PKPatatchona Keyela

PhD Candidate, Lincs Lab, Department of Computer and Software Engineering, Polytechnique Montréal

Patatchona Keyela

Building quantum and AI/ML algorithms for better decision making and resource management in telecommunication industries.

One-on-one tutoring, teaching, mentorship and academic writing in math and CS.

Years in research
5+

Years in research

Publications
10

Publications

Students assisted
300+

Students assisted

Patatchona Keyela
Award-Winning Researcher & Educator

I am a telecommunications researcher specializing in the intersection of next-generation wireless networks, machine learning, and quantum computing. My current research focuses on optimizing Open RAN architectures, network slicing, and developing intelligent resource allocation frameworks for B5G and 6G systems.

I am a doctoral researcher in telecommunications contributing to shape the future of next generation wireless networks. My work focuses on integrating machine learning, deep learning, and quantum optimization techniques to solve complex resource allocation and network slicing challenges in Open RAN and B5G/6G environments. Alongside my research, which has been featured in top-tier venues like Springer and IEEE Communications Surveys & Tutorials, I have extensive experience teaching university-level mathematics, computer networks and computer science courses.

Selected Work

Featured Research

View all publications
Peer-Reviewed2025

Open RAN Slicing with Quantum Optimization

Global Information Infrastructure and Networking Symposium (GIIS25)

This paper explores the application of quantum computing optimization techniques to tackle complex network slicing challenges within Open RAN architectures, improving resource allocation efficiency.

Read paper
Peer-Reviewed2026

ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunities

IEEE Communications Surveys & Tutorials

A comprehensive survey examining the integration of machine learning within Open RAN setups, detailing architectural progress, deployment roadblocks, and future research trajectories.

Read paper
Peer-Reviewed2022

Discrete Time Markov Chain for Drone's Buffer Data Exchange in an Autonomous Swarm

Springer Nature

This work develops a mathematical model using Discrete Time Markov Chains (DTMC) to evaluate buffer data dynamics and exchange performance between drones operating within an autonomous swarm.

Read paper

Ready to think it through together?

Whether you're stuck on a problem set, shaping a thesis, or preparing for exams — I tutor students in mathematics and computer science.

Book a Session