Sriram Krishna
Graduate Research Assistant
Pittsburgh, Pennsylvania, United States
Summary
👤
Senior🎓
Top SchoolSriram Krishna is an MS Robotics student at Carnegie Mellon with a decade of software engineering and research experience bridging machine learning, computer vision, and systems engineering. He developed Real-time on-device depth estimation and 3D scene reconstruction at Samsung’s AR Vision Lab—publishing DeepSmooth for temporal-consistent depth completion at a CVPR workshop—and has strong production ML experience optimizing models for efficiency. His open-source contributions include improving core C++ data structures in the well-regarded mlpack library, ensuring robust copy/move semantics for tree structures used in high-performance ML. He’s also built end-to-end ML platforms and simulations in industry, and has a track record of bringing human-in-the-loop reliability to automated scoring systems in research. Colleagues would describe him as a pragmatic engineer who moves fluidly between prototyping new algorithms and hardening them for production.
9 years of coding experience
4 years of employment as a software developer
B. Tech, Computer Science Engineering, 8.99, B. Tech, Computer Science Engineering, 8.99 at PES UniversityMaster of Science - MS, Robotics, 4.17, Master of Science - MS, Robotics, 4.17 at Carnegie Mellon University
Kannada, English, Hindi
Github Skills (9)
data-structures10
machine-learning10
c-language10
cprogramming-language10
data-structure10
octree9
nearest-neighbors9
nearest-neighbor-search9
unit-testing8
Programming languages (9)
Github contributions (5)
mlpack/mlpack
Sep 2019 - Mar 2020
mlpack: a fast, header-only C++ machine learning library
Role in this project:
ML Engineer
ML EngineerContributions:104 commits, 9 PRs, 84 comments in 6 months
Contributions summary:Sriram primarily contributed to the mlpack library by implementing and testing copy and move constructors and assignment operators for various tree data structures, specifically the Binary Space Tree, KDTree, CoverTree, and Octree. Their work involved adding tests to verify the correct functionality of these operations, ensuring that tree structures can be efficiently copied, moved, and assigned. They also addressed minor style issues within the codebase.
regressionheaderdeep-learningscientific-computingc-plus-plus
sriramsk1999/sriramsk1999.github.io
Aug 2021 - Jan 2025
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
Contributions:44 pushes, 4 branches in 3 years 5 months
templategithub-pages-templatemmistakesmistakesjekyll
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