Shikhar Bhardwaj
Software Engineer at Ubicloud
Amsterdam, North Holland, Netherlands
Summary
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Rockstar🎓
Top SchoolShikhar Bhardwaj is a software engineer with 11 years of experience, currently based in Amsterdam and working at Ubicloud after a multi-year tenure at Instabase. He combines strong backend and numerical computing skills with practical ML engineering experience, notably contributing parallel SGD and sparse SVM implementations to the well-regarded mlpack/ensmallen and mlpack C++ libraries. Comfortable in performance-sensitive C++ code, he has improved numerical output handling and testing infrastructure, showing attention to both correctness and usability. A daily learner who values incremental breakthroughs, he maintains a public portfolio at bluefog.me and brings a habit of documenting and sharing improvements that benefit open-source and internal projects alike.
11 years of coding experience
5 years of employment as a software developer
St. Xavier School - DelhiBachelor’s Degree, Bachelor’s Degree at Delhi College of Engineering
Hindi, English
Stackoverflow
1,894reputation
157kreached
56answers
4questions
javascript
top-5%
Github Skills (27)
algorithm10
c-language10
numerical10
computation10
optimizers10
machine-learning10
numerics10
deep-learning10
parallel-computing10
optimisation10
cprogramming-language10
numeric10
optimization10
data-structure9
javascript9
Programming languages (13)
Github contributions (5)
mlpack/ensmallen
Jun 2017 - Oct 2017
A header-only C++ library for numerical optimization --
Role in this project:
Back-end Developer
Back-end DeveloperContributions:89 commits in 4 months
Contributions summary:Shikhar implemented and refactored the parallel Stochastic Gradient Descent (SGD) optimizer within the `ensmallen` library, which is a header-only C++ library for numerical optimization. Their contributions included implementing the initial parallel SGD algorithm and the sparse SVM loss function. The user also focused on integrating stepsize decay policies, termination conditions, and updated documentation. These changes improved the optimizer's functionality and performance, aligning with the library's goals of numerical optimization.
optimization-methodsoptimization-algorithmsnumerical-optimizationheader-onlycplusplus
mlpack/mlpack
Dec 2016 - Oct 2017
mlpack: a fast, header-only C++ machine learning library
Role in this project:Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:135 commits, 10 PRs, 85 comments in 10 months
Contributions summary:Shikhar primarily focused on improving the mlpack library's numerical computation capabilities, especially related to output formatting and printing. They implemented fixes for printing numerical values with custom precision, especially for Armadillo objects, ensuring correct alignment. The user added tests to verify these improvements, as well as refactoring the test structure to support different test scenarios. Further contributions included the implementation of parallel stochastic gradient descent (SGD) optimization and related components and functions.
regressionheaderdeep-learningscientific-computingc-plus-plus
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