معرفی
Gonzalo Mena is an Assistant Professor in the Department of Statistics & Data Science at Carnegie Mellon University's Dietrich College of Humanities and Social Sciences. His research focuses on applying statistical and machine learning methods to life sciences problems, particularly in neuroscience and epidemiology. He has prior experience as a Florence Nightingale Bicentennial Fellow and Tutor at the University of Oxford, a Postdoctoral Fellow at Harvard University, and holds a Ph.D. in Statistics from Columbia University and a B.Sc. in Mathematical Engineering from Universidad de Chile.
His work bridges cutting-edge neurotechnology data analysis with large-scale epidemiological surveillance, emphasizing valid inference from biased datasets. He is a leader in statistical optimal transport, developing methodologies to measure distributional distances and inform scientific discovery. His research spans the DELPHI Lab Group and intersects with computational neuroscience, genomics, and public health policy.
Key research areas include Alzheimer's disease modeling via multimodal cell atlases, Bayesian methods for neurodegenerative dynamics, and pandemic impact analysis (e.g., socioeconomic disparities in Covid-19 outcomes). His recent publications highlight interdisciplinary applications of optimal transport theory and permutation inference in neuroscience data analysis.
Mena has contributed to foundational statistical frameworks for population size estimation and neuronal identification in C. elegans. His work combines methodological innovation with real-world applications in healthcare and public health surveillance.
