Albert Montillo, Ph.D. Associate Professor School Medical School Department Lyda Hill Department of Bioinformatics | Biomedical Engineering Graduate Programs Biomedical Engineering Biography Since September 2024, Albert Montillo has been appointed as Associate Professor in the Lyda Hill Department of Bioinformatics. He completed his Assistant Professor tenure from December 2017 to August 2024. 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. Education Undergraduate Rensselaer Polytechnic Inst (1992), Computer Sciences Graduate School Rensselaer Polytechnic Inst (1993), Computer Sciences Graduate School University of Pennsylvania (2004), Medical Imaging Research Interest 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. Publications Featured Publications Association between resting-state functional brain connectivity and gene expression is altered in autism spectrum disorder. Berto S, Treacher AH, Caglayan E, Luo D, Haney JR, Gandal MJ, Geschwind DH, Montillo AA, Konopka G, Nat Commun 2022 Jun 13 1 3328 Pitfalls and Recommended Strategies and Metrics for Suppressing Motion Artifacts in Functional MRI. Raval V, Nguyen KP, Pinho M, Dewey RB, Trivedi M, Montillo AA, Neuroinformatics 2022 Mar Targeted Metabolomic Analysis in Alzheimer's Disease Plasma and Brain Tissue in Non-Hispanic Whites. Kalecký K, German DC, Montillo AA, Bottiglieri T, J Alzheimers Dis 2022 Feb Reproducible neuroimaging features for diagnosis of autism spectrum disorder with machine learning. Mellema CJ, Nguyen KP, Treacher A, Montillo A, Sci Rep 2022 02 12 1 3057 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 Single Season Changes in Resting State Network Power and the Connectivity between Regions Distinguish Head Impact Exposure Level in High School and Youth Football Players Gowtham Murugesan, Behrouz Saghafi, Elizabeth Davenport, Ben Wagner, Jillian Urban, Mireille Kelley, Derek Jones, Alex Powers, Christopher Whitlow, Joel Stitzel, Joseph Maldjian, Albert Montillo SPIE Medical Imaging 2017 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 Results 1-10 of 46 1 2 3 4 5 Next Last Books 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 Professional Associations/Affiliations American Society of Neuroradiology (ASNR) (2011) IEEE (senior member) (2002) Medical Image Computing and Computer assisted intervention (MICCAI) (2002) Organization for Human Brain Mapping (OHBM) (2012)
