Md Akmol Masud

Mirpur-13, Dhaka-1206, Bangladesh

Incoming at Queen's University, Kingston, Canada

I’m Akmol (Md Akmol Masud) — an incoming M.A.Sc. student in Electrical and Computer Engineering at Queen’s University (Kingston, Canada), working on federated learning over wireless networks under Dr. Ning Lu.

My research is about making collaborative ML work under heterogeneity, communication constraints, and privacy requirements. That means I will mostly focus on federated and split learning and distributed optimization.

Before this, I was at Jahangirnagar University (B.Sc. in Information and Communication Technology), working across biomedical AI, quantum ML, and federated learning. I’ve explored quite a few areas; now I’m narrowing in.

I’m always happy to talk research, swap ideas, or explore collaborations — if something resonates, reach out at [email protected] (Queen’s) or [email protected].

news

Apr 08, 2026	MosQNet-SA published in PLOS ONE
Apr 01, 2026	Our paper DREAM: A Novel Explainable Neural Network for Detecting Sleep Apnea Using Single-Lead ECG Signals appears in Biomedical Signal Processing and Control (Vol. 114, April 2026; ScienceDirect, DOI).
Feb 15, 2026	Joining Queen's University as M.A.Sc. student
Feb 01, 2026	Our paper Quantum Rationale-Aware Graph Contrastive Learning for Jet Discrimination was published in Transactions on Machine Learning Research (TMLR) (February 2026 issue; OpenReview, arXiv:2411.01642).
Jan 30, 2026	Presented FedDQN-TSR at ICECTE 2026 (RUET)

selected publications

TMLR
Quantum Rationale-Aware Graph Contrastive Learning for Jet Discrimination

Md Abrar Jahin, Md Akmol Masud, M. F. Mridha, and 1 more author

Transactions on Machine Learning Research, 2026

Abs arXiv Bib HTML

We propose a quantum rationale-aware graph contrastive learning framework for jet discrimination in high-energy physics, combining graph neural networks with quantum computing to improve classification performance.
@article{masud2026quantumcontrastive, author = {Jahin, Md Abrar and Masud, Md Akmol and Mridha, M. F. and Dey, Nilanjan}, title = {Quantum Rationale-Aware Graph Contrastive Learning for Jet Discrimination}, journal = {Transactions on Machine Learning Research}, year = {2026}, url = {https://arxiv.org/abs/2411.01642}, }
arXiv
Stabilizing Federated Learning under Extreme Heterogeneity with HeteRo-Select

Md Akmol Masud, Md Abrar Jahin, and Mahmud Hasan

2025

Abs arXiv Bib HTML

HeteRo-Select is a client selection framework for federated learning under extreme statistical and system heterogeneity, featuring variance-aware scheduling and heterogeneity-aware aggregation strategies.
@misc{masud2025heteroselect, author = {Masud, Md Akmol and Jahin, Md Abrar and Hasan, Mahmud}, title = {Stabilizing Federated Learning under Extreme Heterogeneity with HeteRo-Select}, year = {2025}, url = {https://arxiv.org/abs/2508.06692}, }

PLOS ONE

MosQNet-SA: Explainable Convolutional-Attention Network for Mosquito Classification with Application as a RESTful API for Dengue and Malaria Risk Mapping

Md Akmol Masud, Sanjida Akter, Nadia Sultana, and 4 more authors

PLOS ONE, 2026

Abs DOI Bib HTML

@article{masud2026mosquito,
  author = {Masud, Md Akmol and Akter, Sanjida and Sultana, Nadia and Islam, Mohammad Shahidul and Abu Yousuf, Mohammed and Noori, Farzan M. and Uddin, Md Zia},
  title = {MosQNet-SA: Explainable Convolutional-Attention Network for Mosquito Classification with Application as a RESTful API for Dengue and Malaria Risk Mapping},
  journal = {PLOS ONE},
  year = {2026},
  volume = {21},
  number = {4},
  pages = {1--30},
  doi = {10.1371/journal.pone.0344970},
  url = {https://doi.org/10.1371/journal.pone.0344970},
}