Albert Montillo, Ph.D. Titles and Appointments Associate Professor School Medical School Department Lyda Hill Department of Bioinformatics | Biomedical Engineering Graduate Programs Biomedical Engineering, Molecular Biophysics, Neuroscience Biography Since September 2024, Albert Montillo has been appointed as Associate Professor in the Lyda Hill Departments of Bioinformatics and Biomedical Engineering. He completed his Assistant Professor tenure at UTSW from 2017 to 2024. Prior to to UTSW, Dr. Montillo lead neuroimage and machine learning research in industry at Microsoft Research in Cambridge, UK; the Martinos Center for Biomedical Imaging at Harvard/MIT in Boston, and General Electric Research in New York. The research of the Montillo Laboratory focuses on developing the theory and practical solutions to address the main challenges of AI for healthcare and life science. This research provides clinical AI-based tools that support and inform physicians' diagnoses, prognoses, and treatment decisions. The research also develops computational neuroscience approaches to increase our understanding of (patho)neurophysiology. Dr. Montillo is actively engaged in mentoring researchers with interests in AI/ML for healthcare, across the departments of UT Southwestern, as well as from UT Dallas, UT Arlington, and Southern Methodist University. 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 Delivering impactful AI-based tools for the clinic that support and inform physicians' diagnoses, prognoses, and treatment decisions, improving healthcare outcomes for neurological disorders and oncology. Developing computational neuroscience approaches to increase our understanding of (patho)neurophysiology, working at the intersection of medical image analysis, biomedical informatics/machine learning, and causal analysis. Developing the theory and practical algorithmic solutions that address the main challenges of AI for healthcare and life science: (1) deep multimodal fusion of neuroimaging (MRI, PET, EEG) and multi-omic (genomic, proteomic) data , (2) explainable AI, and (3) causal analysis --integrating observational and experimental data, with (4) low sample efficiency. Publications Featured Publications Longitudinal prognosis of Parkinson's outcomes using causal connectivity. Mellema CJ, Nguyen KP, Treacher A, Andrade AX, Pouratian N, Sharma VD, O'Suileabhain P, Montillo AA, Neuroimage Clin 2024 Feb 42 103571 Machine Learning Prediction of Lymph Node Metastasis in Breast Cancer: Performance of a Multi-institutional MRI-based 4D Convolutional Neural Network Polat DS, Nguyen S, Karbasi P, Hulsey K, Cobanoglu MC, Wang L, Montillo A, Dogan BE Radiol Imaging Cancer 2024 Adversarially-Regularized Mixed Effects Deep Learning (ARMED) Models Improve Interpretability, Performance, and Generalization on Clustered (non-iid) Data. Nguyen KP, Treacher AH, Montillo AA, IEEE Trans Pattern Anal Mach Intell 2023 Jan PP 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 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 2022 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 Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, van der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM Neuron 2002 Jan 33 3 341-55 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 Results 1-10 of 25 1 2 3 Next Last Honors & Awards NIH NIA North Texas Alzheimers Disease Research Center (2025) NIH NIGMS Correcting Biases in Deep Learning (2023) NIH NIA Developing Digital Biomarkers for Alzheimers Disease (2021) NIH NIA Blood Biomarker for Alzheimers Disease (2019-2023) NIH NINDS Predicting Parkinsons Disease Progression Rate (2019-2023)
