Gonzalo Mena
Assistant Professor
Contact
Bio
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 Ph.D. in Statistics at Columbia University, advised by Liam Paninski. I am originally from Santiago, Chile. There, I obtained by B.Sc. in Mathematical Engineering at Universidad de Chile.
Specific Research Interests
- 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.
- As also have a stand-alone interest in the emerging field of statistical optimal transport. I derive and study statistical procedures arising from the mathematical framework of optimal transport, a mathematical framework that provides us with powerful tools to e.g., measure the distance between distributions, and turn one distribution into another.