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.
Dr. Albert Montillo's formal training is through a Ph.D. in Computer Science with a focus on Medical Image Analysis from the University of Pennsylvania, Philadelphia, PA through training in departments of Computer and Information Science and Radiology. There his research focuses on medical image analysis in MRI including applications in neuroradiology and cardiology. Additional extensive training stems from his experience as a research scientist leading neuroimaging analysis efforts at the General Electric Research Center in New York and research at the Machine Intelligence and Perception Laboratory of Microsoft Research in Cambridge, United Kingdom, as well as machine learning based medical image analysis research at the Harvard-MIT Martinos Center for Biomedical Imaging, Boston, MA and Rutgers University in New Jersey.
Dr. Montillo's research focus is advancing the theory and application of deep learning. He develops de novo machine learning algorithms that increase the accuracy and precision of quantitative, integrative analyses of multi-modal brain images. His approaches combine data-driven training & clinician/scientist expert prior knowledge and have both clinical and neuroscience applications. Clinical applications include automating image interpretation for diagnoses and prognoses (assistive second reader), optimizing non-invasive treatments (e.g. tDCS, TMS), automatic clean-up of data, and automatic feature extraction from images. Basic science applications include understanding brain activity in health and in neurological disorders.
Researchers in Dr. Montillo’s group focus their efforts on four areas. (1) Discovering the significant assoicaitons between non-invasive functional and structural connectomics features (from fMRI, MEG/EEG and diffusion MRI) and disease severity and treatment outcomes in pediatric radiology, TBI, epilepsy, autism, diabetes, schizophrenia and Alzheimer’s. (2) Uncovering associations between radiological imaging signatures (such as anatomical and structural connectomics from MRI) and gene expression and optimizing their combined use in prognostics. (3) Developing novel machine learning methods (such as deep learning neural networks) for personalized medicine. (4) Developing multi-parametric MRI for improved cancer diagnostics and prognostics.
Dr. Montillo is actively engaged in supervising, teaching and training of students spanning UT Southwestern, UT Dallas, and UT Arlington. He teaches Machine Learning at UTSW and at UT Dallas through an adjunct appointment there, serves on Ph.D. thesis committees at UT Arlington and UT Southwestern.
- Rensselaer Polytechnic Institu (1992), Computer Sciences
- Graduate School
- Rensselaer Polytechnic Institu (1993), Computer Sciences
- Graduate School
- University of Pennsylvania (2004), Medical Imaging
- Developing statistical, machine learning approaches that extract radiological imaging (MEG/EEG, functional, diffusion, perfusion, metabolic MRI) and imaging-genomic biomarkers and that engender personalized diagnostics and prognostics in clinical neuroscience and oncological applications.
- Testing novel candidate therapies with such biomarkers for improved patient care. Developing novel algorithmic methods to stratify patients through retrospective data analyses that guide prospective patient care.
- Automatic Identification of Successful Memory Encoding In Stereo EEG Of Refractory, Mesial Temporal Lobe Epilepsy
- Afarin Famili, Gowtham Krishnan, Elizabeth Davenport, James Germi, Ben Wagner, Bradley Lega, Albert Montillo ISBI 2017
- Changes in Resting State MRI Networks from a Single Season of Football Distinguishes Controls, Low, And High Head Impact Exposure
- Gowtham Murugesan, Afarin Famili, Elizabeth Davenport, Ben Wagner, Jillian Urban, Mireille Kelley, Derek Jones, Christopher Whitlow, Joel Stitzel, Joseph Maldjian, Albert Montillo ISBI 2017
- Association of Regional Brain Perfusion with Diabetes, Renal and Cardiovascular Disease measures in African Americans with Type 2 Diabetes
- Behrouz Saghafi, Benjamin C. Wagner, S. Carrie Smith, Jianzhao Xu, Jasmin Divers, Ananth Madhuranthakam, Barry I. Freedman, Joseph A. Maldjian, Albert A. Montillo ASFNR 2017
- Intra-Default Mode Network Connectivity Changes from a Single Season of Youth Football Distinguish Levels of Head Impact Exposure
- Gowtham Murugesan, Afarin Famili, Elizabeth Davenport, Ben Wagner, Jillian Urban, Mireille Kelley, Derek Jones, Christopher Whitlow, Joel Stitzel, Joseph Maldjian, Albert Montillo RSNA 2017
- Impact of Glycemic Control and Cardiovascular Disease Measures on Hippocampal Functional Connectivity in African Americans with Type 2 Diabetes: a resting state fMRI Study
- Afarin Famili, Gowtham Murugesan, Ben Wagner, S. Carry Smith, Jianzhao Xu, Jasmin Divers, Barry I. Freedman, Joseph A. Maldjian, Albert A. Montillo RSNA 2017
- Acquisition, preprocessing, and reconstruction of ultralow dose volumetric CT scout for organ-based CT scan planning.
- Yin Z, Yao Y, Montillo A, Wu M, Edic PM, Kalra M, De Man B Med Phys 2015 May 42 5 2730-9
- Hierarchical Pictorial Structures for Simultaneously Localizing Multiple Organs in Volumetric Pre-Scan CT
- Albert Montillo, Qi Song, Bipul Das, Zhye Yin Medical Imaging 2015
- Feature Selection and Imaging-Genetics Predictions Using a Sparse, Extremely Randomized Forest Regressor with application to Alzheimer?s disease
- Albert Montillo, Shantanu Sharma, Marcel Prastawa Medical Image Computing and Computer-Assisted Intervention 2014
- BRAIN TUMOR SEGMENTATION WITH SYMMETRIC TEXTURE AND SYMMETRIC INTENSITY-BASED DECISION FORESTS.
- Bianchi A, Miller JV, Tan ET, Montillo A Proc IEEE Int Symp Biomed Imaging 2013 Apr 2013 748-751
- Parsing radiographs by integrating landmark set detection and multi-object active appearance models.
- Montillo A, Song Q, Liu X, Miller JV Proc SPIE Int Soc Opt Eng 2013 Mar 8669 86690H
Entanglement and Differentiable Information Gain Maximization. In Decision Forests for Computer Vision and Medical Image Analysis
Albert Montillo, Jilin Tu, Jamie Shotton, John Winn, J. Eugenio Iglesias, Dimitris Metaxas, and Antonio Criminisi (2013). Springer
Medical Computer Vision: Recognition Techniques and Applications in Medical Imaging, Second International MICCAI Workshop
Bjoern Menze, Georg Langs, Le Lu, Albert Montillo, Zhuowen Tu, Antonio Criminisi (Ed.) (2013). Springer
Medical Computer Vision: Algorithms for Big Data, Fourth International MICCAI Workshop
Bjoern H. Menze, Georg Langs, Albert Montillo, Michael Kelm, Henning Muller, Shaoting Zhang, Weidong Cai, Dimitri Metaxas (Ed.) (2015). Springer
Medical Computer Vision: Large Data in Medical Imaging, Third International MICCAI Workshop
Bjoern Menze, Georg Langs, Albert Montillo, Henning Muller, Zhuowen Tu (2014). Springer
- Organization for Human Brain Mapping (OHBM) (2012)
- American Society of Neuroradiology (ASNR) (2011)
- IEEE (senior member) (2002)
- Medical Image Computing and Computer assisted intervention (MICCAI) (2002)