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
- August 2022: I will give a talk on "Identifiable Deep Generative Models via Sparse Decoding" at the Causal Representation Learning Workshop at UAI (Eindhoven, Netherlands).
- June 2022: I won a poster award (top ~10% of posters) at ISBA for "The Posterior Predictive Null" (Montreal, Canada).
- June 2022: I gave a talk on "Identifiable Deep Generative Models via Sparse Decoding" at the ICSA Applied Statistics Symposium (Gainesville, FL).
Selected Publications
Identifiable Deep Generative Models via Sparse Decoding
Gemma E. Moran, Dhanya Sridhar, Yixin Wang, David M. Blei
arXiv
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
View all