Gemma Moran
Assistant Professor
Contact
Gemma Moran
Assistant Professor
gm845 <at> stat.rutgers.edu
Statistics Department
Rutgers University
As of September 2023, I am a tenure-track Assistant Professor in the Rutgers Statistics Department.
Previously, I was a postdoc at the Columbia Data Science Institute, working with David Blei. I received my PhD in Statistics from the University of Pennsylvania, advised by Edward George and Veronika Rockova.
My research develops flexible Bayesian models for analyzing high-dimensional data. Some of my recent research interests include:
Previously, I was a postdoc at the Columbia Data Science Institute, working with David Blei. I received my PhD in Statistics from the University of Pennsylvania, advised by Edward George and Veronika Rockova.
My research develops flexible Bayesian models for analyzing high-dimensional data. Some of my recent research interests include:
- developing identifiable and interpretable deep generative models (especially variational autoencoders);
- improved tools for Bayesian model criticism.
Prospective PhD students
Please apply to the Rutgers Statistics PhD program and mention my name (link here, more details here).
Selected Publications
Holdout predictive checks for Bayesian model criticism (previously Population Predictive Checks)
Gemma E. Moran, David M. Blei, Rajesh Ranganath
Journal of the Royal Statistical Society, Series B
Identifiable Deep Generative Models via Sparse Decoding
Gemma E. Moran, Dhanya Sridhar, Yixin Wang, David M. Blei
Transactions on Machine Learning Research, 2022
Gemma E. Moran, John P. Cunningham, David M. Blei
Bayesian Analysis, 2022
Venkateswaran Shekar, Vincent Yu, Benjamin J. Garcia, David Benjamin Gordon, Gemma E. Moran, David M. Blei, Loïc M. Roch, Alberto García-Durán, Mansoor Ani Najeeb, Margaret Zeile, Philip W. Nega, Zhi Li, Mina A. Kim, Emory M. Chan, Alexander J. Norquist, Sorelle Friedler, Joshua Schrier
ChemRxiv, 2022
Spike-and-slab lasso biclustering
Gemma E. Moran, Veronika Rockova, Edward I. George
The Annals of Applied Statistics, vol. 15, 2021, pp. 148--173
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