Christopher Bonnett

Christopher Bonnett is a seasoned Machine Learning Engineer with extensive experience in research, data science, and cosmology.

Currently, Christopher holds the position of Machine Learning Engineer at ZOE, where he continues to leverage his expertise in machine learning.

Prior to his current role, Christopher served as a Senior Machine Learning Researcher at Alpha-I, focusing on Bayesian Deep Learning for dynamical systems.

During his tenure as a Fellow at Insight Data Science in NYC, Christopher excelled in implementing automated classification pipelines and developing object recognition systems using cutting-edge technologies like Convolutional Neural Nets, NLP, Keras, and Scikit-learn.

Christopher's post-doc experience at the Institute for High Energy Physics in Barcelona made significant contributions to the field of cosmology by producing galaxy distance measurements and playing a key role in uncovering the accelerated expansion of the universe within the Dark Energy Survey.

As a Marie Curie Post-doc at the Institute for Space Sciences in Barcelona, Christopher conducted groundbreaking research on galaxy shapes, employed Neural Networks for galaxy distance estimation, and won the prestigious Euclid galaxy distance data challenge.

Christopher's educational background is impressive, with a Ph.D. in Cosmology from Université Pierre et Marie Curie in Paris and a B.A./M.S. in Astronomy from Leiden University in the Netherlands.