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Suleman Qamar

@sulemanqamar

AI engineer at University of Technology Sydney who benchmarked FedSOAP against seven federated methods.

At University of Technology Sydney, I design controlled experiments for FedSOAP, comparing it with seven standard federated methods across image and handwriting tasks. I also built an open-source PyTorch benchmark harness so researchers can rerun the comparisons.

At TerraSkyAI, I integrated agricultural drone imagery and trained crop-counting and weed-identification models, packaging them as a FastAPI service. I’ve also built web applications as a freelance developer and released open datasets and reproducible research code.

Experience

Work history, roles, and key accomplishments

Current

Research Assistant

Current

University of Technology Sydney

Oct 2024 - Present (2 years)

Run Python range and throughput experiments on WiFi HaLow (IEEE 802.11ah) for a funded IoT project and analyse the measurements.

Python Networking IoT Experimentation Data Analysis

Current

Graduate Research Scholar

Current

University of Technology Sydney

Mar 2024 - Present (2 years 7 months)

Design and run controlled experiments for FedSOAP, a federated optimiser, comparing against seven standard methods. Built an open-source PyTorch benchmark harness and write up findings for industry partners.

PyTorch Federated Learning Experimental Design Benchmarking Python YAML Open Source

Current

Full Stack Developer

Current

Independent

Jan 2019 - Present (7 years 9 months)

Built and shipped web applications with React front ends and REST APIs in Next.js, TypeScript, Node.js and Flask, on PostgreSQL and MySQL schemas with third-party integrations. Turned loose briefs into written scope and shipped through GitHub Actions CI/CD.

React Next.js TypeScript NodeJS Flask PostgreSQL MySQL REST APIs GitHub Actions CI CD

AI/CV Developer-II

TerraSkyAI

Dec 2025 - Jan 2026 (1 month)

Integrated agricultural drone imagery into a curated training set, designed annotation schemes, and trained crop-counting and weed-identification models in PyTorch. Packaged models as a FastAPI service in Docker and documented run and limitations.

AWS PyTorch fastAPI Docker Computer Vision Data Annotation Model Training Documentation

Casual Academic

University of Kotli

Jan 2021 - Dec 2024 (3 years 11 months)

Taught undergraduate AI, machine learning, neural networks and computer vision; ran laboratory sessions and supervised final-year projects.

Teaching AI Machine Learning Neural Networks Computer Vision Mentorship

Research Assistant

RMIT University

Mar 2024 - Nov 2024 (8 months)

Ran geospatial computer-vision experiments and wrote results analyses and technical write-ups for mixed audiences, used to decide the next experiment.

Computer Vision Geospatial analysis Experimentation Technical Writer

Machine Learning Researcher

PIEAS

Mar 2022 - Feb 2024 (1 year 11 months)

Mentored junior researchers and built an obfuscated-malware image dataset, benchmarking classifiers on imbalanced classes. Also built credit eligibility scoring models and deployed the best as a Flask application.

Machine Learning PyTorch XGBoost Flask Dataset Creation Benchmarking Mentorship

Education

Degrees, certifications, and relevant coursework

University of Technology Sydney

PhD, Engineering

2024 -

Pursuing a PhD in Engineering with a focus on federated and privacy-preserving machine learning, supported by the UTS International Research Scholarship and FEIT scholarship.

Pakistan Institute of Engineering and Applied Sciences (PIEAS)

Master of Science, Computer Science

2019 - 2021

Grade: Gold Medal, GPA 3.96/4.0

Completed a Master of Science in Computer Science with a Gold Medal and a GPA of 3.96/4.0. Thesis focused on autonomous UAVs and deep reinforcement learning.