Iswariya Manivannan
Head Of Cross Platform Technology
Stuttgart, Baden-Württemberg, Germany
Summary
👤
Senior🎓
Top SchoolIswariya Manivannan is a technology leader with eight years of experience bridging machine learning research and software engineering, currently heading Cross Platform Technology at ZEISS in Stuttgart. She has deep expertise in autonomous systems, reinforcement learning, and deep learning gained through roles at ThoughtWorks, Fraunhofer IAIS, and Bosch, and an MS in Autonomous Systems. A hands-on contributor, she has contributed dataset engineering improvements to the widely used tensorflow/datasets project, adding and versioning image datasets with bounding box and segmentation features. Known for translating research into production-ready systems, she combines robotics teaching and thesis-level deep learning work with practical software delivery across embedded and cloud platforms.
8 years of coding experience
8 years of employment as a software developer
Master of Science - MS Autonomous Systems, Master of Science - MS Autonomous Systems at Bonn-Rhein-Sieg University of Applied SciencesBachelor of Engineering (BEng) Electrical and Electronics Engineering, Bachelor of Engineering (BEng) Electrical and Electronics Engineering at Sri Sivasubramaniya Nadar College Of EngineeringHigh School, High School at Savitri Vidyasala G.H.S.S
Tamil, Hindi, German, English
Stackoverflow
678reputation
90kreached
13answers
0questions
Github Skills (16)
datasets10
tensorflow10
python10
image-processing10
data-model10
user-data10
data-set10
numpy9
machine-learning9
git6
pytorch6
azure-pipelines6
bayesian6
uncertainty6
azure-devops6
Programming languages (10)
Github contributions (5)
tensorflow/datasets
Mar 2019 - May 2019
TFDS is a collection of datasets ready to use with TensorFlow, Jax, ...
Role in this project:
Data Scientist
Data ScientistContributions:34 commits, 3 PRs, 25 comments in 1 month
Contributions summary:Iswariya primarily contributed to the `tensorflow/datasets` repository by adding and modifying dataset generation files for various image datasets. Their work involved creating Python scripts to process and structure data, including the Stanford Dogs and Caltech Birds datasets. The user implemented feature engineering, such as the creation of bounding box and segmentation mask features. They also focused on dataset versioning and incorporating the 2011 version of the Caltech Birds dataset.
datanumpydeep-learningdatasetmachine-learning
iswariyam/Mini-semantic-segmentation-network-pytorch
Dec 2018 - Apr 2019
Contributions:14 commits, 2 pushes in 4 months
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