Shikhar Jaiswal
Machine Learning Engineer at University of Toronto
Delhi, India
Summary
🤩
Rockstar🎓
Top SchoolShikhar Jaiswal is a Machine Learning Engineer with a decade of experience building high-performance ML and software systems, blending research-grade rigor with production-focused engineering. He has a research background (CS PhD at University of Toronto, previously at Microsoft Research) and strong C++ systems expertise demonstrated by contributions to SymEngine and mlpack, plus practical ML work on EdgeML for resource-constrained devices. His open-source contributions span symbolic math, neural network layers, approximate nearest neighbor algorithms (Vamana/VamanaPQ) and image-processing layers, showing fluency across theory, algorithms and low-level implementation. Based in Delhi, he combines algorithmic depth with pragmatic optimizations and testing discipline—an engineer who moves ideas from mathematical correctness to efficient, deployable code.
9 years of coding experience
Bachelor of Technology (B.Tech.), Computer Science and Engineering, Bachelor of Technology (B.Tech.), Computer Science and Engineering at Indian Institute of Technology, PatnaHigh School, High School at Tagore International SchoolPrimary and Middle School, Primary and Middle School at The Pinnacle School
Hindi, English
Stackoverflow
125reputation
1kreached
0answers
4questions
Github Skills (43)
algorithm10
algorithms10
computer-algebra10
pytorch10
benchmark10
c-language10
approximate10
python10
nearest-neighbors10
mathematics10
machine-learning10
benchmarking10
machine-learning-algorithms10
math10
implement10
Programming languages (13)
Github contributions (5)
microsoft/EdgeML
Jun 2020 - Feb 2022
This repository provides code for machine learning algorithms for edge devices developed at Microsoft Research India.
Role in this project:
ML Engineer
ML EngineerContributions:31 reviews, 63 commits, 21 PRs in 1 year 8 months
Contributions summary:Shikhar primarily contributed to the implementation and testing of machine learning algorithms for edge devices. They refactored existing code, added new implementations of MBConv, and performed optimizations. The user's work included adding unit tests and addressing code quality issues. They also integrated face detection models and optimized core algorithms.
classifieredge-machine-learningsensortensorflowmicrosoft
mlpack/mlpack
Jan 2018 - May 2020
mlpack: a fast, header-only C++ machine learning library
Role in this project:ML Engineer
ML EngineerContributions:87 commits, 87 PRs, 55 pushes in 2 years 4 months
Contributions summary:Shikhar's commits focus on minor fixes and improvements to the mlpack library, particularly in the area of artificial neural networks. They addressed issues in the SigmoidCrossEntropyError layer, providing corrections and enhancements to the forward and backward functions. Furthermore, the user introduced a Bilinear Interpolation layer, indicating work on image processing or data transformation techniques within the library's deep learning capabilities. These contributions suggest a focus on expanding and refining the library's functionalities related to neural network architectures and related methods.
regressionheaderdeep-learningscientific-computingc-plus-plus
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