This research uses artificial intelligence to predict the progression of Alzheimer’s disease and cancer using medical imaging data. By analyzing brain scans, tumor scans, and treatment responses, AI models can forecast disease development and treatment outcomes, enabling earlier intervention, more personalized care, and improved quality of life for aging populations.

This research develops rigorous mathematical foundations for consensus-based optimization algorithms, where large groups of interacting particles collaboratively search for optimal solutions. Using mean-field theory and propagation of chaos, the work proves long-term stability and improves optimization methods for applications including robotics, aircraft design, and drug discovery under real-world constraints.

This research uses natural language processing techniques to uncover evolutionary relationships between ancient proteins. By analyzing contextual patterns among amino acids, the new computational tool can identify connections between proteins that diverged billions of years ago, helping scientists reconstruct the history of early microbial life and Earth’s biological evolution.

This research introduces iCares, a smart wound-monitoring bandage designed to detect infection and inflammation before visible symptoms appear. Using biosensors, fluid sampling, and machine learning, the system provides real-time wound analysis, enabling earlier intervention, personalized treatment, reduced complications, and improved healing outcomes for patients with chronic wounds.

This research examines how CEO personality influences environmental decoupling, where companies misalign environmental claims and actions. Using the Big Five framework and machine learning on CEO communications, it identifies traits linked to such behavior. Findings aim to improve corporate governance by helping stakeholders select leaders committed to genuine sustainability.