Arun Nelakurthi
Founder at AGNT5
San Jose, California, United States
Summary
👤
Senior🎓
Top SchoolArun Nelakurthi is a founder, researcher, and engineer with 11 years of experience building machine learning and computer vision systems, currently leading AGNT5 from San Jose. He holds advanced degrees from Arizona State University and has bridged academia and industry at organizations including Samsung Research America and Samsung Ads, focusing on real-time bidding, ad services, and scalable ML. Arun is a hands-on contributor to C++ ML libraries like mlpack and ensmallen, where he implemented momentum policies and improved SGD optimizers—work that directly enhances training performance in widely used header-only libraries. A continuous learner and product-minded technologist, he combines transfer learning and production engineering to ship practical AI products. Colleagues know him for turning deep research into reliable, deployable systems and for an entrepreneur’s curiosity about AI for human good.
11 years of coding experience
14 years of employment as a software developer
BITS Pilani, Birla Institute of Technology and ScienceDoctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Arizona State UniversityGowtham Gudavalli..Sainik School Korukonda, Vizianagaram, Andhra Pradesh
English, Hindi, Telugu
Stackoverflow
1reputation
0reached
1answer
0questions
Github Skills (22)
algorithm10
sgd10
c-language10
optimizers10
machine-learning10
deep-learning10
cplus10
optimisation10
cpp10
cpp-templates10
cprogramming-language10
optimization10
c-templates10
html-template9
python-templates9
Programming languages (6)
Github contributions (5)
mlpack/mlpack
Mar 2017 - Mar 2017
mlpack: a fast, header-only C++ machine learning library
Role in this project:
ML Engineer
ML EngineerContributions:14 commits, 2 PRs, 17 comments in 9 days
Contributions summary:Arun's commits focused on implementing a momentum update policy for the Stochastic Gradient Descent (SGD) optimizer within the `mlpack` machine learning library. This included creating new files for the momentum update policy and modifying existing SGD implementations to integrate it. They also made changes to existing tests, and refactored the code to support different update policies. The contributions directly improve the library's optimization capabilities, specifically related to training machine learning models using SGD.
regressionheaderdeep-learningscientific-computingc-plus-plus
mlpack/ensmallen
Mar 2017 - Mar 2017
A header-only C++ library for numerical optimization --
Role in this project:ML Engineer
ML EngineerContributions:11 commits in 9 days
Contributions summary:Arun contributed to the implementation and improvement of Stochastic Gradient Descent (SGD) optimization algorithms within the `ensmallen` library. This included adding a momentum update policy, creating an alias for standard SGD, and modifying existing code to use variadic templates. The changes also involved adding comments, refactoring, and improving the testing framework for the SGD optimizer.
optimization-methodsoptimization-algorithmsnumerical-optimizationheader-onlycplusplus
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