CV

Curriculum vitae of Md Akmol Masud — incoming M.A.Sc. student in ECE at Queen's University working on federated learning, distributed optimization, and privacy-preserving machine learning.

Contact Information

Name Md Akmol Masud
Professional Title Incoming M.A.Sc. Student, Queen's University
Email [email protected]

Professional Summary

Incoming M.A.Sc. student in Electrical and Computer Engineering at Queen’s University, working on federated learning over wireless networks, distributed optimization, and privacy-preserving machine learning for edge AI systems.

Education

  • 2026 - present

    Kingston, ON, Canada

    M.A.Sc.
    Queen's University
    Electrical and Computer Engineering
    • Supervised by Dr. Ning Lu
    • Research focus: federated learning over wireless networks, communication-efficient distributed optimization, privacy-preserving edge AI
  • 2019 - 2024

    Dhaka, Bangladesh

    B.Sc.
    Jahangirnagar University
    Information and Communication Technology
    • Thesis: Federated Learning under Extreme Data and System Heterogeneity
    • Coursework in machine learning, computer networks, digital signal processing, and algorithms

Research Experience

  • 2023 - 2024

    Dhaka, Bangladesh

    Undergraduate Researcher
    Jahangirnagar University
    • Developed HeteRo-Select: a variance-aware client selection framework for federated learning under extreme non-IID data (arXiv:2508.06692)
    • Co-developed MosQNet-SA, an explainable convolutional-attention network for mosquito classification, published in PLOS ONE (2026)
    • Co-developed DREAM, an explainable neural network for sleep apnea detection from ECG, published in Biomedical Signal Processing and Control (2026)
    • Co-authored two IEEE papers on quantum graph neural networks for high-energy physics jet classification

Publications

Skills

Machine Learning & AI (Advanced): Federated Learning, Split Learning, Distributed Optimization, Differential Privacy, Graph Neural Networks, Quantum Machine Learning
Programming (Proficient): Python, PyTorch, TensorFlow, Flower (FL framework), PennyLane, NumPy, scikit-learn
Systems & Tools (Proficient): Docker, Git, Linux, LaTeX, Jupyter, Weights & Biases, Hugging Face Hub

Languages

Bengali : Native speaker
English : Professional working proficiency

Interests

Research: Federated Learning, Wireless Edge AI, Privacy-Preserving ML, Distributed Optimization, Quantum ML
Reading: Science fiction, classic literature, philosophy of science