Harish Rajagopal
Quantitative Technologist
Zurich, Zurich, Switzerland
Summary
👤
Senior🎓
Top SchoolHarish Rajagopal is a Quantitative Technologist and software engineer with nine years of experience building ML-driven systems and backend platforms, currently based in Zurich. He holds an MSc in Computer Science from ETH Zürich with a focus on machine learning and has blended research experience (robust image hashing, differentiable plasticity) with production work, from distributed load-testing pipelines to SAML single-sign-on and customer portals. Harish contributes to open-source ML tooling—improving reproducibility in hyperopt-sklearn by fixing RNG and seeding issues—underscoring his attention to reliable experimentation. Comfortable across Python, TypeScript, PHP and infra tooling (Prometheus, Grafana, OpenTelemetry), he moves models from prototype to scalable services. Colleagues would note his mix of academic rigor and pragmatic engineering, and that he often surfaces subtle correctness issues (e.g., random state handling) that materially improve reliability.
9 years of coding experience
3 years of employment as a software developer
Indian Institute of Technology KanpurMaster of Science - MS, Computer Science, Master of Science - MS, Computer Science at ETH Zürich
English, German, Hindi, Tamil, Marathi
Stackoverflow
1,232reputation
149kreached
7answers
3questions
tensorflow
top-5%
Github Skills (17)
python10
scikit10
machine-learning10
numpy10
hyperparameter-optimization10
scikit-learn10
rep9
tensorflow9
repr9
testing8
github-release6
glm6
timeout6
autoconf6
github6
Programming languages (18)
Github contributions (5)
hyperopt/hyperopt-sklearn
Nov 2021 - May 2022
Role in this project:
ML Engineer
ML EngineerContributions:1 review, 6 commits, 1 PR in 5 months
Contributions summary:Harish's contributions primarily focus on ensuring the correct use of random number generators and fixing related issues within the hyperparameter optimization framework. They addressed seed initialization problems in tests, updated the code to use modern NumPy random number generation, and restored compatibility for integer random states. These changes directly impact the reproducibility and reliability of the hyperparameter search process, core functionality of the library.
sklearnoptimizationhyper-parameter-optimizationmachine-learningparameter
rharish101/vim-config
May 2019 - Jan 2023
My configuration for vim
Contributions:90 commits, 82 pushes, 1 branch in 3 years 8 months
dotfilesneovimvimvim-configuration
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