Meet the PI

Welcome to our webpage! Our group is focused on developing deep learning and artificial intelligence for addressing Big Data analytics for precision health. Our PI, Albert Montillo is an Assistant Professor in the Lyda Hill Department of Bioinformatics with secondary appointments in the Department of Radiology, the Advanced Imaging Research Center, and Biomedical Engineering within the Graduate School of Biomedical Sciences. He also directs the research of the Deep Learning for Precision Health lab. He is also an Adjunct Professor at UT Dallas in the School of Engineering and Computer Science.

Dr. Montillo received bachelor of science and master of science degrees in Computer Science from RPI and minor concentrations in Electrical Engineering and Cognitive science/Psychology. He obtained his PhD in Computer Science from the University of Pennsylvania where he studied automated image analysis of 4D cardiac MRI and neuroimage co-registration and parcellation (automated quantitative neuroanatomical structure volumetry) with applications to Alzheimer’s. During his studies, Dr. Montillo developed a core neuroanatomical structure labeling algorithm which has been adopted into FreeSurfer, while at Harvard/MIT Martinos Center for Biomedical Imaging, and is now used worldwide. A variant of the algorithm has received FDA approval the first brain parcellation algorithm to do so. After his studies, Dr. Montillo developed a deep learning approach for the decision forest, known as entanglement, which improves prediction accuracy and increases prediction speed while a researcher at the Machine Intelligence and Perception group of Microsoft Research in Cambridge, United Kingdom. Subsequently he joined as a Lead Scientist at General Electric Research Center in upstate New York where he led the development of machine learning based methods for analyzing high volume neuroimaging data. His efforts led to automated methods for brain parcellation (patented), brain lesion quantification, and automated brain-connectivity based prognoses for mild traumatic brain injury (mTBI) – all using advanced multi-contrast MRI. His efforts also led to machine learning algorithms that rank features in imaging genomics studies of Alzheimer’s for automated individualized disease progression prediction. His efforts led to fully automated, machine learning based identification of the content of a imaging scan which prepares a wide range of clinical image data for automated analyses and enables radiation dosage reduction in computed tomography via scout-scans.