Bhaskar Mitra
Principal Researcher at Microsoft
Montreal, Quebec, Canada
Summary
🤩
Rockstar🎓
Top SchoolBhaskar Mitra is a Principal Researcher at Microsoft in Montreal with 10+ years of experience advancing information retrieval and applied machine learning in production-scale systems. He progressed through engineering and research roles at Microsoft since 2006, blending deep software engineering expertise with research rigor from doctoral work at UCL. Bhaskar has contributed to high-impact open-source tooling for deep learning—working on core functionality and sparse-matrix kernels in the Microsoft Cognitive Toolkit—highlighting his ability to optimize low-level ML infrastructure. Comfortable bridging theory and practice, he ships robust back-end solutions and improves data readers and matrix operations that power large-scale models. Based in Montreal, he pairs academic depth with a decade of product-focused engineering, making him effective at turning IR research into deployable systems.
10 years of coding experience
16 years of employment as a software developer
BE Computer Science, BE Computer Science at Visvesvaraya Technological UniversityBachelor of Engineering (BE) Computer Science, Bachelor of Engineering (BE) Computer Science at People's Education Society Institute of TechnologyUniversity College London
Stackoverflow
19reputation
0reached
1answer
0questions
Github Skills (13)
cuda10
gpu-programming10
sparse-matrix10
machine-learning10
cntk10
deep-learning10
c-language10
cprogramming-language10
pandas6
jupyter-notebook6
python6
dotnet-core4
csharp4
Programming languages (6)
Github contributions (5)
microsoft/CNTK
Aug 2015 - Sep 2016
Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:316 commits, 34 pushes, 4 branches in 1 year
Contributions summary:Bhaskar primarily contributed to the core functionality of the Microsoft Cognitive Toolkit (CNTK) focusing on deep learning model implementations. They addressed bug fixes related to matrix operations, specifically in the DiagonalNode. The user also implemented the ColumnSlice function for sparse matrices and applied it to existing code, which involved changes to both CPU and GPU kernels related to sparse matrix operations used in the machine learning framework. Additionally, the user contributed to the SparsePCReader, enhancing the reader's layout handling and functionality.
pytorchpythondeep-learningc-plus-plusmachine-learning
microsoft/TREC-2019-Deep-Learning
May 2019 - Dec 2020
Website for the TREC Deep Learning Track 2019
Contributions:23 commits, 1 PR, 22 pushes in 1 year 7 months
deep-learningmachinelearningmachine-learning
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