Building intelligent systems at the intersection of language, data, research, and engineering. Focused on NLP, transformer architectures, explainable ML, backend AI infrastructure, and deploying practical machine learning systems from experimentation to production.
6+
ML Projects
2
Research Publications
10+
Certifications
Lahore, Pakistan
AI Research
& Engineering
Focused on low-resource NLP, explainable AI, transformer architectures, backend ML systems, predictive analytics, and production-ready machine learning workflows.
FinSight: Financial Behavior & Risk Analysis
End-to-end financial analytics platform with supervised risk scoring (~92% accuracy), SHAP explainability, behavioral clustering, and backend API integration. Led a team of 3 as Scrum Master.
Urdu Fake News Detection
Fine-tuned multilingual transformer XLM-RoBERTa across cross-domain conditions on two Urdu corpora; diagnosed a systematic length confound causing catastrophic zero-shot transfer failure (macro F1: 0.005).
NLP-Based Pakistani Fake News Classifier
Classified 4,813 political news articles; Logistic Regression achieved 79.2% accuracy and AUC-ROC 0.885 across 5-fold cross-validation.
Pakistan Rainfall Morphology & Event Typology
Extracted 4,563 rainfall events from 44 years of data; GMM clustering and PELT changepoint detection identified a structural monsoon regime shift around 1996 (Random Forest: 96.6%).
Sleep Disorder Prediction
Predicts sleep disorders (Insomnia, Sleep Apnea, None) based on lifestyle and physiological data using multiple ML algorithms and Artificial Neural Networks.
Feedforward Neural Network Engine from Scratch
Built a complete neural network engine in pure NumPy with Adam optimizer and backpropagation; achieved 97.3% benchmark accuracy — within 0.7% of Keras.
Cross-Dataset Generalization in Urdu Fake News Detection
Empirical study with XLM-RoBERTa revealing a severe length confound in one Urdu dataset causing catastrophic zero-shot failure (F1 collapse from 0.771 to 0.005). Diagnostic analysis and recommendations for low-resource NLP dataset standards.
Machine Learning Based Structural Characterization of Rainfall Event Typology and Regime Shifts in Pakistan (1981–2024)
Event-based morphological analysis of 4,563 rainfall events using GMM clustering and PELT changepoint detection. Identified major monsoon regime shift ~1996. Random Forest achieved 96.6% accuracy.
Languages
ML & Deep Learning
NLP & LLMs
Backend & APIs
Python
Primary Language
TensorFlow
Deep Learning
scikit-learn
Machine Learning
FastAPI
Backend Framework
C++
Systems Programming
SQL Server
Database
HuggingFace
NLP Ecosystem
REST APIs
Backend Integration
VS Code
Development IDE
Jupyter
Notebook Environment
Google Colab
Cloud Training
Git
Version Control
