Background in signal processing, data science, and artificial intelligence. His research focuses on developing interpretable and efficient computational models for analyzing data from biological, ecological, and environmental systems. He has published studies on intelligent detection algorithms and signal-based monitoring systems, with applications spanning ecology, epidemiology, biomedical engineering, and aquaculture. His academic interests include the application of Large Language Models (LLMs), generative AI, and agentic AI systems. He is currently exploring the integration of these technologies into data-centric pipelines to enhance scientific discovery, improve human-AI interaction, and expand access to complex datasets through intelligent orchestration and interactive tools.