Exploring AI & Intelligent Systems

Hello, I'm

Shahid Azam

Python Developer, AI/ML Researcher, Machine Learning Engineer, Computer Science Graduate, Research Enthusiast

Building intelligent systems and turning data into insight. I'm a Computer Science professional focused on Artificial Intelligence, Machine Learning, Python development, and research-driven problem solving.

Explore My Work Get in touch
Portrait of Shahid Azam 
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import f1_score

cv = StratifiedKFold(
    n_splits=5,
    shuffle=True
)
# evaluate beyond accuracy
Scroll to explore
  • Artificial Intelligence
  • Machine Learning
  • Python Development
  • Research & Experimentation
  • Retrieval-Augmented Generation
  • Computer Vision

01 About

Curiosity meets computation.

I enjoy understanding how intelligent systems work — and using that understanding to build practical, evidence-driven solutions.

Hi, I'm Shahid.

My academic background is in Computer Science, with a specialization in Artificial Intelligence. My interests include machine learning workflows, predictive modeling, data preprocessing, model evaluation, and Python-based development.

I care about evaluating models thoughtfully — not only through accuracy, but also by examining recall, F1-score, generalization, and the limitations behind the results.

Research helps me explore emerging methods, ask better questions, and connect technical work with real-world needs.

Current
MS Computer Science (AI), IMSciences Peshawar
Foundation
MSc Computer Science, University of Chitral
Interests
ML · NLP · RAG · Computer Vision
Explore my code on GitHub
  1. 01

    Build with purpose

    Create practical projects with clear goals and reproducible workflows.

  2. 02

    Measure what matters

    Understand model behaviour, performance trade-offs, and limitations.

  3. 03

    Keep researching

    Explore AI literature and translate insights into experiments and ideas.

Goal — to grow as an AI/ML researcher and Python developer, building reliable, well-evaluated intelligent systems and collaborating with teams working on meaningful technology.

02 Skills

From ideas to intelligent systems.

A practical toolkit across programming, machine learning, data work, and research.

AI · ML

Artificial Intelligence & Machine Learning

Predictive modeling, supervised learning, feature engineering, class imbalance handling, cross-validation, and model evaluation.

  • Scikit-learn
  • SMOTE
  • Cross-validation
  • Model Metrics

Programming

Python & Algorithms

Structured programming, algorithmic problem solving, data structures, and implementing core computing concepts.

  • Python
  • Algorithms
  • Data Structures
  • OOP

Data

Data Science Toolkit

Working with datasets, data transformations, experiment workflows, and visual analysis.

  • NumPy
  • Pandas
  • Matplotlib
  • Jupyter

Deep Learning · Vision

Deep Learning & Computer Vision

Comparing CNN architectures for image classification and building face-processing workflows with pre-trained ONNX models.

  • CNNs
  • CIFAR-100
  • OpenCV
  • ONNX

Emerging AI

Generative AI & NLP

Research interests in retrieval-augmented generation, knowledge organization, graph-based retrieval, and natural language processing.

  • RAG
  • NLP
  • Graph Retrieval

Tools

Developer Workflow

Version control, project documentation, notebook-based experimentation, and source-code management.

  • Git
  • GitHub
  • VS Code
  • Jupyter

Research

Research & Academic Writing

Systematic literature review, comparative experimental design, documenting the process, examining limitations, and continuously improving technical practice.

  • Literature Review
  • Comparative Evaluation
  • Technical Documentation

03 Selected Work

Projects built to solve and explore.

A selection of programming and machine learning work. Follow the GitHub links to review source code and implementation details.

Featured · 01 Machine Learning

Heart Attack Risk Prediction

A comparative machine learning study exploring preprocessing, class imbalance handling, and cross-validation for heart attack risk prediction. Emphasizes recall, F1-score, and careful interpretation alongside accuracy.

  • Python
  • Scikit-learn
  • SMOTE
  • Cross-validation
Find on GitHub

Algorithms · 02 Python

Preemptive Priority CPU Scheduling

A Python implementation of priority-based process scheduling, including completion time, turnaround time, and waiting time calculations. A smaller priority number represents a higher priority.

  • Python
  • Algorithms
  • Operating Systems
View repository

Vision · 03 Computer Vision

SmartPhotoVision

A computer vision project using YuNet and SFace ONNX models. Visit GitHub to inspect the current implementation, image-processing workflow, and supported features.

  • Python
  • OpenCV
  • ONNX
Find on GitHub

Image Classification · 04 Deep Learning

CIFAR-100 Model Exploration

A deep learning experimentation track comparing CNN architectures for image classification. Refer to the associated notebook or repository for exact models, implementation, and results.

  • Deep Learning
  • Computer Vision
  • Model Comparison
Find on GitHub
View all projects on GitHub

04 Research

Questions worth investigating.

Connecting applied machine learning with emerging methods for more useful and reliable AI systems.

Manuscript under review Systematic Literature Review

Retrieval-Augmented Generation (RAG) Models: Architectures, Applications, and Emerging Challenges

A systematic literature review focused on RAG architectures, applications, and emerging challenges — aligned with my interest in how retrieval quality and knowledge organization affect AI-generated responses.

Area
Generative AI · NLP
Status
Under review
  • RAG
  • NLP
  • Systematic Literature Review
Research work Comparative Study

A Comparative Machine Learning Framework for Heart Attack Risk Prediction Using Data Preprocessing, Class Imbalance Handling, and Cross-Validation

Comparative evaluation of machine learning workflows for heart attack risk prediction, focusing on preprocessing, class imbalance, validation strategy, and metrics that reveal performance trade-offs.

Area
Applied ML · Health Analytics
  • Predictive Modeling
  • Health Analytics
  • Model Evaluation

05 Background

Learning through practice.

A foundation in computer science, practical IT experience, and ongoing work in programming and AI.

Education

  1. Master's · In progress

    MS Computer Science

    Institute of Management Sciences, Peshawar, Pakistan

    Artificial Intelligence specialization · CGPA 3.27/4.00

    • Advanced Machine Learning
    • Advanced Natural Language Processing

  2. MSc · 16 years of education

    Master of Science (MSc) in Computer Science

    University of Chitral, Pakistan

    • Operating Systems
    • Software Engineering

Experience

  1. —

    IT Support Specialist

    University of Chitral

    Supported day-to-day IT operations and user-facing technical needs.

Currently

Pursuing an MS in Computer Science with an AI specialization, working on machine learning projects and research in Retrieval-Augmented Generation.

06 Contact

Open to connections

Have a project, research idea, or opportunity?

I'd love to connect with researchers, developers, academic supervisors, and teams working on meaningful technology and AI-driven solutions.

shahidazam2020@gmail.com

Also on