# About

I am an Assistant Professor in the Department of Statistics and Data Science at Carnegie Mellon University.

### Brief bio sketch

Before joining CMU I was a Florence Nightingale Bicentennial Fellow and Tutor in Computational Statistics and Machine Learning at the University of Oxford.

Prior to that, I was a Data Science Initiative Postdoctoral Fellow at Harvard University, mentored by Pierre Jacob.

Previously, I did my PhD in Statistics at Columbia University, advised by Liam Paninski. During my PhD, I did an internship at Google Brain (Cambridge, MA), hosted by Jasper Snoek.

I am originally from Santiago, Chile. There, I obtained by B.Sc. in Mathematical Engineering at Universidad de Chile.

### Research

I develop robust, efficient, and theoretically sound statistical methodology for tackling challenging life sciences problems involving data. My experience comes from neuroscience and epidemiology, where datasets are of two extreme types. In neuroscience, they are the output of cutting-edge technologies (e.g., a new microscope), where I develop methods to crack these datasets, i.e., to extract the relevant signals at scale and reasonable time, accelerating scientific discovery. At the other extreme, there are the massive routinely collected “cheap” data (e.g. epidemiological surveillance); my goal here is to propose frameworks to draw valid inferences out of these inherently corrupted, biased data and to combine them with more reliable data.

I am particularly interested in using/analyzing tools coming from Bayesian statistics, because of its practical appeal, and from Optimal Transport, where I have grown a more theoretical/methodological interest; I explore the computational and statistical aspects of entropic Optimal Transport in order to derive new sensible statistical procedures. I am always open to other statistical frameworks and to address scientific and societal problems.

Please go to my Google Scholar profile for the most up-to-date list of my publicly available scholarly work.

### Contact information:

*Email:* gmena AT andrew DOT cmu DOT edu

*Office Address:* 229i Baker Hall, 5000 Forbes Avenue, Pittsburgh, PA 15213

### News/Highlights

- Dec 18, 2023. Preprint (draft) of my new work On model-based clustering with entropic optimal transport’ available here. It will appear on the ArXiv in early 2024.
- Dec 18-19, 2023. I am giving an invited talk on the Optimal Transport session at the 2023 IMS International Conference on Statistics and Data Science (ICSDS) in Portugal, Lisbon.
- Sept 2023. I started my new position as Assistant Professor in the Deparment of Statistics and Data Science at CMU.
- Dec 2022. On Dec 13th I am giving a talk for (Stochastics Seminar). in the the Department of Mathematics, Aarhus University
- May 2022. I was named a ‘Chilean Leader’ By Revista Sabado (from El Mercurio), main newspaper in Chile) and Universidad Adolfo Ibañez. As a result, I was spotlighted by the Columbia Global Centers and the Statistics Department at Columbia University.
- April-May 2022. I am attending the Decision Making and Uncertainty long program at the IMSI institute, Chicago, where I will also present my most recent work in the Applied Optimal Transport workshop (May 16th-May 20th, 2022).
- December 13th, 2021. I am presenting our work on Sinkhorn EM algorithm as a spotlight talk at the Optimal Transport and Machine Learning Workshop at NeuRIPS.
- Nov 26th, 2021. I am presenting my work on the Sinkhorn Algorithm in the Seminar in Statistics at Collegio Carlo Alberto, Torino, Italy.
- November, 2021. I am presenting my work on Entropic Optimal Tranport in the Schrödinger Problem and Mean-field PDE Systems: Computational and Theoretical Advances at CIRM, Marseille, France.
- October 2021 I am presenting my work on inference of stratified infection fatality rates [in the End-to-end Bayesian learning international conference](https://bayesatcirm.github.io/ at at CIRM, Marseille, France.
- April 28th,2021 our work on the impact of COVID-19 in Santiago, Chile has been published in Science. This is joint work with Pamela Martinez (co-first author), Ayesha Mahmud, Pablo Marquet, Caroline Buckee and Mauricio Santillana.
- Jan 07th,2021 our work on neural identification in colorful C.elegans worms has been published in Cell. This is joint work with Eviatar Yemini, Albert Lin, Amin Nejatbakhsh, Erdem Varol, Ruoxi Sun, Aravinthan DT Samuel, Liam Paninski, Vivek Venkatachalam and Oliver Hobert
- July 7th,2020 I gave a talk at Optimal Transport: Regularization and Applications Workshop.
- July 1st,2020 Started a new position as I am a Florence Nightingale Bicentennial Fellow and Tutor in Computational Statistics and Machine Learning at University of Oxford.
- June 30th, 2020, our new work on Sinkhorn EM algorithm (joint with Amin Nejatbakhsh, Erdem Varol, Jonathan Niles-Weed) is available as a preprint.

### Other

- Since September 2021, I am part of the Equality, Diversity and Inclusion Committee at the Department of Statistics, University of Oxford.
- Since July 2020, I co-organize the Oxford Computational Statistics and Machine Learning (OxCSML) Seminar