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

  • 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


Population Predictive Checks


Gemma E. Moran, David M. Blei, Rajesh Ranganath


arXiv


Identifiable Deep Generative Models via Sparse Decoding


Gemma E. Moran, Dhanya Sridhar, Yixin Wang, David M. Blei


arXiv


The Posterior Predictive Null


Gemma E. Moran, John P. Cunningham, David M. Blei


Bayesian Analysis, 2022


Serendipity based recommender system for perovskites material discovery: balancing exploration and exploitation across multiple models


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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