Adjunct Faculty
University of Liberia
Prince Lassemento Fully is a biomedical engineering researcher with a Master's degree from the Indian Institute of Technology (IIT) Madras, where he specialized in integrating engineering principles with medical sciences. His research focuses on applying machine learning and neuroimaging to address healthcare challenges, particularly in cognitive impairment assessment.
His significant contributions include investigating region-specific changes in the corpus callosum to distinguish Mild Cognitive Impairment (MCI) subtypes, and developing a novel machine learning framework that uses geometric features from the fornix to classify early versus late stages of cognitive decline. This groundbreaking work was published in Springer Nature conference proceedings in 2024, representing a major advancement in cognitive health research.
Beyond theoretical research, Prince applies his technical expertise to address real-world public health challenges. As a Graduate Research Assistant, he develops machine learning models to analyze gender-sensitive health metrics in Liberia, aiming to reduce healthcare disparities between rural and urban populations. This work reflects his commitment to creating equitable healthcare solutions through data-driven approaches.
Professionally, Prince serves multiple roles at the University of Liberia, including Information Technology Support Specialist, Instructional Technologist for the School of Public Health, and Informatics Instructor for the School of Pharmacy. His previous internship with the Tamil Nadu Government as a Full Stack Machine Learning Engineer further demonstrates his ability to bridge research with practical implementation.
Internationally recognized for his research, Prince has presented his findings at prestigious conferences including the BIG BRAIN 2023 Conference in Belgrade, Serbia, and the Research and Industrial Conclave 2024 at IIT Guwahati. He is proficient in Python, R, and MATLAB, with expertise spanning machine learning algorithms, statistical modeling, neuroimaging analysis, and full-stack development.
Prince's research philosophy centers on creating interdisciplinary bridges between engineering, medicine, and public health. He believes significant healthcare advances emerge from collaborative, data-informed approaches. Looking forward, he remains committed to advancing biomedical engineering through research, mentorship, and applying his skills to address healthcare challenges in underserved communities.
Selected Publications:
• Fully, P. L., et al. (2024). Machine Learning-Based Classification of Early and Late Mild Cognitive Impairment Conditions. Springer Nature.
• Fully, P. L., et al. (2023). Investigation on Region Specific Alterations in Corpus Callosum. BIG BRAIN Conference.
Technical Expertise:
Python, R, MATLAB, machine learning, statistical modeling, neuroimaging analysis, full-stack development, healthcare informatics.
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Friday, May 1, 2026
3:00 PM - 3:20 PM CT