Christopher Beckham is a machine learning researcher and PhD candidate at MILA with 11 years of experience focused on generative modelling, few-shot learning, and model-based optimization. He combines academic rigor with industry impact through visiting research roles at ServiceNow, NVIDIA, and startups like Alpaca and Maket, and has published work on 3D visual question answering and annotation-efficient medical image segmentation. Comfortable bridging theory and application, he co-advises projects on generative approaches for fashion and meta-learning and has contributed to principled evaluation methods for generative systems. Based in Montreal, he brings a track record of translating minimal-supervision ideas into practical representation learning techniques that scale across domains.
11 years of coding experience
6 years of employment as a software developer
Master’s Degree, Computer Engineering, Master’s Degree, Computer Engineering at Université de Montréal - École Polytechnique de Montréal
Bachelor’s Degree, Computer Science, Hons (First Class), Bachelor’s Degree, Computer Science, Hons (First Class) at The University of Waikato
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Christopher Beckham - Machine Learning Researcher at Alpaca