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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
            {Teaching Assistant – ENGG*4740: Robotics}{Jan 2025 -- Apr 2025}
            {University of Guelph}{Guelph, Ontario}
            \resumeItemListStart
                \resumeItem {Conducted tutorials to reinforce lecture material and guide students through robotics concepts}
                \resumeItem {Led lab demonstrations and provided hands-on guidance in implementing robotic control and navigation algorithms}
                \resumeItem {Graded assignments, lab reports, and project deliverables with constructive feedback}
                \resumeItem {Supported students through Q\&A sessions and office hours, enhancing both technical and problem-solving skills}
            \resumeItemListEnd
        \resumeSubheading
            {Teaching Assistant - ENGG*3640: Microcomputer Interfacing}{Sep 2024 -- Dec 2024}
            {University of Guelph}{Guelph, Ontario}
            \resumeItemListStart
                \resumeItem {Assisted in administering labs by guiding students through hardware/software integration tasks}
                \resumeItem {Evaluated and graded lab reports and assessments, ensuring clear feedback and fair evaluation}
                \resumeItem {Assessed student lab demonstrations and provided constructive feedback on their implementations}
                \resumeItem {Addressed student inquiries related to both lectures and lab work, reinforcing theoretical concepts through practical applications}
            \resumeItemListEnd

       \resumeSubheading
            {Software Engineer—Machine Learning}{Jan 2024 – Aug 2025}
            {University of Guelph}{ Guelph, Ontario}
            \resumeItemListStart
                \resumeItem {Spearheaded the integration of a Swin-Transformer–based compressor into a sophisticated edge-to-cloud pipeline, achieving a 30× reduction in bandwidth usage and a 29\% decrease in latency under variable network conditions}
                \resumeItem {Architectured and trained an adaptive model extension to the existing semantic communication transformer,improving real-time responses to network bandwidth fluctuations while maintaining at least 96\% model accuracy}
                \resumeItem {Quantized PyTorch models via Vitis AI to ONNX for deployment on Xilinx Kria SoCs, leveraging hardware acceleration for 3× faster inference}
                \resumeItem {Published a first-author paper in IEEE conference proceedings, detailing the novel integration of semantic communication with edge computing for real-time applications}
                \resumeItem {Extended semantic communication models with predictive adaptation mechanisms for network anomalies, sustaining 96\% task accuracy under fluctuating conditions}    
            \resumeItemListEnd

        \resumeSubheading
            {Software Developer}{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

    \resumeSubHeadingListEnd
    % \vspace{-8pt}

 	%-----------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; Rust; JavaScript; HTML; Java; Bash; Go\\
    \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 Let’s 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
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\end{document}