Arun Das
Postdoctoral Associate
Pittsburgh, Pennsylvania, United States
Summary
👤
Senior🎓
Top SchoolArun Das is a Postdoctoral Associate at UPMC with a decade of experience building scalable AI and cloud-native systems, blending rigorous academic research (PhD in Electrical Engineering, AI) with hands-on engineering. He has a track record at The Open Cloud Institute creating ML-ready OpenStack appliances, inventing and licensing group emotion/engagement analysis technology, and mentoring graduate students to publication. Arun has industry experience from Intel’s AI research lab working on LSTM enablement for accelerators and contributes to open-source infrastructure tooling—maintaining Dockerized Hadoop/Spark bootstrap scripts for the well-known CaffeOnSpark project. Known as the go-to engineer for Linux environment setup, hyperparameter tuning, and production-ready ML stacks, he combines practical DevOps chops with deep research instincts. Based in Pittsburgh, he pairs academic rigor and product-minded delivery, having shepherded IP to a startup and driven reproducible deployment patterns for accelerated compute.
10 years of coding experience
University of Texas at San AntonioBachelor of Technology (B.Tech.), Electrical and Electronics Engineering, First Class, Bachelor of Technology (B.Tech.), Electrical and Electronics Engineering, First Class at Cochin University of Science and Technology
English, Malayalam, Hindi
Stackoverflow
333reputation
112kreached
15answers
0questions
Github Skills (19)
docker10
bash10
dockers10
hadoop9
spark9
automations8
automation8
cloud-infrastructure8
devtools8
devops8
protoc6
keras6
tensorboard6
deep-learning6
tensorflow6
Programming languages (8)
Github contributions (5)
yahoo/CaffeOnSpark
Apr 2016 - Jan 2017
Distributed deep learning on Hadoop and Spark clusters.
Role in this project:
DevOps Engineer
DevOps EngineerContributions:38 commits, 15 PRs, 114 comments in 9 months
Contributions summary:Arun focused on maintaining and updating the infrastructure setup scripts for running Hadoop and Spark. They addressed broken download links and updated the scripts to reflect the correct locations for Hadoop and Spark distributions. Additionally, they created and updated bootstrap scripts for running Hadoop and Spark within a Docker container, focusing on environment variables and system initialization.
clustersdeep-learningbig-datasparkfeature-store
arundasan91/Deep-Learning-with-Caffe
Mar 2016 - Oct 2017
Contributions:36 commits, 35 pushes, 1 branch in 1 year 7 months
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