I am currently a graduate student at the National University of Sciences and Technology (NUST), pursuing a Master's in Mathematics with a focus on Data Science. My research centers on applying machine learning techniques to analyze and classify voice notes. Specifically, I am working on the extraction of Mel-spectrogram features from voice data to develop models such as Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). The goal of my research is to improve the accuracy and performance of voice classification models by exploring advanced deep learning architectures and feature engineering techniques.
During my internship at EMRchains, I worked on developing and implementing machine learning models for healthcare data analysis, focusing on Electronic Medical Records (EMRs). My responsibilities included:
Data Preprocessing: Cleaned and processed large datasets from EMRs, ensuring data quality and consistency.
Model Development: Built classification and prediction models using algorithms such as Logistic Regression, Decision Trees, and Random Forests to identify trends and patterns in patient data.
Feature Engineering: Applied advanced feature engineering techniques to extract meaningful insights and improve model performance.
Model Evaluation: Performed model evaluation using metrics like accuracy, ROC-AUC, and F1-score to validate model efficiency and effectiveness.
Collaboration: Worked closely with data scientists and healthcare professionals to understand the requirements and adapt models to real-world clinical scenarios.
This internship enhanced my proficiency in machine learning algorithms and tools, including Python, Scikit-learn, and Pandas, while also deepening my understanding of healthcare analytics
I also teach to one of my UK student , from Parai Likai.
I have teached Math and Phy to 9th and 10th grade students.