Sudhanshu Ranjan
Applied Research at AMD
San Diego, California, United States
Summary
👤
Senior🎓
Top SchoolSudhanshu Ranjan is an applied research engineer and MS CS student at UCSD with nine years of experience spanning machine learning, distributed systems, and deep learning for NLP and low-resource learning. He has shipped production features at Nutanix, interned on protein genomics research at ISI Kolkata, and delivered ML improvements on large healthcare datasets during a HEALTH[at]SCALE internship. Currently training large language models on AMD GPUs at Instella, he blends hands-on systems skills with research-driven modeling and weakly/distantly supervised approaches. An active contributor to the C++ mlpack library, he has improved core density-estimation code and added robust constructors and unit tests—showing attention to memory management and code quality beyond typical ML work. Comfortable coordinating cross-team efforts and writing design docs, he brings both pragmatic engineering and rigorous experimentation to production ML challenges.
9 years of coding experience
2 years of employment as a software developer
University of California, San DiegoBTech, Computer Science and Engineering + Mathematics, 9.22/10, BTech, Computer Science and Engineering + Mathematics, 9.22/10 at Indian Institute of Technology, Guwahati
English
Github Skills (11)
unit-testing10
machine-learning10
cpp10
cplus10
data-structure9
algorithm9
data-structures9
algorithms9
regression6
deep-learning5
deeplearning-ai5
Programming languages (6)
Github contributions (5)
mlpack/mlpack
Dec 2016 - Jan 2017
mlpack: a fast, header-only C++ machine learning library
Role in this project:
Back-end Developer
Back-end DeveloperContributions:27 commits, 9 PRs, 29 comments in 1 month
Contributions summary:Sudhanshu's contributions primarily involve implementing and testing core functionalities related to a density estimation tree (DTree) within the mlpack library. The commits include the addition of copy constructors, move constructors, copy assignment operators, and move assignment operators for the DTree class, enhancing its memory management and object lifecycle. Furthermore, the user has written unit tests to verify the correct behavior of these newly added constructors and operators, demonstrating a focus on code quality and robustness. These changes directly improve the usability and efficiency of the library.
regressionheaderdeep-learningscientific-computingc-plus-plus
Udayraj123/protein-secondary-structure-prediction
Dec 2017 - Jan 2019
Contributions:32 commits, 1 PR, 30 pushes in 1 year 1 month
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