myResume/resumes/Murtadha.tex

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%-------------------------------------------
%%%%%% RESUME STARTS HERE %%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{document}
%----------HEADING----------
\begin{tabular*}{\textwidth}{@{\hspace{-1ex}}l}
\textbf{\href{http://m.nisyif.com/}{\Huge\color{blue}MURTADHA NISYIF}} \vspace{2pt}\\
\href{mailto:mnisyif@gmail.com}{\faIcon{at} mnisyif@gmail.com} $|$
\faIcon{phone-square-alt} +1 (519) 502-8463 $|$
\faIcon{map-marker-alt} Ontario, Canada $|$
\href{https://m.nisyif.com/}{\faIcon{user-tie} m.nisyif.com} $|$
\href{https://www.linkedin.com/in/mnisyif}{\faIcon{linkedin} linkedin.com/ln/mnisyif} $|$
\href{https://github.com/mnisyif}{\faIcon{github} github.com/mnisyif}\vspace{12pt} \\
\end{tabular*}
\vspace{-20pt}
%----------Experience----------
\section{\color{blue}WORK EXPERIENCE}
\resumeSubHeadingListStart
\resumeSubheading
{Researcher - Machine Learning \& Semantic Communications}{Jan 2024 Dec 2025}
{University of Guelph}{ Guelph, Ontario}
\resumeItemListStart
\resumeItem {Developed semantic communication pipelines using Swin Transformer models, achieving a 30× reduction in bandwidth usage and 29\% lower latency while preserving atleast 96\% task accuracy under variable network conditions}
\resumeItem {Extended models with adaptive deterministic mechanisms to handle bandwidth fluctuations and anomalies, ensuring stable real-time performance}
\resumeItem {Quantized encoder models to INT8 during edgecloud simulations to emulate smartphone hardware constraints (6-core CPU, limited RAM), enabling realistic performance benchmarking}
\resumeItem {Published a first-author paper in IEEE conference proceedings, detailing the novel integration of semantic communication with edge computing for real-time, near real-time and task-offloading applications}
\resumeItemListEnd
\resumeSubheading
{Software Developer (Work Study)}{Oct 2022 Oct 2023}
{University of Guelph Robotics Institute}{ Guelph, Ontario}
\resumeItemListStart
\resumeItem {Architected and containerized a multi-technology stack combining ROS2, Node.js, and Vue to enable seamless real-time control across distributed robotic systems}
\resumeItem {Implemented automated AWS infrastructure provisioning with Terraform and integrated CI/CD pipelines via GitLab and Jenkins, reducing manual deployment steps by 80\%}
\resumeItem {Created a secure certificate management workflow that streamlined Let's Encrypt renewals and configured a Nginx reverse proxy to enforce HTTPS and granular CORS policies}
\resumeItem {Led the design and implementation of an accessible smart door system using ESP32, PIR sensors, and React Native, achieving over 95\% reliability in extensive field tests}
\resumeItemListEnd
\resumeSubheading
{Information Technology Analyst}{Jul 2020 Dec 2020}
{Kitchener Downtown Community Health Center — SRHC}{ Kitchener, Ontario}
\resumeItemListStart
\resumeItem {Deployed and tuned a centralized Samba file server, increasing file distribution efficiency by 40\% across more than 20 staff and multiple departments}
\resumeItem {Configured and maintained a FortiGate firewall and VPN solution for 60 users, integrating Prometheus-based monitoring for real-time diagnostics and rapid issue resolution}
\resumeItemListEnd
\resumeSubHeadingListEnd
%-----------EDUCATION-----------
\section{\color{blue}EDUCATION}
\resumeSubHeadingListStart
\resumeSchoolItem{University of Guelph $|$ \color{blue} \emph{Masters of Applied Science - Semantic Communication \& ML}}{Dec 2025}
\resumeSchoolItem{University of Guelph $|$ \color{blue} \emph{Bachelors of Engineering - Computer Engineering}}{Apr 2023}
\resumeSubHeadingListEnd
%-----------SKILLS-----------
\section{\color{blue}SKILLS, TECHNOLOGIES, INTERESTS}
\begin{tabularx}{\textwidth}{ @{} >{\bfseries}l X }
\textbf{Skills: } & AI; DevOps; Cloud Computing; IaC; Containerization; CI/CD; Monitoring; Data Engineering; ML Ops \\
\textbf{Languages: } & Python; C++; C; JavaScript; Rust; HTML; Java; Bash\\
\textbf{Tech Stacks: } & FastAPI; PyTorch; React; Flask; SQLite, PostgresSQL, MongoDB, Express JS; Node.js; Swagger; Docker; Git; Jenkins; Terraform; AWS; Kubernetes
% \textbf{Languages: } & English (Fluent); Arabic (Fluent)
\end{tabularx}
\vspace{-7pt}
%-----------PROJECTS-----------
\section{\color{blue}PROJECTS}
\resumeSubHeadingListStart
\resumeProjectHeading {\textbf{\color{black}Personal Portfolio Website} $|$ \color{blue} \emph{React, Rust, Async, Jenkins, Docker}}{}
\resumeItemListStart
\resumeItem{Built a portfolio website featuring a React frontend coupled with a resilient Rust backend}
\resumeItem{Integrated comprehensive Jenkins CI/CD pipelines and Docker-based deployment, slashing manual release efforts by 70\% and ensuring high availability}
\resumeItemListEnd
\resumeProjectHeading {\textbf{\color{black}Home lab Adminstration} $|$ \color{blue} \emph{Docker, Terraform, Jenkins, Prometheus, Grafana, SSL/TLS}}{}
\resumeItemListStart
\resumeItem{Orchestrate a comprehensive home lab environment managing 15+ Docker containers for media, web, and gaming services, configured auto-renewal SSL/TLS certification with Lets Encrypt, setup Prometheus/Grafana monitoring, and applied Fail2Ban for robust security achieving 99.9\% uptime and detailed system analytics}
\resumeItemListEnd
\resumeProjectHeading{\textbf{\color{black}HAM10K Skin Cancer Classifier} $|$ \color{blue} \emph{Python, PyTorch, SciPy, Pandas}}{}
\resumeItemListStart
\resumeItem{Engineered a comprehensive deep learning pipeline integrating a PCA-enhanced MLP, a custom-designed DCNN, and the RegNetY-320 architecture}
\resumeItem{Applied systematic class rebalancing and extensive data augmentation to achieve 96.9\% accuracy, an optimal F1-score, and a flawless 1.00 AUC}
\resumeItemListEnd
\resumeProjectHeading{\textbf{\color{black}Heart Disease Predictor} $|$ \color{blue} \emph{Python, Flask, RESTful, HTML, CSS, JS}}{}
\resumeItemListStart
\resumeItem{Developed a scalable Flask-RESTful API paired with an interactive HTML/JS frontend while leveraging the UCI dataset and implemented real-time feature scaling with hyperparameter tuning to deliver a 95\% prediction accuracy, supporting timely clinical decision-making}
\resumeItemListEnd
\resumeProjectHeading{\textbf{\color{black} Real-Time Noise Cancellation with RL} $|$ \color{blue} \emph{Python, PyTorch, Gymnasium, SciPy, librosa}}{}
\resumeItemListStart
\resumeItem{Created a bespoke OpenAI Gym environment incorporating FFT-based audio processing and trained a PPO agent to perform adaptive noise cancellation in real time, achieving processing speeds exceeding 5,200 FPS for high-fidelity audio performance}
\resumeItemListEnd
\resumeSubHeadingListEnd
% \vspace{-8pt}
\end{document}