Publications

November 2021
Article

Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data

By: William Herlands, Edward McFowland III, Andrew Gordon Wilson and Daniel B. Neill

Identifying anomalous patterns in real-world data is essential for understanding where, when, and how systems deviate from their expected dynamics. Yet methods that separately consider the anomalousness of each individual data point have low detection power for subtle,... View Details

Keywords: Pattern Detection; Subset Scanning; Gaussian Processes; Mathematical Methods

2015
Article

Scalable Detection of Anomalous Patterns With Connectivity Constraints

By: Skyler Speakman, Edward McFowland III and Daniel B. Neill

We present GraphScan, a novel method for detecting arbitrarily shaped connected clusters in graph or network data. Given a graph structure, data observed at each node, and a score function defining the anomalousness of a set of nodes, GraphScan can efficiently and... View Details

Keywords: Biosurveillance; Event Detection; Graph Mining; Scan Statistics; Spatial Scan Statistic

2011
Article

Scalable Detection of Anomalous Patterns With Connectivity Constraints

By: Skyler Speakman, Edward McFowland III and Daniel B. Neill

We present GraphScan, a novel method for detecting arbitrarily shaped connected clusters in graph or network data. Given a graph structure, data observed at each node, and a score function defining the anomalousness of a set of nodes, GraphScan can efficiently and... View Details

Keywords: Biosurveillance; Event Detection; Graph Mining; Scan Statistics; Spatial Scan Statistic

Article

Pattern Detection in the Activation Space for Identifying Synthesized Content

By: Celia Cintas, Skyler Speakman, Girmaw Abebe Tadesse, Victor Akinwande, Edward McFowland III and Komminist Weldemariam

Generative Adversarial Networks (GANs) have recently achieved unprecedented success in photo-realistic image synthesis from low-dimensional random noise. The ability to synthesize high-quality content at a large scale brings potential risks as the generated samples may... View Details

Keywords: Subset Scanning; Generative Models; Synthetic Content Detection

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2022
Article

Nonparametric Subset Scanning for Detection of Heteroscedasticity

By: Charles R. Doss and Edward McFowland III

We propose Heteroscedastic Subset Scan (HSS), a novel method for identifying covariates that are responsible for violations of the homoscedasticity assumption in regression settings. Viewing the problem as one of anomalous pattern detection, we use subset scanning... View Details

Keywords: Scan Statistics; Anomaly Detection; Regression; Model Diagnostics

2021
Working Paper

Detecting Anomalous Patterns of Care Using Health Insurance Claims

By: Sriram Somanchi, Edward McFowland III and Daniel B. Neill

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

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... View Details

Keywords: Autoencoder Networks; Pattern Detection; Subset Scanning; Computer Vision; Statistical Methods And Machine Learning; Machine Learning; Deep Learning; Data Mining; Big Data; Large-scale Systems; Mathematical Methods; Analytics and Data Science

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

January 2021
Article

Machine Learning for Pattern Discovery in Management Research

By: Prithwiraj Choudhury, Ryan Allen and Michael G. Endres

Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The patterns identified by ML could be used for exploratory inductive or abductive research, or for post-hoc analysis of regression results to detect... View Details

Keywords: Machine Learning; Supervised Machine Learning; Induction; Abduction; Exploratory Data Analysis; Pattern Discovery; Decision Trees; Random Forests; Neural Networks; ROC Curve; Confusion Matrix; Partial Dependence Plots; AI and Machine Learning

2016
Article

Penalized Fast Subset Scanning

By: Skyler Speakman, Sriram Somanchi, Edward McFowland III and Daniel B. Neill

We present the penalized fast subset scan (PFSS), a new and general framework for scalable and accurate pattern detection. PFSS enables exact and efficient identification of the most anomalous subsets of the data, as measured by a likelihood ratio scan statistic.... View Details

Keywords: Disease Surveillance; Likelihood Ratio Statistic; Pattern Detection; Scan Statistic; Mathematical Methods

May 2011
Article

Race at the Top: How Companies Shape the Inclusion of African Americans on Their Boards in Response to Institutional Pressures

By: Clayton S. Rose and William T. Bielby

Drawing on institutionalist theory, we conceptualize the racial composition of the boards of directors of large American companies as shaped in response to social and political norms. We use new longitudinal and cross-sectional data to test hypotheses about factors... View Details

Keywords: Leadership; Governing and Advisory Boards; Race; Mathematical Methods; Government and Politics; Public Ownership; United States

2023
Working Paper

Much Ado About Nothing? Overreaction to Random Regulatory Audits

By: Samuel Antill and Joseph Kalmenovitz

Regulators often audit firms to detect non-compliance. Exploiting a natural experiment in the lobbying industry, we show that firms overreact to audits and this response distorts prices and reduces welfare. Each year, federal regulators audit a random sample of... View Details

Keywords: Governance Compliance; Governing Rules, Regulations, and Reforms; Price

SSRN

This paper evaluates the short- and long-term value of sales representatives’ detailing visits to different types of physicians. By understanding the dynamic effect of sales calls across heterogeneous physicians, we provide guidance on the design of optimal call... View Details

November 2019
Article

How Do Sales Efforts Pay Off? Dynamic Panel Data Analysis in the Nerlove-Arrow Framework

By: Doug J. Chung, Byungyeon Kim and Byoung G. Park

This paper evaluates the short- and long-term value of sales representatives’ detailing visits to different types of physicians. By understanding the dynamic effect of sales calls across heterogeneous physicians, we provide guidance on the design of optimal call... View Details

Keywords: Nerlove-Arrow Framework; Stock-of-goodwill; Dynamic Panel Data; Serial Correlation; Instrumental Variables; Sales Effectiveness; Detailing; Analytics and Data Science; Sales; Analysis; Performance Effectiveness; Pharmaceutical Industry