Gemma Moran

Assistant Professor


Curriculum vitae


gm845 <at> stat.rutgers.edu


Statistics Department

Rutgers University



Gemma Moran

Assistant Professor


Contact

Gemma Moran

Assistant Professor


Curriculum vitae


gm845 <at> stat.rutgers.edu


Statistics Department

Rutgers University




About


I am a tenure-track Assistant Professor in the Rutgers Statistics Department.

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

Prospective PhD students

Please apply to the Rutgers Statistics PhD program and mention my name (link here, more details here).

Masters of Data Science students

Capstone Projects
Rutgers MSDS students can use a final project from one of my MSDS classes as their capstone.  

For capstones based on group projects, students will have to complete a revision independently using a different statistical method, different dataset, or a different simulation design, and submit both the initial group and final individual report.
Research Projects
Please take my MSDS-534 Statistical Learning for Data Science course if you are interested in doing a research project with me.

Selected Publications


Holdout predictive checks for Bayesian model criticism (previously Population Predictive Checks)


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

Journal of the Royal Statistical Society, Series B


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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