Alexander Galozy

Alexander Galozy is a Postdoctoral Researcher at Halmstad University's School of Information Technology, specializing in Reinforcement Learning applications for healthcare. His work focuses on developing Bandit algorithms that enable personalized adaptive interventions with minimal data requirements.

His primary research interests center on Reinforcement Learning in healthcare contexts, particularly:

• Personalized mobile health interventions using contextual bandits
• Medication adherence optimization through adaptive digital systems
• Chronic disease management (especially diabetes and hypertension)
• Privacy-preserving health data analysis
• Real-world implementation of AI-driven clinical decision support

His methodology emphasizes practical solutions that function effectively with limited patient data.

Analysis of his 15 most recent publications reveals a consistent trajectory applying Bandit algorithms to healthcare challenges. The research spans from theoretical advancements in latent bandit frameworks (2023-2025) to concrete medical applications including diabetes management (2022-2024), medication adherence systems (2020-2021), and critical care analytics (2019). A distinctive pattern shows progressive refinement of contextual bandit approaches for personalized interventions across multiple chronic conditions.

His doctoral work culminated in the 2023 thesis "Mobile Health Interventions through Reinforcement Learning," establishing the foundation for his current postdoctoral research. Current projects include AI-augmented data pipeline orchestration (2025) and novel bandit frameworks balancing state evolution with corrupted context handling.