Brandon Rufino

My academic background focuses on signal processing techniques, machine learning and natural language processing. As an engineer, I feel most motivated when I am applying theory to real-world solutions. I am particularly interested in text as I see a lot of promise behind unstructured data in the clinical domain. For example, can we generate a clinician report through an encoder-decoder model using an x-ray image as input? Can we take a consultation note and generate possible diagnosis? The questions I posed require state-of-the-art knowledge and domain understanding, both of which I hope to bring to T-CAIREM.