Filter Results:
(1,219)
Show Results For
- All HBS Web
(1,219)
- People (1)
- News (247)
- Research (698)
- Events (17)
- Multimedia (9)
- Faculty Publications (582)
Show Results For
- All HBS Web
(1,219)
- People (1)
- News (247)
- Research (698)
- Events (17)
- Multimedia (9)
- Faculty Publications (582)
- 2023
- Article
Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse
By: Martin Pawelczyk, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci and Himabindu Lakkaraju
As machine learning models are increasingly being employed to make consequential decisions in real-world settings, it becomes critical to ensure that individuals who are adversely impacted (e.g., loan denied) by the predictions of these models are provided with a means... View Details
Pawelczyk, Martin, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci, and Himabindu Lakkaraju. "Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse." Proceedings of the International Conference on Learning Representations (ICLR) (2023).
- Winter 2016
- Article
Analytics for an Online Retailer: Demand Forecasting and Price Optimization
By: Kris J. Ferreira, Bin Hong Alex Lee and David Simchi-Levi
We present our work with an online retailer, Rue La La, as an example of how a retailer can use its wealth of data to optimize pricing decisions on a daily basis. Rue La La is in the online fashion sample sales industry, where they offer extremely limited-time... View Details
Ferreira, Kris J., Bin Hong Alex Lee, and David Simchi-Levi. "Analytics for an Online Retailer: Demand Forecasting and Price Optimization." Manufacturing & Service Operations Management 18, no. 1 (Winter 2016): 69–88.
- 06 Jun 2017
- First Look
First Look at New Research and Ideas: June 6, 2017
understanding the lack of diversity in entrepreneurship and the venture capital industry. Download working paper: https://www.hbs.edu/faculty/Pages/item.aspx?num=52704 Cellophane, the New Visuality, and the... View Details
Keywords: Sean Silverthorne
- 01 Mar 2016
- News
Faculty Q&A: Price Check
How did you come to focus on algorithmic pricing? In my doctoral work at MIT, I was studying optimization, probability, and machine learning, which are essentially mathematical tools that enable us to use data to make better decisions. From there, I realized I wanted... View Details
- 11 Dec 2019
- News
Are you ready for a robot boss? Many workers say that yes, they are
Shunyuan Zhang
Shunyuan Zhang is an associate professor in the Marketing unit at Harvard Business School. She teaches the first-year Marketing course in the MBA required curriculum.
Professor Zhang studies the sharing economy and the marketing problems that... View Details
- September 2020
- Case
True North: Pioneering Analytics, Algorithms and Artificial Intelligence
By: Karim R. Lakhani, Kairavi Dey and Hannah Mayer
True North was a private equity fund that specialized in the growth and buyout of mid-market, India-centric companies. The leadership team initially believed that technology was not core to traditional businesses and steered clear of new age technology-oriented... View Details
Keywords: Artificial Intelligence; Information Technology; Management; Operations; Organizations; Leadership; Innovation and Invention; Business Model; AI and Machine Learning; Computer Industry; Technology Industry
Lakhani, Karim R., Kairavi Dey, and Hannah Mayer. "True North: Pioneering Analytics, Algorithms and Artificial Intelligence." Harvard Business School Case 621-042, September 2020.
- 24 Jul 2019
- Blog Post
Data-Driven and in Demand
mining and exploratory analysis, and familiarized herself with the ins and outs of various machine learning... View Details
- September 2023 (Revised December 2023)
- Case
TetraScience: Noise and Signal
By: Thomas R. Eisenmann and Tom Quinn
In 2019, TetraScience CEO “Spin” Wang needed advice. Five years earlier, he had cofounded a startup that saw early success with a hardware product designed to help laboratory scientists in the biotechnology and pharmaceutical spaces more easily collect data from... View Details
Keywords: Entrepreneurship; Business Growth and Maturation; Business Organization; Restructuring; Forecasting and Prediction; Digital Platforms; Analytics and Data Science; AI and Machine Learning; Organizational Structure; Network Effects; Competitive Strategy; Biotechnology Industry; Pharmaceutical Industry; United States; Boston
Eisenmann, Thomas R., and Tom Quinn. "TetraScience: Noise and Signal." Harvard Business School Case 824-024, September 2023. (Revised December 2023.)
Isamar Troncoso
Isamar Troncoso is an Assistant Professor of Business Administration in the Marketing Unit at HBS. She teaches the Marketing course in the MBA required curriculum.
Professor Troncoso studies problems related to digital marketplaces and new technologies. She... View Details
- 29 May 2018
- First Look
New Research and Ideas, May 29, 2018
incorporate these advancements to improve the way the functions work, how to incorporate machine learning and artificial intelligence that de facto improve productivity View Details
Keywords: Dina Gerdeman
- 23 Mar 2023
- HBS Seminar
Tinglong Dai, Johns Hopkins
- December 2022 (Revised January 2025)
- Case
Akooda: Charging Toward Operational Intelligence
By: Christopher Stanton and Mel Martin
The Akooda case describes the challenges confronting founder and CEO Yuval Gonczarowski (MBA ‘17) in 2022 as he attempts to boost sales. Launched in November 2020, Akooda was an AI company that mined 20 different sources of digital data, from tools like Slack, Google... View Details
Keywords: Data Mining; Productivity; Monitoring; Data Analysis; AI and Machine Learning; Knowledge Management; Operations; Problems and Challenges; Employee Relationship Management; Information Technology Industry; Technology Industry; Information Industry; Boston; Israel
Stanton, Christopher, and Mel Martin. "Akooda: Charging Toward Operational Intelligence." Harvard Business School Case 823-018, December 2022. (Revised January 2025.)
- 17 Jul 2023
- Research & Ideas
Money Isn’t Everything: The Dos and Don’ts of Motivating Employees
that the process is transparent and understood, even if everyone’s individual pay isn’t transparent. Don’t replace all your people with robots In the age of AI and robotics,... View Details
Keywords: by Avery Forman
- 09 Dec 2019
- News
Identify Great Customers from Their First Purchase
- 2025
- Working Paper
Warnings and Endorsements: Improving Human-AI Collaboration in the Presence of Outliers
By: Matthew DosSantos DiSorbo, Kris Ferreira, Maya Balakrishnan and Jordan Tong
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 make... View Details
DosSantos DiSorbo, Matthew, Kris Ferreira, Maya Balakrishnan, and Jordan Tong. "Warnings and Endorsements: Improving Human-AI Collaboration in the Presence of Outliers." Working Paper, May 2025.
Julian De Freitas
Julian De Freitas is an Assistant Professor of Business Administration in the Marketing Unit, and Director of the Ethical Intelligence Lab, at Harvard Business School. He earned his PhD in psychology from Harvard, masters from Oxford, and BA from Yale. He teaches... View Details
- 2023
- Other Article
The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications
By: Mirac Suzgun, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers and Stuart Shieber
Innovation is a major driver of economic and social development, and information about many kinds of innovation is embedded in semi-structured data from patents and patent applications. Though the impact and novelty of innovations expressed in patent data are difficult... View Details
Keywords: USPTO; Natural Language Processing; Classification; Summarization; Patent Novelty; Patent Trolls; Patent Enforceability; Patents; Innovation and Invention; Intellectual Property; AI and Machine Learning; Analytics and Data Science
Suzgun, Mirac, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers, and Stuart Shieber. "The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications." Conference on Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track 36 (2023).
- 12 May 2022
- News