cv

Basics

Name Muhammad Sulaiman
Label Ph.D Candidate
Email m4sulaim@uwaterloo.ca
Phone (226) 978-5211
Url https://sulaimanalmani.github.io/
Summary I am a fifth-year Ph.D. student at the University of Waterloo. I am passionate about using artificial intelligence for autonomous management and orchestration of 5G and beyond networks.

Work

  • 2020.09 - Present
    PhD Research Assistant
    University of Waterloo
    I worked on the 5G ELITE project, focusing on autonomous network slicing and resource management using AI. I developed several novel algorithms and addressed gaps in SOTA literature on 5G slice modeling, 5G slice admission control (SAC), and resource allocation.
    • Developed a novel slice modeling approach.
    • Developed a novel RL-based framework for joint slicing and admission control of 5G slices.
    • Developed an online slice admission control framework with a theoretical worst-case performance guarantee.
    • Developed primal-dual and RL based frameworks for dynamic resource scaling of 5G slices.
  • 2020.06 - 2020.08
    Undergraduate Research Assistant
    Information Processing and Transmission (IPT) Lab, National University of Sciences and Technology
    Researched Channel State Information (CSI) for activity recognition. Developed expertise in Universal Software Radio Peripherals (USPRs) using GNU Radio.
    • Developed a CSI-based Live Activity Recognition Framework using commodity hardware.

Education

  • 2022.01 - Present

    Waterloo, ON

    Ph.D.
    University of Waterloo
    Computer Science
    Research Area: Autonomous 5G network management
    Supervisor: Raouf Boutaba
    GPA: 96.7/100
  • 2020.09 - 2022.01

    Waterloo, ON

    Masters of Mathematics (MMATH). - Fast-tracked to Ph.D.
    University of Waterloo
    Computer Science
    Research Area: Autonomous 5G network management
    Supervisor: Raouf Boutaba
    GPA: 96.7/100
  • 2015.09 - 2019.07

    Islamabad, PK

    Bachelor of Engineering
    National University of Sciences and Technology (NUST)
    Electrical Engineering
    Research Area: AI-based Activity Recognition using Channel State Information
    Supervisor: Seyd Ali Hassan
    GPA: 3.89/4

Awards

  • 2023
    Best Paper Award
    Network Operations and Management Symposium
    Won the conference best paper award at the Network Operations and Management Symposium, 2023.
  • 2023
    David R. Cheriton Graduate Scholarship
    University of Waterloo
    Received Cheriton Graduate Scholarship for Winter 2023. Awarded to top 5 students based on scholastic excellence.
  • 2022
    Best Paper Award
    Network Operations and Management Symposium
    Won the conference best paper award at the Network Operations and Management Symposium, 2022.
  • 2022
    Travel Grant
    Network Operations and Management Symposium
    Awarded the travel grant for Network Operations and Management Symposium, held in Budapest, Hungary.
  • 2019
    Principal's Appreciation Award
    Received principal's appreciation certificate for excellent academic performance, twice, during undergrad.

Certificates

Publications

Skills

Programming
C/C++
Python
Bash
MATLAB/R
Networking
Linux networking
Open vSwitch
ONOS
P4
Data
Spark
Hadoop
Elasticsearch
Pytorch
Tensorflow
Pandas
Cloud
OpenStack
Kubernetes
Docker

Languages

Urdu/Hindi
Native speaker
English
Fluent

Interests

Artificial Intelligence
Machine Learning
Reinforcement Learning
Deep Learning
AI for Network Management
Networking
5G Networks
Network Slicing
Resource Management
Autonomous Network Management
Software Defined Networking
Network Modeling

References

Dr. Raouf Boutaba
Dr. Boutaba has been my supervisor throughout my Masters and Ph.D. I have interacted closely with him for all my graduate research projects.
Dr. Mohammad A. Salahuddin
Dr. Salahuddin has practically acted as my co-supervisor for the past 4 years. He has intimately helped and guided me through my research.
Mahdieh Ahmadi
I worked with Mahdieh on several projects during her post-doc at University of Waterloo. She is a great researcher, and helped me tremendously throughout her stay.

Projects

  • 2020.09 - Present
    5G Elite
    5G mobile networks are expected to support a wide range of applications and services beyond traditional voice and data services. They will offer services with diverse QoS requirements, such as enhanced mobile broadband, ultra-reliable low-latency communications, and massive machine-type communications. Network slicing is an enabling technology for accommodating different QoS requirements on the same physical network. The project aims to realize automated and data-driven 5G network life-cycle management, powered by AI, ML, and large-scale data processing.
    • Developed a generalizable resource scaling framework for 5G slices using constrained reinforcement learning, recognized with the Best Paper Award at IEEE/IFIP Network Operations and Management Symposium (NOMS), 2023.
    • Implemented a coordinated slicing and admission control system using multi-agent deep reinforcement learning, published in IEEE Transactions on Network and Service Management, 2022.
    • Created a multi-agent deep reinforcement learning framework for slicing and admission control in 5G-CRAN, awarded the Best Paper Award at IEEE/IFIP Network Operations and Management Symposium (NOMS), 2022.
    • Developed a ML-model-driven approach for optimizing resource allocation in 5G network slices.