About

My research focuses on the validity and reliability of digital model analyses, biomechanical optimization in clear aligner and lingual systems, parameters influencing anchorage success in mini-implant–assisted tooth movement, and AI-enabled diagnosis and treatment planning. Using 3D imaging, photogrammetry, and deep-learning pipelines, I develop methods for malocclusion classification, treatment-time prediction, and early identification of relapse risk. I prioritize multicenter, prospective designs with rigorous power analysis, blinding, and adherence to reporting standards (CONSORT/STROBE, PRISMA). Findings are disseminated through peer-reviewed publications and conference presentations, supported by open-science practices in data and code sharing.

My educational and administrative work centers on quality assurance, patient safety, and ethics-aligned clinical protocols. I adapt evidence-based guidelines into orthodontic clinical pathways, integrate measurable performance indicators (KPIs), apply Failure Mode and Effects Analysis (FMEA), and drive continuous improvement cycles (PDSA). In undergraduate and graduate teaching, I combine methodology, scientific writing, data visualization, and research ethics with case-based learning, encouraging students to conduct systematic reviews, meta-analyses, and reproducibility studies. My academic goal is to advance a transparent, reproducible orthodontic science ecosystem that delivers measurable outcomes in clinical practice.