Vandana kannan
San Jose, California, United States
Summary
🤩
RockstarVandana kannan is a software development engineer at Amazon AI with a decade of industry experience spanning graphics, display driver development, and AI/ML tooling. She holds a Master’s in Computer Science from SJSU and brings deep expertise in Python and C++, Unity, and cross-platform display stack work for Windows and Linux, including eDP/MIPI DSI bring-up and HDMI compliance. Prior to Amazon she spent six years as a graphics software engineer—over five focused on Intel platform display drivers—and contributed to open-source i915 features like Dynamic Refresh Rate Switching. On GitHub she has improved the MXNet ecosystem by extending ONNX export capabilities and hardening image data handling, reflecting a blend of systems-level engineering and machine-learning tooling. Based in San Jose, she combines low-level display driver pragmatism with a strong interest in computer vision, AR/VR, and genetic algorithms, making her comfortable moving between kernel work and higher-level AI systems.
10 years of coding experience
Github Skills (8)
mxnet10
machine-learning10
onnx10
python10
apidoc9
api9
image-processing8
documentation7
Programming languages (6)
Github contributions (5)
apache/mxnet
Jul 2018 - Sep 2020
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:3 reviews, 49 commits, 58 PRs in 2 years 2 months
Contributions summary:Vandana primarily contributed to the improvement and extension of the MXNet library. Their work includes updating documentation, supporting integer data types in ImageIter, and fixing import statements, indicating a focus on code correctness and usability. The user also made changes to the ONNX export, adding support for new operators and addressing undefined variable errors in several examples, suggesting their involvement in the broader ecosystem and model conversion capabilities. Additionally, the user worked on examples for machine learning models.
pythonschedulerdataflowmutationdata-science
vandanavk/incubator-mxnet
Jul 2018 - Jan 2020
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Contributions:374 pushes, 23 branches in 1 year 6 months
pythonschedulerdataflowmutationorchestration
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