I am seeking a PhD position in advanced machine learning as a skilled data analyst with technical and mathematical expertise. My experience includes diverse skills in Bayesian framework and statistical data analysis, providing high-level insights for decision-making. I have experience using machine learning tools, PowerBI, SQL, and Python as a data analyst in an IT business company and as a graduate research assistant for HSBC Financial Services.

Decisions are based on a combination of factors, such as business knowledge, external factors like market trends and competition, and regulatory requirements, as I have learned from an international MBA program. Machine learning can also aid in quick decision-making processes by identifying complex relationships between sustainable metrics and making predictions based on those relationships, which I learned from the HSBC industry project.

Machine learning includes AI for Health Care: Concepts and Applications, which I learned from Harvard University, fellowship. I gained familiarity with machine learning frameworks and libraries such as TensorFlow, PyTorch, Scikit-learn, and Keras while pursuing my master's in the DABI program. I have a solid grasp of statistical inference, regression analysis, and probability theory and can construct machine-learning models with the aid of data cleaning, feature extraction, and selection. I am familiar with techniques for sentiment analysis, text classification, text generation, and continuous learning methods like neural networks, convolutional neural networks, and recurrent neural networks, as well as optimisation techniques like gradient descent, stochastic gradient descent and especially in Bayesian optimization, where Gaussian processes help in finding the maximum or minimum of a function efficiently.

In my research, I applied machine learning techniques to evaluate the sustainability and risk-weighted assets of companies. This approach not only shed light on their Environmental, Social, and Governance (ESG) performance but also assessed their likelihood of default. My research include data analysis on Electronic Health Records (EHRs), medical imaging, genomic information, laboratory test outcomes, data from wearables and sensors, clinical notes, as well as patient feedback and reviews. I have also developed AI models capable of providing personalized assessments of heart failure risks. These models consider a broad spectrum of factors, including genomic data, lifestyle habits, and existing health conditions, to create highly tailored risk profiles. Additionally, I have worked on creating accessible, AI-powered tools for the general public and developing dynamic monitoring and early warning systems to detect potential health issues promptly.

I am eager to use my expertise in data analysis and machine learning to drive positive change and growth for academia and industry organisations. Pursuing a PhD will allow me to further my understanding of cutting-edge AI,Machine-learning, and Deep Learning techniques in health data science especially in heart related disease as research interest