Kumar Shridhar
PhD Candidate
Zurich, Zurich, Switzerland
Summary
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RockstarKumar Shridhar is a PhD candidate at ETH Zürich with nine years of experience building machine learning and NLP systems. He blends research rigor with hands-on engineering, contributing practical Bayesian deep learning implementations such as a PyTorch Bayesian CNN library that applies Bayes by Backprop. Based in Zurich, he focuses on backend development and model implementation, refactoring codebases to improve structure and reproducibility. His work suggests a knack for translating probabilistic ML research into maintainable, production-ready code. Colleagues can expect a researcher-engineer who balances theoretical depth with pragmatic software practices.
9 years of coding experience
Github Skills (7)
bayesian-network10
neural-network10
pytorch10
convolutional-neural-networks10
python10
variational-inference9
image-recognition8
Programming languages (7)
Github contributions (5)
kumar-shridhar/PyTorch-BayesianCNN
Jul 2018 - Feb 2021
Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:221 commits, 11 PRs, 196 pushes in 2 years 6 months
Contributions summary:Kumar made changes to the file structure and refactored code within the repository. They also added several Bayesian Convolutional Neural Network models in PyTorch, indicating a focus on implementing and modifying machine learning models. These changes demonstrate involvement in backend code development, as well as implementing ML models based on Bayes by Backprop.
bayesian-inferencebayesian-networkspymc3variational-inferencetemporal-data
kumar-shridhar/Online-Toxicity-Detection
May 2020 - Jun 2020
Contributions:48 commits, 3 pushes in 27 days
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