Rishav Chourasia
Head Of Product at Betterdata
Singapore
Summary
👤
Senior🎓
Top SchoolRishav Chourasia is Head of Product based in Singapore with a decade of experience at the intersection of machine learning, privacy, and production systems. He holds a PhD in AI from NUS where his research blended information theory and privacy, and he’s translated that academic rigor into applied privacy work at Google Brain and multiple roles at Betterdata. Rishav has hands-on ML engineering experience contributing bug fixes and integration examples to well-known open-source projects like Chainer and Optuna, showing deep familiarity with gradient handling and hyperparameter optimization. He’s comfortable moving models from research to reliable services—evidenced earlier by building low-latency fraud-evaluation platforms at Amazon and production privacy audits and tooling at Betterdata. Colleagues describe him as someone who bridges careful theoretical thinking with pragmatic product delivery, and he often surfaces subtle implementation bugs that improve long-term system robustness.
10 years of coding experience
4 years of employment as a software developer
High School Diploma Pure Science, High School Diploma Pure Science at Delhi Public School - IndiaBachelor’s Degree Computer Science, Bachelor’s Degree Computer Science at Indian Institute of Technology, GuwahatiDoctor of Philosophy - PhD Artificial Intelligence, Doctor of Philosophy - PhD Artificial Intelligence at National University of Singapore
English, Hindi
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (15)
neural-network10
hyperparameter-optimization10
keras10
mxnet10
machine-learning10
pytorch10
deep-learning10
python10
optuna10
chainer10
testing9
numpy9
tensorflow9
cuda6
gpu6
Programming languages (8)
Github contributions (5)
optuna/optuna
Mar 2019 - Jun 2019
A hyperparameter optimization framework
Role in this project:
ML Engineer
ML EngineerContributions:32 commits, 7 PRs, 56 comments in 3 months
Contributions summary:Rishav contributed significantly to the project by implementing and testing example code related to hyperparameter optimization for MXNet, Keras, and PyTorch deep learning models within the Optuna framework. They added pruning callbacks and integrated the framework with these ML libraries, including an example using TensorFlow eager execution, and a PyTorch MNIST example. Furthermore, the user fixed code style issues and resolved errors, improving code quality and ensuring the examples ran correctly.
pythonoptimization-frameworkparallelhyperparameteroptimization
chainer/chainer
Apr 2019 - Apr 2019
A flexible framework of neural networks for deep learning
Role in this project:ML Engineer
ML EngineerContributions:8 commits, 1 PR, 9 comments in 3 days
Contributions summary:Rishav focused on debugging and improving the Chainer deep learning framework, specifically addressing issues related to gradient handling in unchained variables. Their contributions included bug fixes and the addition of test cases to verify the correct behavior of the framework. They refactored code and made style adjustments to enhance readability and maintainability, demonstrating expertise in the inner workings of the framework.
cudapythonmxnetcaffe2flexible-framework
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