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David B. Madigan

Executive Vice President for Arts and Sciences; Dean of the Faculty of Arts and Sciences; Professor of Statistics
School: 
School of the Arts
Department: 
Statistics Department
Office: 
208 Low Library, MC 4314
Email: 
dm2418@columbia.edu
Phone: 
212-854-8296
Fax: 
212-854-5401
Appointments
  • Executive Vice President, Arts and Sciences
  • Executive Committee Member, Institute for Data Sciences and Engineering
  • Professor of Statistics
  • Member, University Forum on Global Columbia
Biography

Madigan was appointed Executive Vice President and Dean of the Faculty of Arts and Sciences in September 2013. He joined Columbia’s faculty in 2007 as a professor of statistics, and became the department's chair the following year.

Born in Athlone, Ireland, Madigan received his bachelor’s degree in mathematical sciences and a Ph.D. in statistics from Trinity College, Dublin. He is a Fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the American Association for the Advancement of Science. Before coming to Columbia Madigan was dean of physical and mathematical sciences at Rutgers University.

At Columbia, Madigan serves on the executive committee of the Institute for Data Sciences and Engineering, which was created last year in partnership with New York City. He also chairs the Provost’s Faculty Committee on Online Learning and the Shared Research Computing Policy Advisory Committee and has helped lead Columbia’s effort to offer online versions of the M.A. in statistics and the M.S. in actuarial science.

Research & Other Works
Report
Priors on the Variance in Sparse Bayesian Learning; the demi-Bayesian Lasso
Book Reviews: Principles of Data Mining. By David Hand, Heikki Mannila, and Padhraic Smyth.
Article
Medication-Wide Association Studies
Article
Algorithms for Sparse Linear Classifiers in the Massive Data Setting
Article
Learning Theory Analysis for Association Rules and Sequential Event Prediction
Article
A One-Pass Sequential Monte Carlo Method for Bayesian Analysis of Massive Datasets
Article
A Characterization of Markov Equivalence Classes for Acyclic Digraphs
Article
Correction: Separation and completeness properties for AMP chain graph Markov models
Article
Bayesian Hierarchical Rule Modeling for Predicting Medical Conditions
Article
[Bayesian Analysis in Expert Systems]: Comment: What's Next?
Article
Bayesian Model Averaging: a Tutorial (with Comments by M. Clyde, David Draper and E. I. George, and a Rejoinder by the Authors)
Article
[A Report on the Future of Statistics]: Comment
Article
Separation and Completeness Properties for Amp Chain Graph Markov Models
Article
[Least Angle Regression]: Discussion
Article
A Hierarchical Model for Association Rule Mining of Sequential Events: An Approach to Automated Medical Symptom Prediction
Article
Generating Productive Dialogue between Consulting Statisticians and their Clients in the Pharmaceutical and Medical Research Settings
Article
A Note on Equivalence Classes of Directed Acyclic Independence Graphs
Article
Location Estimation in Wireless Networks: A Bayesian Approach
Book chapter
A Flexible Bayesian Generalized Linear Model for Dichotomous Response Data with an Application to Text Categorization
 

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