Manoj Kumar
Amsterdam, North Holland, Netherlands
Summary
🤩
Rockstar🎓
Top SchoolManoj Kumar is a machine learning engineer and research practitioner with 14 years of experience building ML systems, contributing to open-source scientific Python projects, and advancing generative modeling during a residency at Google Brain. He has production and research experience at Google (including Tensor2Tensor and Google Research) working on stochastic video generation, normalizing flows, and architecture search, and has a track record of open-sourcing model code for reproducible research. Earlier contributions span core tooling in the Python ML ecosystem—improvements to scikit-learn, Spark MLlib, IPython, and libraries like scikit-optimize and lightning—demonstrating both low-level algorithmic work and developer-facing infrastructure. Based in Amsterdam, he blends research rigor with practical engineering: examples include refactoring SAVP video models, adding sparse-input support and persistence for Spark GMM/LDA, and stabilizing IPython’s timeit internals. Notably, his background in mechanical engineering and data science underpins a penchant for performant numerical implementations and scalable ML tooling that bridge research and production.
14 years of coding experience
5 years of employment as a software developer
Master's degree, Data Science, Master's degree, Data Science at NYU Center for Data ScienceBachelor of Engineering (B.E.), Mechanical Engineering, Bachelor of Engineering (B.E.), Mechanical Engineering at Birla Institute of Technology and Science, Pilani - Goa Campus
Stackoverflow
961reputation
119kreached
1answer
36questions
Github Skills (45)
apache-spark10
parallelization10
word2vec10
python10
data-science10
model-driven10
scikit10
video-processing10
machine-learning10
pca10
bash10
ipython10
travis-ci10
lda10
cicd10
Programming languages (9)
Github contributions (5)
scikit-optimize/scikit-optimize
Mar 2016 - Sep 2017
Sequential model-based optimization with a `scipy.optimize` interface
Role in this project:
DevOps Engineer
DevOps EngineerContributions:2 releases, 262 commits, 177 PRs in 1 year 6 months
Contributions summary:Manoj's primary contributions revolved around setting up and configuring the CI/CD pipeline for the project. They focused on integrating Travis CI and making the tests work. The user also made changes to install necessary dependencies and install and configure scikit-learn. The user's contributions impacted the project by getting the build and testing pipeline working, and integrating dependencies for the project.
pythonbayesoptscipymodel-based-optimizationbayesian-optimization
tensorflow/tensor2tensor
Sep 2017 - Jan 2020
Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.
Role in this project:ML Engineer
ML EngineerContributions:89 commits, 1 comment in 2 years 3 months
Contributions summary:Manoj contributed to the development and improvement of the NextFrameStochastic model within the tensor2tensor library. They implemented changes to the data generator to encode the end of sentences using <EOS> tags, logged the number of ground truth frames, and added components for the Stochastic Adversarial Video Prediction (SAVP) model. Their work also included refactoring parts of the codebase to decouple the SAVP model. The contributions suggest a focus on video prediction and adversarial training techniques.
deep-learningmachine-learningmachine-translationreinforcement-learningtpu
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