Gemma Moran
Postdoctoral research scientist
Gemma Moran
Postdoctoral research scientist
gm2918 <at> columbia.edu
Data Science Institute
Columbia University
About
Previously, 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.
Recent news
- December 2021: I gave a talk on "Identifiable Variational Autoencoders via Sparse Decoding" at the CMStatistics conference (London, UK).
- December 2021: I gave a talk on "Identifiable Variational Autoencoders via Sparse Decoding" at the Microsoft Research New England Machine Learning Seminar.
- October 2021: I gave a talk on "Identifiable Variational Autoencoders via Sparse Decoding" at the NYU Math and Data Seminar.
Selected Publications
Identifiable Deep Generative Models via Sparse Decoding
Gemma E. Moran, Dhanya Sridhar, Yixin Wang, David M. Blei
arXiv
Spike-and-slab lasso biclustering
Gemma E. Moran, Veronika Rockova, Edward I. George
The Annals of Applied Statistics, 2021, pp. 148--173
Spike-and-slab group lassos for grouped regression and sparse generalized additive models
*Ray Bai, *Gemma E. Moran, *Joseph L. Antonelli, Yong Chen, Mary R. Boland
Journal of the American Statistical Association, 2020, pp. 1--14
Variance prior forms for high-dimensional Bayesian variable selection
Gemma E. Moran, Veronika Rockova, Edward I. George
Bayesian Analysis, 2019, pp. 1091--1119
View all