Ali Anwar
Assistant Professor at University of Minnesota
Minneapolis, Minnesota, United States
Summary
👤
Senior🎓
Top SchoolAli Anwar is an Assistant Professor and systems researcher with a decade of experience building and analyzing distributed storage, operating systems, and federated learning systems. He transitioned from research staff roles at IBM Almaden—where he analyzed large-scale Docker registries and contributed federated learning examples to IBM's popular FL library—to academia at the University of Minnesota, bringing practical insights into storage behavior and container workloads. His work blends hands-on engineering (from embedded toolchains and build systems to ML example notebooks in Keras and PyTorch) with rigorous research, producing pragmatic optimizations for real-world systems. Trained as a PhD in computer science from Virginia Tech, he focuses on making distributed and storage systems more efficient and easier to deploy, and uniquely pairs storage-system measurement experience with applied federated learning expertise.
10 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy (PhD), Computer Science, Doctor of Philosophy (PhD), Computer Science at Virginia TechHigh School - HSSC, Physics, Chemistry, Maths, High School - HSSC, Physics, Chemistry, Maths at Government College University, LahoreMSc., Computer Engineering, MSc., Computer Engineering at University of Engineering and Technology, Lahore
English, Urdu, Punjabi
Github Skills (9)
keras10
pytorch10
machine-learning10
jupyter-notebook10
federated-learning10
python10
mnist9
deep-learning9
tensorflow8
Programming languages (6)
Github contributions (5)
IBM/federated-learning-lib
Sep 2020 - Jul 2022
A library for federated learning (a distributed machine learning process) in an enterprise environment.
Role in this project:
ML Engineer
ML EngineerContributions:3 releases, 7 reviews, 40 commits in 1 year 10 months
Contributions summary:Ali primarily contributed to example notebooks demonstrating federated learning using the IBM FL library with a focus on a Keras-based MNIST classifier and a PyTorch-based MNIST classifier. Their work involved setting up the environment, splitting datasets, defining model configurations, and integrating these components within the federated learning framework. The user also updated documentation links and corrected minor typos, ensuring the examples' usability and accuracy.
federated-learningmachine-learning
chalianwar/chalianwar.github.io
Oct 2021 - Jun 2026
Contributions:522 pushes, 4 branches in 4 years 8 months
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