AI & Intelligent Systems
Exploring artificial intelligence, generative AI, machine learning, predictive analytics, and intelligent decision-support systems.
Applied research, experiments, and technical exploration focused on software engineering, artificial intelligence, healthcare technology, architecture, and connected systems.
My research interests are strongly connected to practical software engineering and technology implementation.
Exploring artificial intelligence, generative AI, machine learning, predictive analytics, and intelligent decision-support systems.
Investigating digital healthcare, electronic medical records, hospital information systems, healthcare interoperability, and healthcare AI.
Studying scalable software architectures, API ecosystems, system integration, distributed services, and enterprise platforms.
Exploring connected devices, sensor networks, monitoring platforms, early warning systems, and IoT-based applications.
Selected topics representing areas of experimentation, study, and applied engineering.
Exploring how data profiling, visualization, statistical analysis, and pattern discovery can improve the understanding of datasets before developing analytical or machine learning models.
Exploring the integration of artificial intelligence into healthcare workflows to support clinical documentation, decision support, predictive analytics, medical coding, and operational intelligence.
Exploring the transformation of business requirements into scalable architectures, integrated services, APIs, data layers, and reliable enterprise platforms.
Exploring the use of connected sensors, data acquisition, real-time monitoring, and intelligent alert mechanisms for environmental and operational early warning systems.
I approach research from an applied engineering perspective — connecting concepts with experiments, prototypes, systems, and measurable outcomes.
Identify the problem, context, users, constraints, and expected outcomes.
Explore existing approaches, technologies, methods, and relevant evidence.
Build prototypes, test assumptions, analyze data, and evaluate technical approaches.
Translate findings into practical systems, products, architectures, or engineering improvements.
LLM applications, AI agents, intelligent workflows, and enterprise AI.
Data analysis, visualization, predictive analytics, and decision intelligence.
RME, SIMRS, interoperability, healthcare workflows, and clinical intelligence.
Scalable architecture, API integration, distributed services, and platforms.
Open to research collaboration, technology exploration, applied research, and engineering innovation.
Let's Collaborate →