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. In September 2023, I will join the Rutgers Statistics Department as a tenure-track Assistant Professor.

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

  • November 2022:  I will give a talk on  "Identifiable Deep Generative Models via Sparse Decoding" at the Rising Stars in Data Science workshop at the University of Chicago (Chicago, Illinois).
  • August 2022: I gave 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

Transactions on Machine Learning Research, 2022


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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