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Show Results For
- All HBS Web
(757)
- News (190)
- Research (336)
- Events (8)
- Multimedia (7)
- Faculty Publications (263)
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- 2015
- Conference Presentation
The Color of New Tastes: State Power, Industry, and Hegemony of Vision in Modern Food Stores in the United States, 1870s-1930s
By: Ai Hisano
- 11 Jan 2015
- Talk
Processed Foods in the Early-Twentieth-Century United States
By: Ai Hisano
Hisano, Ai. "Processed Foods in the Early-Twentieth-Century United States." Culinary Historians of Washington D.C. Meeting, Culinary Historians of Washington, D.C., Washington, DC, January 11, 2015.
- 2023
- Working Paper
Sending Signals: Strategic Displays of Warmth and Competence
By: Bushra S. Guenoun and Julian J. Zlatev
Using a combination of exploratory and confirmatory approaches, this research examines how
people signal important information about themselves to others. We first train machine learning
models to assess the use of warmth and competence impression management... View Details
Keywords: AI and Machine Learning; Personal Characteristics; Perception; Interpersonal Communication
Guenoun, Bushra S., and Julian J. Zlatev. "Sending Signals: Strategic Displays of Warmth and Competence." Harvard Business School Working Paper, No. 23-051, February 2023.
- 06 Jun 2017
- First Look
First Look at New Research and Ideas: June 6, 2017
survival of only the most productive domestic firms. We investigate the roles of the two different mechanisms in determining the aggregate productivity gains by exploring their distinct predictions on the distributions of domestic firms:... View Details
Keywords: Sean Silverthorne
- April 2023
- Article
On the Privacy Risks of Algorithmic Recourse
By: Martin Pawelczyk, Himabindu Lakkaraju and Seth Neel
As predictive models are increasingly being employed to make consequential decisions, there is a growing emphasis on developing techniques that can provide algorithmic recourse to affected individuals. While such recourses can be immensely beneficial to affected... View Details
Pawelczyk, Martin, Himabindu Lakkaraju, and Seth Neel. "On the Privacy Risks of Algorithmic Recourse." Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 206 (April 2023).
- 2024
- Article
Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules
By: Michael Lingzhi Li and Kosuke Imai
A century ago, Neyman showed how to evaluate the efficacy of treatment using a randomized experiment under a minimal set of assumptions. This classical repeated sampling framework serves as a basis of routine experimental analyses conducted by today’s scientists across... View Details
Li, Michael Lingzhi, and Kosuke Imai. "Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules." Journal of Causal Inference 12, no. 1 (2024).
- January–February 2023
- Article
Data-Driven COVID-19 Vaccine Development for Janssen
By: Dimitris Bertsimas, Michael Lingzhi Li, Xinggang Liu, Jennings Xu and Najat Khan
The COVID-19 pandemic has spurred extensive vaccine research worldwide. One crucial part of vaccine development is the phase III clinical trial that assesses the vaccine for safety and efficacy in the prevention of COVID-19. In this work, we enumerate the first... View Details
Keywords: COVID-19; Health Testing and Trials; Forecasting and Prediction; AI and Machine Learning; Research; Pharmaceutical Industry
Bertsimas, Dimitris, Michael Lingzhi Li, Xinggang Liu, Jennings Xu, and Najat Khan. "Data-Driven COVID-19 Vaccine Development for Janssen." INFORMS Journal on Applied Analytics 53, no. 1 (January–February 2023): 70–84.
- 2022
- Conference Presentation
Towards the Unification and Robustness of Post hoc Explanation Methods
By: Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu and Himabindu Lakkaraju
As machine learning black boxes are increasingly being deployed in critical domains such as healthcare and criminal justice, there has been a growing emphasis on developing techniques for explaining these black boxes in a post hoc manner. In this work, we analyze two... View Details
Keywords: AI and Machine Learning
Agarwal, Sushant, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu, and Himabindu Lakkaraju. "Towards the Unification and Robustness of Post hoc Explanation Methods." Paper presented at the 3rd Symposium on Foundations of Responsible Computing (FORC), 2022.
- 2023
- Working Paper
The Optimal Stock Valuation Ratio
By: Sebastian Hillenbrand and Odhrain McCarthy
Trailing price ratios, such as the price-dividend and the price-earnings ratio, scale prices by trailing cash flow measures. They theoretically contain expected returns, yet, their performance in predicting stock market returns is poor. This is because of an omitted... View Details
Keywords: Price; Investment Return; AI and Machine Learning; Valuation; Cash Flow; Forecasting and Prediction
Hillenbrand, Sebastian, and Odhrain McCarthy. "The Optimal Stock Valuation Ratio." Working Paper, November 2023.
- 2008
- Conference Presentation
Betty Crocker and American Women in the Early Twentieth Century
By: Ai Hisano
Hisano, Ai. "Betty Crocker and American Women in the Early Twentieth Century." Paper presented at the Graduate Students in American Studies Program Annual Meeting, Tokyo, Japan, 2008.
- 2015
- Conference Presentation
Creating ‘Natural’ Yellow: The Development of the American Dairy Industry at the Turn of the Twentieth Century
By: Ai Hisano
- 2015
- Dictionary Entry
Consistency of Food Products/Ingredients
By: Ai Hisano
Hisano, Ai. "Consistency of Food Products/Ingredients." In The SAGE Encyclopedia of Food Issues, edited by Ken Albala, 280–282. Thousand Oaks, CA: SAGE Publications, 2015.
- Summer 2013
- Article
Negotiating Taste: Food Market Research in the Hagley Library
By: Ai Hisano
Hisano, Ai. "Negotiating Taste: Food Market Research in the Hagley Library." Digest: A Journal of Foodways and Culture 2 (Summer 2013).
- April 2024
- Article
A Machine Learning Algorithm Predicting Risk of Dilating VUR among Infants with Hydronephrosis Using UTD Classification
By: Hsin-Hsiao Scott Wang, Michael Lingzhi Li, Dylan Cahill, John Panagides, Tanya Logvinenko, Jeanne Chow and Caleb Nelson
Backgrounds: Urinary Tract Dilation (UTD) classification has been designed to be a more objective grading system to evaluate antenatal and post-natal UTD. Due to unclear association between UTD classifications to specific anomalies such as vesico-ureteral reflux (VUR),... View Details
Wang, Hsin-Hsiao Scott, Michael Lingzhi Li, Dylan Cahill, John Panagides, Tanya Logvinenko, Jeanne Chow, and Caleb Nelson. "A Machine Learning Algorithm Predicting Risk of Dilating VUR among Infants with Hydronephrosis Using UTD Classification." Journal of Pediatric Urology 20, no. 2 (April 2024): 271–278.
- June 2020
- Article
Real-time Data from Mobile Platforms to Evaluate Sustainable Transportation Infrastructure
By: Omar Isaac Asensio, Kevin Alvarez, Arielle Dror, Emerson Wenzel, Catharina Hollauer and Sooji Ha
By displacing gasoline and diesel fuels, electric cars and fleets reduce emissions from the transportation sector, thus offering important public health benefits. However, public confidence in the reliability of charging infrastructure remains a fundamental barrier to... View Details
Keywords: Environmental Sustainability; Transportation; Infrastructure; Behavior; AI and Machine Learning; Demand and Consumers
Asensio, Omar Isaac, Kevin Alvarez, Arielle Dror, Emerson Wenzel, Catharina Hollauer, and Sooji Ha. "Real-time Data from Mobile Platforms to Evaluate Sustainable Transportation Infrastructure." Nature Sustainability 3, no. 6 (June 2020): 463–471.
- 30 Jan 2018
- First Look
January 30, 2018
historically, only government-subsidized products have achieved widespread adoption. A recent contractual innovation, which links insurance payouts to realized weather rather than farmer indemnity, has spurred substantial research in the past decade. This review begins... View Details
Keywords: Sean Silverthorne
- October 2015 (Revised October 2016)
- Case
Building Watson: Not So Elementary, My Dear! (Abridged)
By: Willy C. Shih
This case is set inside IBM Research's efforts to build a computer that can successfully take on human challengers playing the game show Jeopardy! It opens with the machine named Watson offering the incorrect answer "Toronto" to a seemingly simple question during the... View Details
Keywords: Analytics; Big Data; Business Analytics; Product Development Strategy; Machine Learning; Machine Intelligence; Artificial Intelligence; Product Development; AI and Machine Learning; Information Technology; Analytics and Data Science; Information Technology Industry; United States
Shih, Willy C. "Building Watson: Not So Elementary, My Dear! (Abridged)." Harvard Business School Case 616-025, October 2015. (Revised October 2016.)
- 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).
- January–February 2023
- Article
Forecasting COVID-19 and Analyzing the Effect of Government Interventions
By: Michael Lingzhi Li, Hamza Tazi Bouardi, Omar Skali Lami, Thomas Trikalinos, Nikolaos Trichakis and Dimitris Bertsimas
We developed DELPHI, a novel epidemiological model for predicting detected cases and deaths in the prevaccination era of the COVID-19 pandemic. The model allows for underdetection of infections and effects of government interventions. We have applied DELPHI across more... View Details
Keywords: COVID-19 Pandemic; Epidemics; Analytics and Data Science; Health Pandemics; AI and Machine Learning; Forecasting and Prediction
Li, Michael Lingzhi, Hamza Tazi Bouardi, Omar Skali Lami, Thomas Trikalinos, Nikolaos Trichakis, and Dimitris Bertsimas. "Forecasting COVID-19 and Analyzing the Effect of Government Interventions." Operations Research 71, no. 1 (January–February 2023): 184–201.
- 2015
- Talk
Creating 'Natural Yellow' for Butter and Oleomargarine
By: Ai Hisano
Hisano, Ai. "Creating 'Natural Yellow' for Butter and Oleomargarine." National Museum of American History Colloquium, 2015.