Education

[ Master of Science (MSc) - Big Data Science and Technology ]

[ University of Bradford, United Kingdom ] 

[ 2021 ] - [ 2022 ]

* Distinction | WES Evaluated GPA: 3.74 / 4.00

Dissertation:
Smart Meter Data Analysis: Energy Consumption Forecasting through Clustering of High-Resolution Time-Series Data

Summary:
This research focused on the analysis of large-scale smart meter datasets, integrating unsupervised learning and time-series forecasting to model energy consumption behavior. Clustering techniques (e.g., K-Means) were applied to identify consumption patterns across households, followed by forecasting models to improve short-term load prediction.

The work emphasized:

• Handling high-volume, high-frequency longitudinal data

• Feature selection and its impact on predictive performance

• Integration of clustering-driven segmentation with forecasting models

This research was conducted as part of the SAFI project within Prof. Daniel Neagu’s AI research group, contributing to applied research in smart energy analytics and knowledge transfer initiatives.

[ Bachelor of Science (BS) - Software Engineering ]

[ COMSATS University Islamabad, Pakistan ] 

[ 2013 ] - [ 2017 ]

Final Year Project:
Brain–Computer Interface (BCI)-Based Attention Training System

Summary:
Designed and developed an EEG-based cognitive training system using non-invasive brain–computer interface technology. The system integrated real-time EEG acquisition, signal preprocessing, feature extraction, and interactive game-based feedback.

The project involved:

• Experimental session design across different user groups

• Analysis of attention dynamics and engagement metrics

• Development of interactive BCI-driven applications

This work established the foundation for my ongoing research in EEG signal analysis, neurotechnology, and human–computer interaction.