Kartik Dutt
Software Engineer II (L62) at Microsoft
Delhi, India
Summary
🤩
Rockstar🎓
Top SchoolKartik Dutt is a software engineer with 8 years of experience building high-throughput, reliable rendering and export systems at Microsoft, currently at L62 working on Designer rendering infrastructure. He combines deep expertise in TypeScript, Node.js, C# and performance engineering to run services handling millions of daily requests with 99.9%+ QoS, and has delivered inventive optimizations like memory-optimized GIF encoders and CPU-backed rendering to reduce GPU costs. An open-source contributor and GSoC alumnus who worked on mlpack’s ANN module (adding “Same”/“Valid” padding to transposed convolutions), he brings practical machine learning and computer-vision knowledge to production engineering. Based in Delhi, Kartik also mentors in the mlpack community, and is steadily expanding his skills into cloud platforms, DevOps and compiler design.
7 years of coding experience
3 years of employment as a software developer
Bachelor of Technology - BTech Electronics and Communications Engineering, Bachelor of Technology - BTech Electronics and Communications Engineering at Delhi Technological University (Formerly DCE)Delhi Public School - R. K. Puram
English, Hindi, French, Japanese
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (7)
machine-learning10
c-language10
deep-learning10
cprogramming-language10
testing10
cplus9
cpp9
Programming languages (8)
Github contributions (5)
mlpack/mlpack
Dec 2019 - Feb 2021
mlpack: a fast, header-only C++ machine learning library
Role in this project:
ML Engineer
ML EngineerContributions:102 reviews, 188 commits, 81 PRs in 1 year 2 months
Contributions summary:Kartik primarily contributed to the `mlpack/mlpack` repository by implementing and testing features within the artificial neural network (ANN) module. Specifically, they focused on adding and testing "Valid" and "Same" padding options to the Transposed Convolution layer. Their work involved modifying the layer's implementation and test files and included the addition of test cases to validate the new functionality. The commits show detailed code changes and test cases verifying the new padding options.
regressionheaderdeep-learningscientific-computingc-plus-plus
kartikdutt18/CNN-LSTM-for-Activity-Recognition
Jan 2019 - Mar 2019
Contributions:24 commits, 23 pushes, 1 branch in 2 months
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