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- All HBS Web
(422)
- People (1)
- News (88)
- Research (285)
- Events (4)
- Multimedia (4)
- Faculty Publications (162)
Show Results For
- All HBS Web
(422)
- People (1)
- News (88)
- Research (285)
- Events (4)
- Multimedia (4)
- Faculty Publications (162)
- July 1984 (Revised May 1986)
- Background Note
Solving Mysteries: Playing Organizational Detective
Kets de Vries, Manfred F. "Solving Mysteries: Playing Organizational Detective." Harvard Business School Background Note 485-001, July 1984. (Revised May 1986.)
- 26 Mar 2008
- News
Scientists Detect Nice NYT Readers
- 22 Jun 2017
- Cold Call Podcast
ShotSpotter: A Gunfire Detection Business Looks for a New Market
- 24 Apr 2014
- News
Detection devices that outsmart the criminally minded
Security, US Customs and Border Protection, and about 60 foreign governments to detect even the most cleverly concealed drugs, cash, and guns. “I come from inner-city Chicago, an area that was decimated by drugs,” says Harris. “To think... View Details
- 22 Jun 2017
- News
ShotSpotter: A Gunfire Detection Business Looks for a New Market
- 2023
- Working Paper
Detecting Structural Breaks in Inflation Trends: A High-Frequency Approach
By: Alberto Cavallo and Gaston Garcia Zavaleta
We combine standard structural-break methods with high-frequency data to identify shifts in inflation trends. We use this approach to study the inflation dynamics of 25 countries from January 2022 to April 2023 and find evidence of a broad-based slowdown in about half... View Details
Cavallo, Alberto, and Gaston Garcia Zavaleta. "Detecting Structural Breaks in Inflation Trends: A High-Frequency Approach." Working Paper, May 2023. (Preliminary draft.)
- 25 Jun 2020
- News
Connecting the dots from detection to cure
- 2023
- Article
Provable Detection of Propagating Sampling Bias in Prediction Models
By: Pavan Ravishankar, Qingyu Mo, Edward McFowland III and Daniel B. Neill
With an increased focus on incorporating fairness in machine learning models, it becomes imperative not only to assess and mitigate bias at each stage of the machine learning pipeline but also to understand the downstream impacts of bias across stages. Here we consider... View Details
Ravishankar, Pavan, Qingyu Mo, Edward McFowland III, and Daniel B. Neill. "Provable Detection of Propagating Sampling Bias in Prediction Models." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 9562–9569. (Presented at the 37th AAAI Conference on Artificial Intelligence (2/7/23-2/14/23) in Washington, DC.)
- December 1979
- Article
Evaluating and Comparing Projects: Simple Detection of False Alarms
By: John S. Hammond and John W. Pratt
Keywords: Performance Evaluation
Hammond, John S., and John W. Pratt. "Evaluating and Comparing Projects: Simple Detection of False Alarms." Journal of Finance 34, no. 5 (December 1979): 1231–1242.
- March 2025
- Supplement
Thrive Earlier Detection (B) and the Evolution of Catalio Capital Management
By: Malcolm Baker, Tara Bozzini and William Vrattos
Baker, Malcolm, Tara Bozzini, and William Vrattos. "Thrive Earlier Detection (B) and the Evolution of Catalio Capital Management." Harvard Business School Supplement 225-046, March 2025.
- Article
Organization of Information and the Detection of Gender Discrimination
By: C. Rutte, T. Diekmann, J. Polzer, F. Crosby and D. Messick
Rutte, C., T. Diekmann, J. Polzer, F. Crosby, and D. Messick. "Organization of Information and the Detection of Gender Discrimination." Psychological Science 5, no. 4 (July 1994): 226–231.
- 23 Jun 2021
- News
Investors Burned by Fraud Get Better at Detecting Future Bad Actors
- 2023
- Working Paper
Efficient Discovery of Heterogeneous Quantile Treatment Effects in Randomized Experiments via Anomalous Pattern Detection
By: Edward McFowland III, Sriram Somanchi and Daniel B. Neill
In the recent literature on estimating heterogeneous treatment effects, each proposed method makes its own set of restrictive assumptions about the intervention’s effects and which subpopulations to explicitly estimate. Moreover, the majority of the literature provides... View Details
Keywords: Causal Inference; Program Evaluation; Algorithms; Distributional Average Treatment Effect; Treatment Effect Subset Scan; Heterogeneous Treatment Effects
McFowland III, Edward, Sriram Somanchi, and Daniel B. Neill. "Efficient Discovery of Heterogeneous Quantile Treatment Effects in Randomized Experiments via Anomalous Pattern Detection." Working Paper, 2023.
- 2021
- Working Paper
Detecting Anomalous Patterns of Care Using Health Insurance Claims
By: Sriram Somanchi, Edward McFowland III and Daniel B. Neill
- November 2004
- Article
True or False? Lie Detection at the Bargaining Table
By: Michael Wheeler
Keywords: Negotiation
Wheeler, Michael. "True or False? Lie Detection at the Bargaining Table." Negotiation 7, no. 11 (November 2004).
- 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... View Details
Keywords: Artificial Intelligence; Diagnosis; Computer-assisted; Image Interpretation; Machine Learning; Radiography; Panoramic Radiograph; AI and Machine Learning
Endres, Michael G., 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. "Development of a Deep Learning Algorithm for Periapical Disease Detection in Dental Radiographs." Diagnostics 10, no. 6 (June 2020).
- June 2016
- Article
Detecting Figures and Part Labels in Patents: Competition-based Development of Graphics Recognition Algorithms
By: Christoph Riedl, Richard Zanibbi, Marti A. Hearst, Siyu Zhu, Michael Menietti, Jason Crusan, Ivan Metelsky and Karim R. Lakhani
Riedl, Christoph, Richard Zanibbi, Marti A. Hearst, Siyu Zhu, Michael Menietti, Jason Crusan, Ivan Metelsky, and Karim R. Lakhani. "Detecting Figures and Part Labels in Patents: Competition-based Development of Graphics Recognition Algorithms." International Journal on Document Analysis and Recognition (IJDAR) 19, no. 2 (June 2016): 155–172.
- 2018
- Working Paper
Detecting Anomalies: The Relevance and Power of Standard Asset Pricing Tests
By: Malcolm Baker, Patrick Luo and Ryan Taliaferro
The two standard approaches for identifying capital market anomalies are cross-sectional coefficient tests, in the spirit of Fama and MacBeth (1973), and time-series intercept tests, in the spirit of Jensen (1968). A new signal can pass the first test, which we label a... View Details
Keywords: Investment Management; Anomalies; Portfolio Construction; Transaction Costs; Investment; Management; Asset Pricing; Market Transactions; Cost
Baker, Malcolm, Patrick Luo, and Ryan Taliaferro. "Detecting Anomalies: The Relevance and Power of Standard Asset Pricing Tests." Working Paper, July 2018.
- Article
Multivoxel Patterns in Face-sensitive Temporal Regions Reveal an Encoding Schema Based on Detecting Life in a Face
By: Christine E. Looser, J. Swaroop Guntupalli and Thalia Wheatley
More than a decade of research has demonstrated that faces evoke prioritized processing in a 'core face network' of three brain regions. However, whether these regions prioritize the detection of global facial form (shared by humans and mannequins) or the detection of... View Details
Keywords: Brain Imaging; Social Psychology; Mind Perception; Identity; Science; Cognition and Thinking
Looser, Christine E., J. Swaroop Guntupalli, and Thalia Wheatley. "Multivoxel Patterns in Face-sensitive Temporal Regions Reveal an Encoding Schema Based on Detecting Life in a Face." Social Cognitive and Affective Neuroscience 8, no. 7 (October 2013): 799–805.