Kumar Ashutosh
Graduate Research Assistant
Austin, Texas, United States
Summary
👤
Senior🎓
Top SchoolKumar Ashutosh is a PhD student and graduate research assistant at UT Austin with nine years of experience focused on video understanding, multimodal learning, video generation, and machine learning. He has a strong research-industry bridge evidenced by multiple roles at Meta (intern, visiting researcher, and research scientist intern) and internships at Sony and NUS, complemented by a dual BTech+MTech from IIT Bombay. Beyond research, he contributes to production-quality scientific Python projects—improving testing and backporting features for scikit-learn and strengthening numeric reliability in DIPY—showing a knack for robust engineering in widely used open-source libraries. He also has applied ML and systems skills in AR and EEG prototype projects and has taught advanced linear algebra and matrix computation courses. Known for blending rigorous research with practical software quality work, he brings both experimental depth and a test-driven, reproducible approach to ML systems.
9 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at The University of Texas at AustinIndian Institute of Technology BombayKendriya Vidyalaya Sangathan
Github Skills (18)
unit-testing10
python10
data-science10
testing10
scikit10
machine-learning10
numpy10
scikit-learn10
regression-testing9
data-analysis9
medical-imaging9
neuroimaging8
tractography8
segmentation7
image-segmentation7
Programming languages (7)
Github contributions (5)
scikit-learn/scikit-learn
Aug 2017 - Mar 2018
scikit-learn: machine learning in Python
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:16 commits, 17 PRs, 284 comments in 6 months
Contributions summary:Kumar contributed significantly to the testing infrastructure and backporting features for the scikit-learn library. Their work included adding and modifying unit tests, as well as backporting features like `assert_raises` and `assert_raises_regex` from Python 3. The user's changes included modifications to testing utilities, ensuring the library's robustness and proper error handling. Additionally, they focused on adding helpful messages to assertions, and addressed deprecation warnings.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
dipy/dipy
Jan 2018 - Mar 2018
DIPY is the paragon 3D/4D+ medical imaging library in Python. Contains generic methods for spatial normalization, signal processing, machine learning, statistical analysis and visualization of medical images. Additionally, it contains specialized methods for computational anatomy including diffusion, perfusion and structural imaging.
Role in this project:QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:28 commits, 4 PRs, 18 comments in 1 month
Contributions summary:Kumar primarily contributed by modifying and adding tests within the `dipy/dipy` repository. Their work involved writing and modifying test files for various components, specifically focusing on reconstruction algorithms and streamline functionality. The commits demonstrate a focus on ensuring the accuracy and reliability of the library's functionalities, including verifying data shape, affine transformations, and expected results. The user's contributions help maintain the quality and integrity of the scientific computing library.
signalpythonmicrostructurespatialtractography
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.