Kellin De Jesus

My current research explores how artificial intelligence can make radiation therapy safer and more efficient. I design models that learn from thousands of clinical delivery logs and imaging records to flag subtle patterns that could impact treatment quality. The work blends medicine, computation, and systems thinking by being equal parts data science and patient safety. I came into this field through a winding scientific path: studying senescence and orthopedic degeneration, experimenting with omics data, and learning how biological complexity expresses itself in numbers and images. Those experiences shaped the way I think about data as an imprint of living systems. Alongside the technical work, I’m drawn to the human side of research: mentoring, communicating results across disciplines, and building tools that clinicians actually want to use. My goal is to make machine learning in healthcare more transparent, interpretable, and grounded in real-world impact.

• Provided technical support for diagnostics, imaging, and treatment in a fast-paced emergency veterinary setting. • Managed data entry, case tracking, and imaging records across multiple modalities to ensure accurate patient information. • Recognized the critical impact of reliable data handling on patient outcomes and research models.

I excelled in providing high-quality patient care and effective communication in a specialty veterinary setting. • Engaged with clients to clarify medical findings, translating technical jargon into understandable language. • Collaborated with diverse teams to ensure seamless patient care and diagnostics. • Enhanced my skills in patient care coordination and client communication, vital for veterinary practice.

Dissertation Focus: Machine learning applications in radiation oncology and biomedical imaging; predictive modeling of treatment plan quality assurance using multimodal datasets. Key Areas: Machine Learning • Biomedical Imaging • Oncology Data Science • Statistical Modeling • AI in Healthcare • Translational Research Collaborations / Activities: Medical Physics department, Sidney Kimmel Cancer Center Conference presentations: AAPM 2025 (Washington DC), AIHealth 2025 (Lisbon) Manuscripts in preparation for peer-reviewed journals