Publications
Publications
Show Results For
- All HBS Web
(1,304)
- Faculty Publications (420)
←
Page 21 of 420
Results
- Teaching Interest
Data Science and Artificial Intelligence for Leaders
By: Chiara Farronato
With artificial intelligence (AI)... View Details
- Teaching Interest
Empirical Technology and Operations Management Course
I taught a set of lectures on "Introduction to Machine Learning for Social Scientists" as part of this required course for first year PhD students. This module familiarizes students with all the basic concepts in machine learning, their implementations, as well as the... View Details
- Forthcoming
- Article
Engaging Customers with AI in Online Chats: Evidence from a Randomized Field Experiment
By: Shunyuan Zhang and Das Narayandas
We examine how artificial intelligence (AI) affected the productivity of customer service agents and customer sentiment in online interactions. Collaborating with a meal delivery company, we conducted a randomized field experiment that exploited exogenous variation in... View Details
- Teaching Interest
Harvard Business Analytics Program: Operations and Supply Chain Management
By: Dennis Campbell
Digital technologies and data analytics are radically changing the operating model of an organization and how it connects to its broader supply chain and ecosystem. This course emphasizes managing product availability, especially in a context of rapid product... View Details
- 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
- 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
- Research Summary
Making Machine Learning Models Fair
The goal of this research direction is to ensure that the machine learning models we build and deploy do not discriminate against individuals from minority groups. View Details
- Research Summary
Making Machine Learning Models Interpretable
I work on developing various tools and methodologies which can help decision makers (e.g., doctors, managers) to better understand the predictions of machine learning models. View Details
- Research Summary
Making Machine Learning Robust to Adversarial Attacks
The goal of this research is to ensure that machine learning models that we build and deploy are not easily susceptible to attacks by adversarial or malicious entities. View Details
- Teaching Interest
Overview
Public entrepreneurship, entrepreneurship, leadership, business and government, cities, artificial intelligence View Details
- Teaching Interest
Overview
By: V.G. Narayanan
I teach accounting to MBA students, executives, and Harvard Extension School students. I teach topics from both financial and managerial accounting. I also train professors in teaching by the case method. View Details
- Research Summary
Overview
By: Isamar Troncoso
Professor Troncoso's research explores problems related to digital marketplaces and AI applications in marketing, and combines toolkits from econometrics, causal inference, and machine learning. She has studied how different platform design choices can lead to... View Details
- Research Summary
Overview
By: Iavor I. Bojinov
Over the last decade, technology companies like Amazon, Google, and Netflix have pioneered data-driven research and development processes centered on massive experimentation. However, as companies increase the breadth and scale of their experiments to millions of... View Details
- 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
- Research Summary
Overview
By: Shunyuan Zhang
Professor Zhang uses machine learning to address marketing problems that have arisen within the nascent sharing economy. She conducts rigorous analyses of structured and unstructured data generated by new sharing economy platforms to address important issues emerging... View Details
- Research Summary
Overview
By: Srikant M. Datar
Professor Datar has several research and course development interests. His initial areas of research interest were in cost management and management control, strategy implementation and governance. Over the last few years his areas of interest are management education,... View Details
- Research Summary
Overview
Engaged with field work in East Africa, South Asia, and in several large hybrid organizations in the United States, Professor Whillans places a focus on exploring questions with strong theoretical motivation in the social psychological literature and relevant... View Details
- Research Summary
Overview
Professor Ferreira's research primarily focuses on how retailers can use algorithms to make better revenue management decisions, including pricing, product display, and assortment planning. In the retail industry, anticipating consumer demand is arguably one of the... View Details
- Teaching Interest
Transforming Education through Social Entrepreneurship
This course is designed for students who want to understand the central role that education plays in our economy and society and who may want to play an active role (e.g., as entrepreneur, board member, etc.) in shaping the future workforce, bringing about a more... View Details
- Research Summary
Understanding the Limitations of Model Explanations
The goal of this research is to understand how adversaries can exploit various algorithms used for explaining complex machine learning models with an intention to mislead end users. For instance, can adversaries trick these algorithms into masking their racial and... View Details
- ←
- 21