Alan Le Goallec (0000-0002-4257-1851)

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I graduated from Harvard University with a PhD in Systems Biology in May 2021. My PhD thesis was about studying the multidimensionality of aging with deep learning-based age predictors. https://www.multidimensionality-of-aging.net/
Before that I did my undergraduate in France at Ecole Nationale des Ponts et Chaussées, where I majored in Mathematics and Physics, before completing a M.Phil in Bioengineering at the University of Cambridge.

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Harvard Medical School: Boston, MA, US

2015-09 to 2021-08 | Research Assistant (Department of Biomedical Informatics)

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Alan Le Goallec

https://orcid.org/0000-0002-4257-1851

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Employment (6)

Harvard Medical School: Boston, MA, US

Harvard Medical School: Boston, MA, US

University of Cambridge: Cambridge, Cambridgeshire, GB

Massachusetts Institute of Technology: Cambridge, MA, US

University of Cambridge: Cambridge, Cambridgeshire, GB

Tongji University: Shanghai, Shanghai, CN

Education and qualifications (3)

Harvard University: Cambridge, MA, US

University of Cambridge: Cambridge, Cambridgeshire, GB

Ecole Nationale des Ponts et Chaussees: Paris, Île-de-France, FR

Professional activities (3)

Crimson EMS: Cambridge, MA, US

Harvard University and Massachusetts Institute of Technology: Cambridge, MA, US

The Lynch Foundation: Boston, Massachusetts, US

Works (13)

Using deep learning to predict abdominal age from liver and pancreas magnetic resonance images

Comparing the genetic and environmental architecture of blood count, blood biochemistry and urine biochemistry biological ages with machine learning

Predicting age from hearing test results with machine learning reveals the genetic and environmental factors underlying accelerated auditory aging

Deep learning of fundus and optical coherence tomography images enables identification of diverse genetic and environmental factors associated with eye aging

Using deep learning to predict age from liver and pancreas magnetic resonance images allows the identification of genetic and non-genetic factors associated with abdominal aging

Predicting age from 100,000 one week-long 100Hz wrist accelerometer records of physical activity

Using deep learning to predict brain age from brain magnetic resonance images and cognitive tests reveals that anatomical and functional brain aging are phenotypically and genetically distinct

Using deep learning to analyze the compositeness of musculoskeletal aging reveals that spine, hip and knee age at different rates, and are associated with different genetic and non-genetic factors

Predicting arterial age using carotid ultrasound images, pulse wave analysis records, cardiovascular biomarkers and deep learning

Dissecting heart age using cardiac magnetic resonance videos, electrocardiograms, biobanks, and deep learning

Analyzing the multidimensionality of biological aging with the tools of deep learning across diverse image-based and physiological indicators yields robust age predictors

A systematic machine learning and data type comparison yields metagenomic predictors of infant age, sex, breastfeeding, antibiotic usage, country of origin, and delivery type

Age-dependent co-dependency structure of biomarkers in the general population of the United States