Noman Bashir
Applied Scientist at Amazon Web Services (AWS)
Seattle, Washington, United States
Summary
👤
Senior🎓
Top SchoolNoman Bashir is an applied scientist with 9 years of experience at the intersection of computer systems and sustainability, currently leading efforts to benchmark and improve the sustainability of Amazon AI services within AWS Sustainability Science and Innovation. His research-driven career spans postdoctoral and graduate work at UMass Amherst, a Computing & Climate Impact Fellowship at MIT CSAIL/MCSC, and influential internships at Google where his peak-prediction overcommitment work became Borg’s default strategy. He combines deep systems expertise—resource management, datacenter efficiency, and energy systems engineering—with policy-aware training from MIT’s Technology and Policy program. Based in Seattle, he brings a rare blend of academic rigor and production impact, focusing on measurable climate outcomes from compute infrastructure.
8 years of coding experience
10 years of employment as a software developer
Master of Science - MS Technology and Policy Program, Master of Science - MS Technology and Policy Program at Massachusetts Institute of TechnologyDoctor of Philosophy (Ph.D.) Computer Engineering, Doctor of Philosophy (Ph.D.) Computer Engineering at University of Massachusetts AmherstMaster of Science (MS) Energy Systems Engineering, Master of Science (MS) Energy Systems Engineering at National University of Sciences and Technology (NUST)Bachelor of Science (BS) Electrical Engineering, Bachelor of Science (BS) Electrical Engineering at University of Engineering and Technology, Lahore
English, Urdu
Github Skills (13)
jekyll10
jekyll-theme10
github-pages9
responsive9
forecast8
bioinformatics8
science7
image-analysis7
cpu6
kubernetes4
python2
python-client2
influxdb1
Programming languages (2)
Github contributions (5)
noman-bashir/noman-bashir.github.io
Mar 2018 - Dec 2022
Contributions:83 pushes, 1 branch in 4 years 9 months
noman-bashir/cluster-resource-forecast
Jun 2020 - Apr 2021
This repository contains experimental tools we developed to forecast a clusters' resource (CPU or memory) usage.
Contributions:185 pushes, 33 branches in 10 months
cpumemorymemory-usageclustersforecast
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