Javid Lakha
Machine Learning Researcher at Harvard John A. Paulson School of Engineering and Applied Sciences
Cambridge, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolJavid Lakha is a machine learning researcher and engineer with eight years of experience building production ML systems and full-stack applications, now based at Harvard SEAS. He has led ML research and engineering at startups and scaleups—including five years as Lead ML Research Engineer at Legatics and founding ML roles at Spherical Defense—bridging research, deployment, and domain-specific product work. At Harvard he teaches MLOps and classic CS/AI courses while pursuing a Master of Engineering in Computational Science and engaging with robotics coursework through MIT, reflecting a blend of academic rigor and practical tooling experience. His background in quantitative analysis and early freelancing for clinical clients gives him a pragmatic edge in turning complex models into reliable, user-facing systems.
8 years of coding experience
14 years of employment as a software developer
Cross-Registered Harvard Student, Robotics, Cross-Registered Harvard Student, Robotics at Massachusetts Institute of TechnologyBachelor of Arts, Philosophy, Politics and Economics, Bachelor of Arts, Philosophy, Politics and Economics at University of OxfordMaster of Engineering, Computational Science and Engineering, Master of Engineering, Computational Science and Engineering at Harvard University
Stackoverflow
73reputation
1kreached
0answers
1question
Github Skills (17)
discord-bot9
metadata8
to-do8
the-matrix7
rust6
matrix6
python6
matrix-profile5
chat5
machine-learning5
nextjs5
chatgpt4
typescript4
ai4
chatbot4
Programming languages (6)
Github contributions (5)
javidlakha/matrix-profile
Dec 2017 - Jan 2020
A Python implementation of the matrix profile algorithm
Contributions:3 commits, 1 PR, 5 pushes in 2 years 1 month
pythonthe-matrixpython-implementationmatrixmatrix-profile
javidlakha/bayesian-flow-networks
Sep 2023 - Oct 2023
A Python implementation of the Bayesian Flow Networks paper
Contributions:6 PRs, 19 pushes, 4 branches in 18 days
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