Vignesh Kothapalli
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolVignesh Kothapalli is a researcher-engineer bridging geometric deep learning and production ML systems with nine years of experience across industry and academia. Currently a PhD student at Stanford after graduate research at NYU Courant, he studies embedding structures of neural networks while building tools to probe and analyze model internals. He has a strong systems and ML engineering background from multiple roles at LinkedIn and IBM, and contributed to tensorflow/io by improving documentation and adding Kafka tests to strengthen data I/O reliability. Based in Palo Alto, he seeks industry research opportunities where rigorous theory meets scalable infrastructure, and brings a habit of turning deep mathematical insight into practical, well-tested tooling.
9 years of coding experience
6 years of employment as a software developer
Master's degree, Computer Science, Master's degree, Computer Science at Courant Institute of Mathematical SciencesBachelor's degree, Electronics and Communications Engineering, Bachelor's degree, Electronics and Communications Engineering at Indian Institute of Technology, GuwahatiDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Stanford University
English, Telugu, Hindi
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (4)
tensorflow10
documentation10
python10
kafka9
Programming languages (5)
Github contributions (5)
tensorflow/io
Jul 2020 - Apr 2022
Dataset, streaming, and file system extensions maintained by TensorFlow SIG-IO
Role in this project:
Back-end Developer
Back-end DeveloperContributions:337 reviews, 107 commits, 209 PRs in 1 year 8 months
Contributions summary:Vignesh adjusted docstrings and updated the documentation across multiple files within the `tensorflow/io` repository. Their work primarily focused on improving the clarity and accuracy of documentation within the core Python operations, particularly those related to DICOM image decoding, tensor operations, genome operations, and Kafka integration. These documentation updates enhanced the usability of the library. Additionally, the user added tests for Kafka-related functionalities within the tests/test\_kafka.py file, indicating a focus on ensuring the reliability and correctness of the Kafka integrations within the library.
datasetstreamingsigextensionsfilesystem
kvignesh1420/tensorflow
Jul 2020 - Apr 2021
An Open Source Machine Learning Framework for Everyone
Contributions:370 pushes, 65 branches in 9 months
pythondata-sciencedeep-learningmlmachine-learning
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.