Manjunath Bhat
Systems Analyst at KRSSG
Summary
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RockstarManjunath Bhat is a systems analyst with eight years of engineering experience and a strong focus on machine learning tooling and automatic differentiation. He contributed to FluxML's flagship projects, Zygote.jl and Flux.jl, adding adjoints, custom @nograd annotations, new layers like AlphaDropout, and Float64 support for depthwise convolutions—work that helped harden gradient correctness and numerical robustness in a widely used Julia ML stack. A GSoC'19 alumnus and Summer Analyst at Goldman Sachs, he blends research-grade open-source contributions with practical finance-industry exposure. Known for meticulous testing and edge-case fixes, he brings a developer-first mindset to improving core library behavior rather than just building wrappers. Comfortable navigating both low-level AD semantics and higher-level model primitives, he’s the kind of engineer who improves tooling so teams can iterate faster and more reliably.
8 years of coding experience
Github Skills (13)
neural-network10
automatic-differentiation10
machine-learning10
deeplearning-ai10
fluxor10
deep-learning10
artificial-neural-networks10
julia10
gradient10
flux10
data-science9
julialang9
compiler9
Programming languages (7)
Github contributions (5)
FluxML/Flux.jl
Mar 2019 - Jan 2020
Relax! Flux is the ML library that doesn't make you tensor
Role in this project:
ML Engineer
ML EngineerContributions:82 commits, 12 PRs, 14 pushes in 10 months
Contributions summary:Manjunath primarily contributed to the development and improvement of machine learning-related functionalities within the Flux.jl library. Their work involved adding and exporting new layers like AlphaDropout, crucial for Self-Normalizing Neural Networks. The user fixed issues related to data types, specifically adding support for Float64 in DepthwiseConv, ensuring the library's robustness. Additionally, they introduced new loss functions and improved existing ones, showing a focus on the core machine learning capabilities of the library.
ml-librarythe-human-braindata-sciencedeep-learningneural-networks
FluxML/Zygote.jl
Jul 2019 - Aug 2019
21st century AD
Role in this project:ML Engineer
ML EngineerContributions:6 commits, 5 PRs, 13 comments in 29 days
Contributions summary:Manjunath primarily focused on implementing and testing features related to automatic differentiation within the Zygote.jl repository. They introduced `@nograd` annotations to prevent differentiation of certain functions, such as integer division. Furthermore, they added adjoints for functions like `dropdims` and made modifications to existing adjoint rules, indicating a focus on extending Zygote's capabilities and ensuring correct gradient calculations. They also added tests for the `dropdims` function, verifying the accuracy of the gradients.
control-flowautomatic-differentiationmachine-learningjulia-compilerjulia
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