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- 2021
- Working Paper
Deep Learning for Two-Sided Matching
By: Sai Srivatsa Ravindranatha, Zhe Feng, Shira Li, Jonathan Ma, Scott Duke Kominers and David Parkes
We initiate the use of a multi-layer neural network to model two-sided matching and to explore the design space between strategy-proofness and stability. It is well known that both properties cannot be achieved simultaneously but the efficient frontier in this design...
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- June 2021
- Case
Bozoma Saint John: Leading with Authenticity and Urgency
By: Francesca Gino and Frances X. Frei
In this multimedia case, Bozoma Saint John recounts numerous defining moments from her childhood and work experiences. We learn what empowered and inspired her to be her authentic self, to be vulnerable and open to new experiences, to find commonality with others, to...
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- February 2021
- Case
Emma Dench: Leadership and Ancient Rome
By: Francesca Gino and Frances X. Frei
In this multimedia case, classics scholar Emma Dench guides us in understanding leadership insights that can be captured from historical figures and works dating back to Ancient Rome. We learn the language, ideas, and patterns of behavior that are relevant to...
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- Article
Development of a Deep Learning Algorithm for Periapical Disease Detection in Dental Radiographs
By: Michael G. Endres, Florian Hillen, Marios Salloumis, Ahmad R. Sedaghat, Stefan M. Niehues, Olivia Quatela, Henning Hanken, Ralf Smeets, Benedicta Beck-Broichsitter, Carsten Rendenbach, Karim R. Lakhani, Max Helland and Robert A. Gaudin
Periapical radiolucencies, which can be detected on panoramic radiographs, are one of the most common radiographic findings in dentistry and have a differential diagnosis including infections, granuloma, cysts, and tumors. In this study, we seek to investigate the...
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- Article
Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error
By: Celia Cintas, Skyler Speakman, Victor Akinwande, William Ogallo, Komminist Weldemariam, Srihari Sridharan and Edward McFowland III
Reliably detecting attacks in a given set of inputs is of high practical relevance because of the vulnerability of neural networks to adversarial examples. These altered inputs create a security risk in applications with real-world consequences, such as self-driving...
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- 2020
- Conference Presentation
Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error
By: Celia Cintas, Skyler Speakman, Victor Akinwande, William Ogallo, Komminist Weldemariam, Srihari Sridharan and Edward McFowland III
Reliably detecting attacks in a given set of inputs is of high practical relevance because of the vulnerability of neural networks to adversarial examples. These altered inputs create a security risk in applications with real-world consequences, such as self-driving...
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- July 2019
- Teaching Note
AT&T, Retraining, and the Workforce of Tomorrow
By: William R. Kerr and Carl Kreitzberg
A Teaching Note for the "AT&T, Retraining, and the Workforce of Tomorrow" case study (HBS#820-017). The case describes how AT&T designed and implemented a program to retrain 100,000 of its workers. The case first reviews the technological forces that compelled AT&T to...
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Keywords:
AT＆T;
Workforce;
Future Of Work;
Telecommunications;
Unions;
Technological Change;
Layoffs;
MOOCS;
Strategic Planning;
Employees;
Training;
Labor;
Learning;
Labor Unions;
Technology Adoption;
Talent and Talent Management;
Transformation;
Telecommunications Industry;
Communications Industry;
United States
- July 2019 (Revised November 2019)
- Case
Osaro: Picking the Best Path
By: William R. Kerr, James Palano and Bastiane Huang
The founder of Osaro saw the potential of deep reinforcement learning to allow robots to be applied to new applications. Osaro targeted warehousing, already a dynamic industry for robotics and automation, for its initial product—a system which would allow robotic arms...
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- 2020
- Working Paper
Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach
By: Nicolas Padilla and Eva Ascarza
The success of Customer Relationship Management (CRM) programs ultimately depends on the firm's ability to understand consumers' preferences and precisely capture how these preferences may differ across customers. Only by understanding customer heterogeneity, firms can...
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- September 2018 (Revised December 2019)
- Case
Zebra Medical Vision
By: Shane Greenstein and Sarah Gulick
An Israeli startup founded in 2014, Zebra Medical Vision developed algorithms that produced diagnoses from X-rays, mammograms, and CT-scans. The algorithms used deep learning and digitized radiology scans to create software that could assist doctors in making...
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Keywords:
Radiology;
Machine Learning;
X-ray;
CT Scan;
Medical Technology;
Probability;
FDA 510(k);
Diagnosis;
Business Startups;
Health Care and Treatment;
Technology;
Software;
Competitive Strategy;
Product Development;
Commercialization;
Decision Choices and Conditions;
Health Industry;
Medical Devices and Supplies Industry;
Technology Industry;
Israel
- 2017
- Mimeo
Science for Society: Science and Technology Based Social Entrepreneurship
By: Tarun Khanna, Shashank Shah and Kundan Madireddy
This publication is an outcome of the team's research, engagement and interactions with over 25 science and technology-based social enterprises in India. It provides details on the research process, insightful outcomes and innovative impact.
Throughout the... View Details
Throughout the... View Details
- February 2015 (Revised September 2016)
- Teaching Note
Making stickK Stick: The Business of Behavioral Economics
By: Leslie K. John and Michael Norton
Email mking@hbs.edu for a courtesy copy.
This Teaching Note explains the theory of the case and teaching plan for the case: Making sticK Stick: The Business of Behavioral Economics (514019). The case focuses on a... View Details
This Teaching Note explains the theory of the case and teaching plan for the case: Making sticK Stick: The Business of Behavioral Economics (514019). The case focuses on a... View Details
Keywords:
Behavioral Economics;
Behavior Change;
B2B Vs. B2C;
Human Resource Management;
Marketing Of Innovations;
Health ＆ Wellness;
Weight Loss;
Charitable Giving;
Marketing;
Consumer Behavior;
Entrepreneurship;
Internet;
Health;
Business Model;
Sales;
Human Resources;
Health Industry;
United States
- 12 Dec 2014
- Conference Presentation
Aspect Specific Sentiment Analysis Using Hierarchical Deep Learning
By: Himabindu Lakkaraju, Richard Socher and Chris Manning
- 2014
- Book
Managerial Accounting: Making Decisions and Motivating Performance
By: Srikant M. Datar and Madhav Rajan
Managerial Accounting: Making Decisions and Motivating Performance enables future managers and business owners to attain the core skills they need to become integral members of their company’s decision-making teams. This new program from established authors...
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- April 2011
- Article
Why Leaders Don't Learn from Success
By: Francesca Gino and Gary P. Pisano
We argue that for a variety of psychological reasons, it is often much harder for leaders and organizations to learn from success than to learn from failure. Success creates three kinds of traps that often impede deep learning. The first is attribution error or the...
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- Research Summary
Innovating in Energy: Learning from High-Potential Ventures
My work at HBS has always focused on high-potential ventures. Most recently, these have been professionally financed start-ups and buyouts in newly emerging energy and cleantech businesses. These ventures tend to be based on innovative insights into technology and... View Details
- Teaching Interest
Interpretability and Explainability in Machine Learning
As machine learning models are increasingly being employed to aid decision makers in high-stakes settings such as healthcare and criminal justice, it is important to ensure that the decision makers correctly understand and consequent trust the functionality of these... View Details
- Forthcoming
- Article
Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach
By: Nicolas Padilla and Eva Ascarza
The success of Customer Relationship Management (CRM) programs ultimately depends on the firm's ability to understand consumers' preferences and precisely capture how these preferences may differ across customers. Only by understanding customer heterogeneity, firms can...
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- Forthcoming
- Article
What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features
By: Shunyuan Zhang, Dokyun Lee, Param Singh and Kannan Srinivasan
We study how Airbnb property demand changed after the acquisition of verified images (taken by Airbnb’s photographers) and explore what makes a good image for an Airbnb property. Using deep learning and difference-in-difference analyses on an Airbnb panel dataset...
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