Academic Work
Research
Thesis work, research interests, and ongoing projects at the intersection of AI and intelligent systems.
MS Thesis
Student Information System
MCS · Abdul Wali Khan University, Mardan
Focus Areas
Research Interests
Artificial Intelligence
Designing intelligent systems that reason, learn, and adapt to real-world problems.
Machine Learning
Applying statistical learning methods to extract actionable insight from structured and unstructured data.
Deep Learning
Building and evaluating neural network architectures for prediction, classification, and assessment tasks.
Intelligent Systems
Engineering systems that combine AI models with robust, production-grade software architecture.
Data Science
Turning raw data into rigorous, reproducible analysis that informs decisions.
Selected Work
Research Projects
Credit Risk Prediction using Deep Learning
Featured
Problem Statement
Traditional credit scoring models struggle to capture complex, non-linear relationships in borrower data, leading to suboptimal default prediction and missed risk signals.
Methodology
Designed and trained deep neural network architectures (including feed-forward and regularized deep nets) on real-world credit datasets, benchmarking against logistic regression, random forests, and gradient boosting baselines. Applied feature engineering, class-imbalance handling, and k-fold cross-validation to ensure robust generalization.
Results
The deep neural network model outperformed traditional baselines on key metrics (AUC-ROC, F1-score), demonstrating the viability of deep learning for credit risk assessment. This work formed the basis of the MS thesis at SZABIST University.
Technologies Used
Python
TensorFlow
Scikit-Learn
Pandas
NumPy
DASS-42 Mental Health Assessment using Machine Learning
Featured
Problem Statement
Manual scoring and interpretation of the DASS-42 (Depression, Anxiety and Stress Scale) questionnaire is time-consuming and can benefit from automated, data-driven severity classification.
Methodology
Built supervised machine learning classifiers trained on DASS-42 response data to automatically predict severity levels across depression, anxiety, and stress sub-scales, with exploratory analysis of feature importance across questionnaire items.
Results
Demonstrated that machine learning classifiers can reliably automate DASS-42 severity scoring, offering a scalable tool for mental health screening support.
Technologies Used
Python
Scikit-Learn
Pandas
Matplotlib
Bone Classification using Deep Learning
Featured
Problem Statement
Manual classification of bone types and conditions from medical images is labor-intensive and subject to inter-observer variability.
Methodology
Developed convolutional neural network models for image-based bone classification, applying data augmentation and transfer learning to improve performance on a limited medical imaging dataset.
Results
Achieved strong classification accuracy using transfer learning, highlighting the potential of deep learning to support radiological workflows.
Technologies Used
Python
TensorFlow
Keras
OpenCV