Gonzalo E. Mena bio photo

Gonzalo E. Mena

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About


I am 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, working in the lab of Pierre Jacob.
Previously, I did my PhD in Statistics at Columbia University mentored by Liam Paninski.
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 and pressing problems in life sciences 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 there 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. My CV is available here

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