Gemma Moran


Postdoctoral research scientist


Curriculum vitae


gm2918 <at> columbia.edu

Data Science Institute


Columbia University



Gemma Moran


Postdoctoral research scientist


Contact

Gemma Moran


Postdoctoral research scientist


Curriculum vitae


gm2918 <at> columbia.edu

Data Science Institute


Columbia University




About


I am a postdoc at the Columbia Data Science Institute, working with David Blei.

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


Posterior Predictive Null Checks


Gemma E. Moran, John P. Cunningham, 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


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