I’m a clinician–researcher and software developer working at the intersection of medicine, data, and technology. My work focuses on building practical, human-centered digital health solutions — especially tools that support early disease detection, preventive care, and AI-driven clinical decision-making.

 With a background that bridges clinical training, public health, and advanced software engineering, I specialize in turning complex medical problems into clear, scalable digital systems. Over the past years, I have contributed to projects ranging from mobile health applications and predictive analytics to medical data visualization platforms and open-source tools for researchers.

What motivates me most is the idea that well-designed technology can close health gaps, improve early diagnosis, and bring high-quality care to people who often get it too late. I enjoy working on projects where thoughtful research, strong engineering, and clinical understanding come together to create something meaningful for patients and healthcare providers.

My work includes:

• Designing and developing digital health tools that support personalized prevention

• Building AI models and computational pipelines for analyzing clinical and epidemiological data

• Translating research insights into real-world healthcare applications

• Collaborating with interdisciplinary teams across medicine, data science, and public health

• Developing open-source resources to empower the research community

I’m also actively involved in scientific research, teaching, and mentoring younger students interested in digital health and AI.

Across all my projects, I try to keep one principle at the center: technology only matters when it makes someone’s life easier, healthier, or safer.

If you’re interested in collaborating on projects related to digital health, AI in medicine, or research on preventive care, feel free to reach out.