Sharath Ramkumar
Founder at Google
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolSharath Ramkumar is a software engineer and founder based in the San Francisco Bay Area with 11 years of experience building production systems at tech leaders including Google and Amazon. He holds BS degrees in Computer Science and Mathematics and an MS in Computer Science from UMass Amherst (4.0), where he contributed to spiking neural network research in the BINDS Lab. At Amazon he progressed from intern to SDE II, and now balances a role at Google with founding HD Acquisitions, demonstrating both startup initiative and large-scale engineering experience. An active open-source contributor, he improved geospatial encoders and preprocessing for the PyTorch-based BindsNET SNN project, bridging cutting-edge research and practical data pipelines. Known for rigorous academic performance and a penchant for making research-grade ML tooling production-ready, he brings a rare mix of research depth and delivery focus.
10 years of coding experience
4 years of employment as a software developer
High School Diploma, Salutatorian, High School Diploma, Salutatorian at Robinson High SchoolMaster of Science - MS, Computer Science, 4.0, Master of Science - MS, Computer Science, 4.0 at University of Massachusetts Amherst
c++, python, go
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (16)
simulations10
simulation10
pytorch10
machine-learning10
spiking-neural-networks10
python10
pandas9
accelerated-computing8
numpy8
gpu8
gpgpu8
parallel-computing8
data-structure7
data-structures7
algorithms7
Programming languages (4)
Github contributions (5)
BindsNET/bindsnet
Jun 2018 - Oct 2019
Simulation of spiking neural networks (SNNs) using PyTorch.
Role in this project:
ML Engineer
ML EngineerContributions:32 commits, 17 PRs, 93 pushes in 1 year 3 months
Contributions summary:Sharath primarily contributed to the development of encoders for geospatial data and other data preprocessing techniques within the context of a spiking neural network simulation. Their work involved implementing a Numenta encoder, refactoring existing encoder implementations, and creating a preprocessing package. They also added unit tests to validate the encoder functionality. The user's contributions directly improved the data processing capabilities of the spiking neural network.
pytorchspiking-neural-networksdynamicreinforcement-learningstdp
sharath/orderbook-predictor
Nov 2017 - Dec 2017
Contributions:1 release, 58 commits, 1 PR in 27 days
fetchespredictorderbooks
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