Kundroo Majid
Postdoctoral Researcher at Chungbuk National University
Cheongju-si, South Korea
Summary
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Senior🎓
Top SchoolKundroo Majid is a Postdoctoral Researcher at Chungbuk National University with eight years of experience building privacy-preserving, communication-efficient federated learning systems. He focuses on hyper-parameter optimization, convergence analysis, and practical deployments of decentralized ML, bridging rigorous research with reproducible engineering. Kundroo authored open-source tooling such as FedEasy to streamline federated experiments and has published multiple peer-reviewed papers on edge ML and applied federated learning. He has hands-on experience simulating and deploying real-world edge use cases using SUMO and KubeEdge and has applied ML solutions to industrial problems like RL-based parameter tuning for laser-cutting. Known for mentoring students and shipping reproducible code, he combines academic depth from a PhD in Information and Communication Engineering with pragmatic software development skills honed in industry roles. He aims to enable enterprises to leverage dispersed data securely and efficiently through distinctive algorithms and deployable systems.
8 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy - PhD, Information and Communication Engineering, Doctor of Philosophy - PhD, Information and Communication Engineering at Chungbuk National UniversityMaster of Computer Applications, Master of Computer Applications at Islamic University of Science & Technology, Pulwama
Stackoverflow
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Github Skills (12)
machine-learning5
singer2
backup2
servicenow2
python2
etl1
data-warehouse1
apache1
airflow1
airbyte1
elt1
apache-airflow1
Programming languages (3)
Github contributions (5)
nclabteam/FedEasy
Sep 2024 - Mar 2025
FedEasy is an intuitive powerful yet simple to use Federated Learning framework. Our goal is to streamline the process of setting up and running federated learning experiments with ease, making advanced machine learning techniques accessible to researchers and developers alike.
Contributions:1 review, 4 PRs, 20 pushes in 6 months
federated-learning-frameworkmachine-learning
kundroomajid/studentFeedback
May 2020 - Oct 2020
Contributions:2 PRs, 38 pushes, 6 branches in 5 months
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