Research Interests

My research primarily focuses on the following topics:

• Machine Learning - Developing and applying ML Models to address real-world problems such as health disparities, disease prediction, and drug discovery.
• Optimization Techniques - Developing and applying optimization methods to improve machine learning models' performance and efficiency.
• Exploratory Data Analysis - Understanding trends, patterns, and insights in data for better problem understanding and decision-making.
• Data Science - Natural Language Processing (NLP) and Large Language Models (LLMs) to extract meaningful insights and solve complex challenges in data-driven applications.

Collaboration

I am always happy to collaborate on interdisciplinary research projects, particularly in the areas of probabilistic machine learning, statistical modeling, optimization, and biomedical data science. I am especially interested in collaborations involving uncertainty-aware modeling, Bayesian methods, survival analysis, and real-world healthcare applications.

If you are interested in collaboration or discussion, feel free to reach out.

Publications

Below are some of my selected research papers:

• Automatic centroid initialization in k-means using artificial hummingbird algorithm - Neural Computing and Applications (2024)
• Density Peak Clustering Using Grey Wolf Optimization Approach - Journal of Classification (2024)
• A Modified Lévy Flight Grey Wolf Optimizer Feature Selection Approach to Breast Cancer Dataset - Lecture Notes in Networks and Systems (2023
• A random walk Grey wolf optimizer based on dispersion factor for feature selection on chronic disease prediction- Expert Systems with Applications (2022)