Felix Laumann
Senior Director - Machine Learning at Mimecast
London, England, United Kingdom
Summary
👤
Senior🎓
Top SchoolFelix Laumann is a machine learning leader and entrepreneur with nine years of experience building language-first AI products and teams from research to production. As co-founder and former CEO of NeuralSpace he scaled enterprise-grade speech and text APIs across 100+ languages, driven by fieldwork in Southern Asia, Africa and the Middle East that shaped his focus on linguistic inclusivity. Now Senior Director of Machine Learning at Mimecast, he blends rigorous PhD-level expertise in mathematics and statistics with practical interests in kernel methods, causal discovery and NLP to solve real-world problems at scale. Felix’s background spans academia and industry—teaching at Imperial College London while finishing a PhD—and technical foundations in engineering from DTU, Aarhus and Chemnitz, giving him a rare mix of theoretical depth and product-led execution.
9 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy - PhD Mathematics and Statistics, Doctor of Philosophy - PhD Mathematics and Statistics at Imperial College LondonBachelor of Science - BS Mechanical Engineering, Bachelor of Science - BS Mechanical Engineering at Technische Universität ChemnitzMaster of Science - MS Engineering, Master of Science - MS Engineering at Aarhus BSS - Aarhus UniversityTechnical University of Denmark
German, English
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (10)
coco9
convolutional-neural-networks9
api8
bayesian-inference8
statistics7
python6
pytorch5
mathematics5
machine-learning4
kernel-methods1
Programming languages (2)
Github contributions (5)
felix-laumann/SDG-networks
Aug 2020 - Nov 2022
Contributions:331 commits, 326 pushes, 2 branches in 2 years 3 months
felix-laumann/Bayesian_CNN_ContinualLearning
May 2018 - Jun 2018
Interpreting Bayesian inference as continual learning with a CNN
Contributions:45 commits, 41 pushes, 2 branches in 20 days
bayesian-inferenceconvolutional-neural-networkscontinual-learning
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