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Montillo, Albert

Dr. Albert Montillo

Affiliate Assistant Professor

UT Southwestern Bioinformatics Department

 

 Degrees:

  • B.S. Computer Science, Rensselaer Polytechnic Institute
  • M.S. Computer Science, Rensselaer Polytechnic Institute
  • Ph.D. Computer Science and Medical Imaging, University of Pennsylvania

Research Interests:

  • Theory and application of deep learning to improve healthcare
  • Mentoring PhD and MD/PhD students, and postdoctoral fellows
  • Automated hyperparameter optimization, embedding priors, causality, visualizing learned network abstractions
  • Developing diagnostics, prognostics and treatment guidance from neuroimaging and non-imaging data for mental and neurodevelopment disorders, as well as neurodegenerative and oncological diseases.

Major Honors and Awards:

  • Outstanding Computer Science Senior Award, Rensselaer Polytechnic Institute
  • Best Paper Award, SPIE Medical Imaging
  • Seven issued U.S. Patents, key contributor to FDA approved brain atrophy quantification system

Representative Publications:

  • Prabhat Garg, Elizabeth M. Davenport, Gowtham Murugesan, Christopher Whitlow, Joseph Maldjian, Albert Montillo, Using Convolutional Neural Networks to Automatically Detect Eye-Blink Artifacts in Magnetoencephalography, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2017
  • Behrouz Saghafi, Benjamin C. Wagner, S. Carrie Smith, Jianzhao Xu, Jasmin Divers, Ananth Madhuranthakam, Barry I. Freedman, Joseph A. Maldjian, and Albert A. Montillo, Deep Fully Connected Neural Network for Estimation of Caudate Perfusion from Clinical Parameters in African Americans with Type 2 Diabetes, Deep Learning for Medical Image Analysis, 2017
  • Albert Montillo, Qi Song, Bipul Das, Zhye Yin, Hierarchical Pictorial Structures for Simultaneously Localizing Multiple Organs in Volumetric Pre-Scan CT, Proc.  SPIE Medical Imaging, Vol. 9413, pp. 9413.28:1-6, 2015
  • Albert Montillo, Jilin Tu, Jamie Shotton, John Winn, J. Eugenio Iglesias, Dimitris Metaxas, and Antonio Criminisi, Entangled Forests and Differentiable Information Gain Maximization, Chapter 19 of Decision Forests for Computer Vision and Medical Image Analysis, Springer, pp. 273-293, 2013
  • Bruce Fischl, David Salat, Evelina Busa, Marilyn Albert, Megan Dieterich, Christian Haselgrove, Andre van der Kouwe, Ron Killiany, David Kennedy, Shuna Klaveness, Albert Montillo, Nikos Makris, Bruce Rosen, and Anders Dale, Whole brain segmentation- automated labeling of neuroanatomical structures in the human brain: with application to change detection in Alzheimer’s disease, Neuron, volume 33, pp. 341-355, 2002.

Notable Service:

  • Teaching machine learning and statistics
  • Member of Program Committee: SPIE Medical Imaging Conference
  • Admissions Committee: Biomedical Engineering Program, UTSW

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