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- Faculty Publications (289)
Show Results For
-
All HBS Web
(650)
- News (146)
- Research (402)
- Events (13)
- Multimedia (10)
- Faculty Publications (289)
- Research Summary
Overview
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
1. How to build... View Details
- 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...
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- 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...
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Keywords:
Recommender Systems;
Artificial Intelligence;
Interpretability;
Information Technology;
Forecasting and Prediction;
Decision Making;
Attitudes
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.
- 25 Aug 2018
- News
Are Superstar Firms and Amazon Effects Reshaping the Economy?
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
- 2019
- Article
More Amazon Effects: Online Competition and Pricing Behaviors
By: Alberto Cavallo
I study how online competition, with its shrinking margins, algorithmic pricing technologies, and the transparency of the web, can change the pricing behavior of large retailers in the U.S. and affect aggregate inflation dynamics. In particular, I show that in the past...
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Keywords:
Amazon;
Online Prices;
Inflation;
Uniform Pricing;
Price Stickiness;
Monetary Economics;
Economics;
Macroeconomics;
Inflation and Deflation;
System Shocks;
United States
Cavallo, Alberto. "More Amazon Effects: Online Competition and Pricing Behaviors." Jackson Hole Economic Symposium Conference Proceedings (Federal Reserve Bank of Kansas City) (2019).
- 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...
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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).
- August 2021 (Revised April 2022)
- Case
Intenseye: Powering Workplace Health and Safety with AI
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...
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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
Toffel, Michael W., and Youssef Abdel Aal. "Intenseye: Powering Workplace Health and Safety with AI." Harvard Business School Case 622-037, August 2021. (Revised April 2022.)
- 25 Sep 2015
- Blog Post
4 Challenges All Early-Stage Startups Face
During our first year at HBS, my classmates and I took the opportunity to build cleverlayover, a flight search engine that uses advanced algorithms to find flights hundreds of dollars cheaper than any other search engine. We were able to...
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- 14 Nov 2016
- News
Why Big Data Isn’t Enough
- February 2021 (Revised March 2022)
- Case
Marvin: A Personalized Telehealth Approach to Mental Health
By: Regina E. Herzlinger, Eshani Sharma, Andrew Nguyen, Thomas Arsenault, Carin-Isabel Knoop and Julia Kelley
More than one third of Americans were said to suffer some type of behavioral health ailment at some point in their lifetime, with many people requiring chronic therapy or intervention. Despite significant clinical needs, access to reliable treatment has been difficult...
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Keywords:
Mental Health;
Applications;
Startup Management;
Telehealth;
Health Care Entrepreneurship;
Health & Wellness;
Health Care;
Health Care and Treatment;
Customization and Personalization;
Internet and the Web;
Entrepreneurship;
Growth and Development Strategy;
Applications and Software
Herzlinger, Regina E., Eshani Sharma, Andrew Nguyen, Thomas Arsenault, Carin-Isabel Knoop, and Julia Kelley. "Marvin: A Personalized Telehealth Approach to Mental Health." Harvard Business School Case 321-127, February 2021. (Revised March 2022.)
- February 2024
- Module Note
Data-Driven Marketing in Retail Markets
By: Ayelet Israeli
This note describes an eight-class sessions module on data-driven marketing in retail markets. The module aims to familiarize students with core concepts of data-driven marketing in retail, including exploring the opportunities and challenges, adopting best practices,...
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Keywords:
Data;
Data Analytics;
Retail;
Retail Analytics;
Data Science;
Business Analytics;
"Marketing Analytics";
Omnichannel;
Omnichannel Retailing;
Omnichannel Retail;
DTC;
Direct To Consumer Marketing;
Ethical Decision Making;
Algorithmic Bias;
Privacy;
A/B Testing;
Descriptive Analytics;
Prescriptive Analytics;
Predictive Analytics;
Analytics and Data Science;
E-commerce;
Marketing Channels;
Demand and Consumers;
Marketing Strategy;
Retail Industry
Israeli, Ayelet. "Data-Driven Marketing in Retail Markets." Harvard Business School Module Note 524-062, February 2024.
- 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...
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Keywords:
Machine Learning;
Theory Building;
Induction;
Decision Trees;
Random Forests;
K-nearest Neighbors;
Neural Network;
P-hacking;
Analytics and Data Science;
Analysis
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.)
- 15 Sep 2020
- Video
Competing in the Age of AI and Digital Transformation
- 06 May 2012
- News
FTC Wants in on Google Antitrust Action
- 08 Nov 2016
- First Look
November 8, 2016
in power. A mass shooting increases the number of enacted laws that loosen gun restrictions by 75% in states with Republican-controlled legislatures. We find no significant effect of mass shootings on laws enacted when there is a Democrat-controlled legislature. View Details
Keywords:
Sean Silverthorne
- 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...
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Keywords:
Computational Biology;
Bioinformatics;
Innovation Competitions;
Research;
Collaborative Innovation and Invention
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).
- 21 Mar 2019
- Working Paper Summaries
Advancing Computational Biology and Bioinformatics Research Through Open Innovation Competitions
- March 2019
- Case
DayTwo: Going to Market with Gut Microbiome
By: Ayelet Israeli and David Lane
DayTwo is a young Israeli startup that applies research on the gut microbiome and machine learning algorithms to deliver personalized nutritional recommendations to its users in order to minimize blood sugar spikes after meals. After a first year of trial rollout in...
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Keywords:
Start-up Growth;
Startup;
Positioning;
Targeting;
Go To Market Strategy;
B2B2C;
B2B Vs. B2C;
Health & Wellness;
AI;
Machine Learning;
Female Ceo;
Female Protagonist;
Science-based;
Science And Technology Studies;
Ecommerce;
Applications;
DTC;
Direct To Consumer Marketing;
US Health Care;
"USA,";
Innovation;
Pricing;
Business Growth;
Segmentation;
Distribution Channels;
Growth and Development Strategy;
Business Startups;
Science-Based Business;
Health;
Innovation and Invention;
Marketing;
Information Technology;
Business Growth and Maturation;
E-commerce;
Applications and Software;
Health Industry;
Technology Industry;
Insurance Industry;
Information Technology Industry;
Food and Beverage Industry;
Israel;
United States
Israeli, Ayelet, and David Lane. "DayTwo: Going to Market with Gut Microbiome." Harvard Business School Case 519-010, March 2019.