, but should not be treated as the result of a... View Details
        Keywords: Machine Learning; Theory Building; Induction; Decision Trees; Random Forests; K-nearest Neighbors; Neural Network; P-hacking; Analytics and Data Science; Analysis
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        Choudhury, Prithwiraj, Ryan Allen, and Michael G. Endres. "Machine Learning for Pattern Discovery in Management Research." Harvard Business School Working Paper, No. 19-032, September 2018. (Revised June 2020.)
        • Article

        Advancing Computational Biology and Bioinformatics Research Through Open Innovation Competitions

        By: Andrea Blasco, Michael G. Endres, Rinat A. Sergeev, Anup Jonchhe, Max Macaluso, Rajiv Narayan, Ted Natoli, Jin H. Paik, Bryan Briney, Chunlei Wu, Andrew I. Su, Aravind Subramanian and Karim R. Lakhani
        Open data science and algorithm development competitions offer a unique avenue for rapid discovery of better computational strategies. We highlight three examples in computational biology and bioinformatics research where the use of competitions has yielded significant... View Details
        Keywords: Computational Biology; Bioinformatics; Innovation Competitions; Research; Collaborative Innovation and Invention
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        Blasco, Andrea, Michael G. Endres, Rinat A. Sergeev, Anup Jonchhe, Max Macaluso, Rajiv Narayan, Ted Natoli, Jin H. Paik, Bryan Briney, Chunlei Wu, Andrew I. Su, Aravind Subramanian, and Karim R. Lakhani. "Advancing Computational Biology and Bioinformatics Research Through Open Innovation Competitions." PLoS ONE 14, no. 9 (September 2019).
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