Vishnu Mani
Research Engineer at Hitachi Research and Development Center
Bengaluru, Karnataka, United States
Summary
👤
Senior🎓
Top SchoolVishnu Mani is a research engineer with 12 years of experience designing and shipping automotive embedded systems, combining advanced research work and production programs. He specializes in driver-facing cameras, interior sensing platforms, voice recognition for infotainment, and HMI/driver interfaces for NAFTA demos and CES programs, with a strong focus on delivering customer-centric, high-performance systems. Trained as an electrical engineer with an MSE from the University of Michigan, he brings deep expertise in embedded Linux, low-level registers, signal processing, ML-driven computer vision, and sensor interfaces like CAN, SPI, and UART. He codes across C/C++, Python, MATLAB and has practical DevOps and build experience with Jenkins, Docker, and cross-platform toolchains. On GitHub he’s contributed ML work enabling TF-IDF text features within an AutoML pipeline, showing a willingness to iterate and integrate NLP into production analytics. Known for translating complex concepts into robust, testable designs, he seeks roles where technical depth meets product impact.
12 years of coding experience
Masters of Science, Masters of Science at University of MichiganBachelor of Engineering, Bachelor of Engineering at Coimbatore Institute of Technology
Github Skills (10)
scikit10
machine-learning10
nlp10
python10
natural-language-processing10
scikit-learn10
feature-engineering9
automated-machine-learning9
data-cleaning9
pandas8
Programming languages (1)
Github contributions (5)
ClimbsRocks/auto_ml
Sep 2016 - Oct 2016
[UNMAINTAINED] Automated machine learning for analytics & production
Role in this project:
ML Engineer
ML EngineerContributions:18 commits, 6 PRs, 20 comments in 21 days
Contributions summary:Vishnu primarily focused on integrating and testing a TF-IDF vectorizer for text input within the automated machine learning pipeline. Their work involved adding the vectorizer, modifying data cleaning steps to incorporate text transformations, and ensuring compatibility with existing datasets. The user's contributions include several iterations of integrating and reverting the TF-IDF functionality, demonstrating a focus on refining the text feature extraction process. They successfully adapted the pipeline to handle text and NLP inputs, as well as dataframes, and conducted tests to ensure the functionality worked correctly.
analyticsdata-scienceautomated-machineautomated-machine-learningmachine-learning
vishnumani2009/Extracting-Keyphrases-Relations-from-Scientific-Publications
Aug 2016 - Oct 2016
Contributions:18 commits, 16 pushes, 1 branch in 1 month
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