Over my time as a trainee, I have focused on developing a robust skill set to bridge artificial intelligence (AI) and medical imaging. My graduate studies focused on AI in the radiation oncology domain, where I developed predictive analytic tools for head and neck cancer diagnosis and treatment under the guidance of Dr. Clifton Fuller. Serially funded through an NCATS TL1 Fellowship (TL1TR003169) and subsequently through an NIH F31 Fellowship (F31DE031502-01), I have contributed to a variety of projects related to imaging in head and neck cancer radiotherapy. Broadly, I performed research on image processing, automated region-of-interest segmentation, and clinical outcome prediction. A few key projects I'm particularly proud of are highlighted below, but you can find a full up to date list of my publications on Google Scholar. You can also check out my full PhD dissertation (and defense recording) on Figshare.  

 

I plan to pursue a career as a clinician-scientist with a research emphasis on applying AI to personalized/precision oncology. In the near term, I aim to pursue a residency position in radiation oncology with the goal of performing clinical trials based on AI-centric clinical decision support tools as a path towards mentored and eventual independent investigator status. While my PhD training has granted me technical informatics domain knowledge and mentorship, I believe a crucial yet often under-valued aspect of clinical translation of these technologies is the underlying ethical considerations for physician and patient end-users. Therefore, I am pursuing a post-doctoral opportunity through the NIH Image Guided Cancer Therapy T32 program to explore these topics with sufficient rigor so that I may successfully lead the ethical and equitable translation of image-guided AI technologies to the clinic.