Abhinav Moudgil
PhD Candidate
Montreal, Quebec, Canada
Summary
👤
Senior🎓
Top SchoolAbhinav Moudgil is a PhD candidate and experienced software engineer with 11 years of hands-on work in deep learning, AI, and open-source C++ libraries. Based in Montreal and affiliated with Mila, he blends research rigor with production-minded engineering, contributing optimizers like AdaGrad to well-known header-only projects mlpack and ensmallen. His contributions show practical expertise in numerical optimization, API design, testing, and iterative bug-fixing—skills that bridge academic algorithms and robust library implementations. Energetic about community-driven software, he proves that careful low-level C++ engineering can materially accelerate ML research and tooling.
11 years of coding experience
Concordia University
Hindi, English
Stackoverflow
25reputation
2kreached
0answers
6questions
Github Skills (24)
algorithm10
c-language10
machine-learning-models10
optimizers10
machine-learning10
deep-learning10
cplus10
optimisation10
cpp10
cprogramming-language10
optimization10
minimize9
minification9
test-automation8
regression8
Programming languages (9)
Github contributions (5)
mlpack/ensmallen
Mar 2017 - May 2017
A header-only C++ library for numerical optimization --
Role in this project:
ML Engineer
ML EngineerContributions:46 commits in 2 months
Contributions summary:Abhinav primarily contributed to the implementation and testing of the AdaGrad optimization algorithm within the `ensmallen` library. They refactored and moved the AdaGrad update policy, added a new AdaGrad optimizer class, and updated corresponding tests. The user's work involved modifying existing code files and adding new ones, focusing on numerical optimization algorithms.
optimization-methodsoptimization-algorithmsnumerical-optimizationheader-onlycplusplus
mlpack/mlpack
Mar 2017 - May 2017
mlpack: a fast, header-only C++ machine learning library
Role in this project:ML Engineer
ML EngineerContributions:55 commits, 10 PRs, 85 comments in 2 months
Contributions summary:Abhinav primarily contributed to the implementation of the AdaGrad optimizer within the mlpack library. Their work involved adding the AdaGrad optimizer class, associated update policies, and unit tests to ensure correct functionality. They made iterative changes, including bug fixes, comment improvements, and adjustments to variable names. This involved integrating the AdaGrad optimizer with the existing SGD framework.
regressionheaderdeep-learningscientific-computingc-plus-plus
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