Albert Montillo, Ph.D.
Assistant Professor

 

Since December 2017 Albert Montillo has been an appointed as Assistant Professor in the Lyda Hill Department of Bioinformatics. He holds 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.  Before establishing his lab at UTSW, Montillo lead neuroimage and machine learning research in industry at Microsoft Research in Cambridge, UK; the Harvard/MIT Martinos Center for Biomedical Imaging in Boston, and GE Research in New York.

Dr. Montillo advances the theory and application of deep learning using datasets of multi-modal brain images and recordings. He develops de novo machine learning algorithms that can analyze these with better precision. These algorithms integrate brain images with diverse patient data and perform image interpretation to inform accurate diagnoses and prognoses. These precision analyses will provide valuable information for optimization of non-invasive treatments and deepen insight into understanding brain activity in neurological disorders.

Dr. Montillo is actively engaged in supervising, teaching and training of students spanning UT Southwestern, Southern Methodist Univeristy (Statistics and Computer Science ),  UT Dallas (Electrical, Bioengineering, and Computer Science) and UT Arlington. He teaches Machine Learning, Biostatistics and the Python Bootcamp at UTSW and at UT Dallas through an adjunct appointment there and serves on Ph.D. thesis committees at these universities.

Undergraduate

Rensselaer Polytechnic Inst (1992), Computer Sciences

Graduate School

Rensselaer Polytechnic Inst (1993), Computer Sciences

Graduate School

University of Pennsylvania (2004), Medical Imaging

• 1. Developing statistical, machine learning approaches that identify significant associations between functional and structural connectomics measures (from fMRI, MEG/EEG and diffusion MRI, ECoG, DBS) and disease severity and treatment outcomes in Autism Spectrum Disorder, neuropsychiatric disorders, movement disorders and Alzheimer?s.
• 2. Uncovering associations between radiological imaging signatures and gene expression and optimizing their combined use in prognostics. Identifying neurobiological subtypes that have preferential treatment response using machine learning dimensionality reduction methods.
• 3. Using preclinical disease models with collaborators to validate machine learning-biomarker discovered from above research interests.
• 4. Developing multi-parametric MRI for improved cancer diagnostics and prognostics.
• 5. Translating research results into practical tools that improve healthcare in the clinic.

Featured Publications

MEGnet: Automatic ICA-based artifact removal for MEG using spatiotemporal convolutional neural networks.

Treacher AH, Garg P, Davenport E, Godwin R, Proskovec A, Bezerra LG, Murugesan G, Wagner B, Whitlow CT, Stitzel JD, Maldjian JA, Montillo AA, Neuroimage 2021 Jul 241 118402

Predicting Parkinson's disease trajectory using clinical and neuroimaging baseline measures.

Nguyen KP, Raval V, Treacher A, Mellema C, Yu FF, Pinho MC, Subramaniam RM, Dewey RB, Montillo AA, Parkinsonism Relat Disord 2021 Apr 85 44-51

Patterns of Pre-Treatment Reward Task Brain Activation Predict Individual Antidepressant Response

Nguyen KP, Fatt CC, Treacher A, Mellema C, Cooper C, Jha MK, Kurian B, Fava M, McGrath PJ, Weissman M, Phillipes ML, Trivedi MH, Montillo A Biological Psychiatry 2021

Preoperative Prediction of Lymph Node Metastasis from Clinical DCE MRI of the Primary Breast Tumor Using a 4D CNN.

Nguyen S, Polat D, Karbasi P, Moser D, Wang L, Hulsey K, Çobanoglu MC, Dogan B, Montillo A, Med Image Comput Comput Assist Interv 2020 Oct 12262 326-334

Prediction of Individual Progression Rate in Parkinson's Disease Using Clinical Measures and Biomechanical Measures of Gait and Postural Stability.

Raval V, Nguyen KP, Gerald A, Dewey RB, Montillo A, Proc IEEE Int Conf Acoust Speech Signal Process 2020 May 2020 1319-1323

Improved motion correction for functional MRI using an omnibus regression model.

Raval V, Nguyen KP, Mellema C, Montillo A, Proc IEEE Int Symp Biomed Imaging 2020 Apr 2020 1044-1047

Architectural configurations, atlas granularity and functional connectivity with diagnostic value in Autism Spectrum Disorder.

Mellema CJ, Treacher A, Nguyen KP, Montillo A, Proc IEEE Int Symp Biomed Imaging 2020 Apr 2020 1022-1025

Predicting response to the antidepressant bupropion using pretreatment fMRI

Nguyen KP, Fatt CC, Treacher A, Mellema C, Trivedi MH, Montillo A Medical Image Computing and Computer-Assisted Intervention 2019

Deep Learning Architectures Achieve Superior Performance Diagnosing Autism Spectrum Disorder Using Features Previously Extracted from Structural and Functional MRI

Mellema C, Treacher A, Nguyen KP, Montillo A IEEE International Symposium on Biomedical Imaging 2019 1 1891-1895

Deep Fully Connected Neural Network for Estimation of Caudate Perfusion from Clinical Parameters 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, and Albert A. Montillo MICCAI 2017

 Featured Books

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: 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

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

• 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)