Welcome to the Deep Learning for Precision Health Lab

We develop the theory and application of deep learning to improve healthcare.

Overview of our projects and impact on healthcare


We develop the theory and application of deep learning to improve diagnoses, prognoses and clinical decision making. We advance the boundaries of what predictive models can achieve by developing new methods and tools for machine learning and deep learning and improve their applicability and performance on information rich, biomedical problems. Our applications of machine learning focus on building predictive models for neurodegenerative diseases, neurodevelopmental disorders and mental disorders. To achieve these aims, we employ the latest, most advanced, non-invasive neuroimaging acquisition techniques and develop optimized post-processing for neuroimaging data (multi-contrast MRI, EEG/MEG, PET/SPECT), voice and speech data, and surgery video (e.g., endoscopy) data.


Building new fundamental Deep Learning frameworks

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How can we make our deep learning models more interpretable, and output statistically meaningful results? How can uncertainty (e.g., aleatoric, epistemic, dataset) be accounted for in our deep learning models? How can deep learning models be optimally tailored to each new problem to maximize prediction performance, despite the use of multimodal data and finite computing resources? How can domain expertise from clinicians be embedded into deep learning models? How can causal information be extracted in longitudinal data to reduce Type I and II errors commonly resulting from most correlation-based machine learning in use today? We are tackling these problems and more, with a combination of innovative algorithmic development for automated hyperparameter optimization, Bayesian Probability theory, and Information theory. Our focus is on making customized deep learning solutions available to any researcher, including those without machine learning expertise. Our approaches optimize the use of limited labeled training data and provide detailed information about what the models have learned to reveal how predictions are made.

Developing impactful biomedical applications of deep learning

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How can we improve our current subjective diagnoses of subtypes of brain disorders and diseases that have overlapping symptoms? How can we identify new gene targets for spectrum disorders using the exquisite phenotypes provided by multimodal functional neuroimaging? How do we identify pre-treatment biomarkers of therapy response so that optimal patient management decisions can be made before therapy starts? We are addressing these critical biomedical questions by building deep learning predictive models that combine quantitative, multimodal neuroimaging data with multi-omic data and clinical information to reduce uncertainty and improve patient care. This helps patients receive the correct treatment sooner, when it can do the most good. Working together with our clinical collaborators, our models are used to cluster disease subtypes, quantify causal treatment effects before treatment ensues, and help identify the best treatment for each individual patient.


About the lab

The lab is an active part of the departments of Biomedical Engineering and Bioinformatics. The lab is closely aligned with the O'Donnell Brain Institute for basic and translational neuroscience research. We actively participate in multiple academic programs, such as the Biomedical Engineering academic program, the Computational Biology program, the Medical Physics track, the Molecular Biophysics academic program, and the Neuroscience academic program. We maintain close collaborations with faculty in our School of Medicine including the departments of: Radiology, Neurology, Psychiatry, Neuroscience, and Otolaryngology, and the Advanced Imaging Research Center.



LAB MEMBERS


Albert Montillo, Ph.D.

Associate Professor

Biomedical Informatics & Biomedical Engineering

Principal Investigator

Faculty Page


Son Nam Nguyen, Ph.D.

Electrical Engineering

Postdoctoral Fellow

Son has obtained a Ph.D. in Electrical Engineering with a focus on machine learning from the University of Texas at Arlington. He has developed advanced extensions to the backpropagation algorithms using second order training methods to substantially improve their convergence speed. He has adapted and demonstrated his approaches for both feed forward neural networks and for convolutional neural networks. His expertise includes Tensorflow, PyTorch, Matlab, scientific programing and cloud computing. His postdoctoral research focuses on additional improvements to machine learning methodology as well as their clinical applications including oncological applications and the quantitative analysis of diagnostic MRI.

Aixa X. Andrade

Medical Physics

PhD student

Aixa has a MSc. in Medical Radiation Physics from McGill University and a Bachelor's degree in Physics from the National Autonomous University of Mexico. She is interested in artificial intelligence (AI) as a powerful tool to derive insights from data and excited to explore its potential applications in medicine. She is currently pursuing her Ph.D. degree at UT Southwestern Medical center in Dr. Albert Montillo's lab, where she is developing AI skills and novel tools. She hopes to apply these techniques to discover computationally-driven solutions for human disease. Some of Aixa's hobbies are dancing, painting and reading. She enjoys open-air activities and traveling to different places around the world.

Austin Marckx

Computational Biology

PhD student

After obtaining undergraduate degrees in Neuroscience and Classical languages, Austin worked in Dr. Joachim Herz's lab at UT Southwestern studying the hippocampal learning phenotype of neurodegenerative disease mouse models. Currently, he is pursuing his Ph.D. in Computational Biology at UT Southwestern Medical Center in Dr. Montillo's lab. At present, Austin's research is focused on applying mixed effects techniques to deep learning models and is interested in using machine learning for causal inference and counterfactual estimation. Outside of the lab, Austin is a passionate boulderer, avid intra-mural volleyball player, and will read any novel by Brandon Sanderson. "Life before death. Strength before weakness. Journey before destination." The Way of Kings

Krishna Kanth Chitta, M.S.

Research Scientist

Krishna has formal training in medical image analysis, deep learning and computer vision with special emphasis in the physics and clinical applications of Magnetic Resonance Imaging. He has experience in methods for detecting and quantitating structures/lesions in MRI datasets, Fluorescent microscopy images and colonoscopy. He is developing machine learning algorithms including advanced convolutional neural networks to solve image analysis challenges involving multi-modal medical imaging.

Atef Ali

Undergraduate Research Assistant

Atef is studying Mathematics, Computer Science, and Statistics as an undergraduate at the University of Minnesota and is working in Dr. Montillo’s lab as part of a year long internship program at UTSW. In this undergraduate research experience, Atef is implementing novel machine learning methods for multiple-label segmentation algorithms for multi-dimensional MRI. After his Bachelor’s, Atef hopes aims to pursue a PhD in Bioinformatics or Machine Learning. In his free time, Atef enjoys biking (when the Minnesota weather permits!), reading, and watching basketball.

Adam Wang

Undergraduate Research Assistant

Adam is studying Biomedical Engineering as an undergraduate at Harvard University and is being advised remotely by Dr. Montillo as part of an ongoing research project began at UTSW and as part of Harvard’s CS91r supervised research course. In this undergraduate research experience, Adam is implementing fairness enhancing methods for deep learning models with development and external validation in both healthcare and financial application domains. Adam is a native of Texas, who enjoys programming, mathematical modeling, machine learning and life science applications.


Meet the PI

Our PI, Albert Montillo is an Associate Professor in the departments of Bioinformatics and Biomedical Engineering. He is also an Investigator at the O’Donnell Brain Institute. He maintains close collaborations with faculty in our School of Medicine including the departments of: Radiology, Neurology, Psychiatry, Neuroscience, and Otolaryngology, and the Advanced Imaging Research Center. He is also an Adjunct Professor at UT Dallas in the School of Engineering in Computer Science and Biomedical Engineering.

Dr. Montillo obtained a PhD in Medical Imaging and Computer Science from the University of Pennsylvania, where he studied automated image analysis of 4D cardiac MRI and neuroimage co-registration and parcellation (automated quantitative neuroanatomical structure volumetry) with applications to Alzheimer's. He received a master of science degree in Computer and Information Science at UPenn and a Bachelor’s in Computer Science from RPI where he also studied Electrical Engineering and Cognitive neuroscience/Psychology. Through his research, Dr. Montillo developed the leading artifact suppression method for magnetoencephalography (MEG), which is in use at labs nationally including at the MEG Core lab of the National Institutes of Health in Bethesda, and is used worldwide through the Enigma Working group. He also developed a core neuroanatomical structure labeling algorithm which has been adopted into FreeSurfer, while at Harvard/MIT Martinos Center for Biomedical Imaging, and is now used worldwide. A variant of the algorithm has received FDA approval --the first brain parcellation algorithm to do so. Dr. Montillo developed a deep learning approach for the decision forest, known as entanglement, which improves prediction accuracy and increases prediction speed while he was a researcher at the Machine Intelligence and Perception group of Microsoft Research in Cambridge, United Kingdom. While a Lead Scientist at General Electric Research Center in upstate New York, he led the development of machine learning based methods for analyzing high volume neuroimaging data. His efforts led to automated methods for brain parcellation (patented), brain lesion quantification, and automated brain-connectivity based prognoses for mild traumatic brain injury (mTBI) – all using advanced multi-contrast MRI. His efforts also led to machine learning algorithms that rank features in imaging genomics studies of Alzheimer’s and methods for radiation dosage reduction in computed tomography via ML-based scout-scan analysis.


Publications

** = Corresponding author

Polat D, Nguyen S, Wang L, Çobanoglu MC, Montillo A**, Dogan B, Prediction of Lymph Node Metastasis Using a Primary Breast Cancer DCE-MRI-Based 4D Convolutional Neural Network, Radiology: Imaging Cancer, Vol 6, No 3, 2024.
[bib| pdf| PMID: 38607282 | doi: 10.1148/rycan.230107]

Mellema C, Nguyen KP, Andrade AX, Pouratian N, Sharma V, O'Suilleabhain P, Montillo A**, Longitudinal Prognosis of Parkinson’s Outcomes using Causal Connectivity, Neuroimage: Clinical, 2024.
[bib| pdf| doi: 10.1016/j.nicl.2024.103571| PMID: 38471435| PMCID: PMC10944096]

Mellema C, Montillo A**, Novel machine learning approaches for improving the reproducibility and reliability of functional and effective (causal) connectivity from functional MRI, Journal of Neural Engineering, Vol 20 (6), December 2023.
[bib| pdf| doi: 10.1088/1741-2552/ad0c5f| PMID: 37963396]

Treacher AH, Chitta K, McDonald J, German D, Montillo A. A metabolomics blood test for Parkinson’s disease. AD/PD 2023 Conference. April, 2023.

Nguyen K, Treacher, AH, Montillo, A. Adversarially-regularized mixed effects deep learning (ARMED) models for improved interpretability, performance, and generalization on clustered (non-iid) data. IEEE Transactions on Pattern Analysis and Machine Intelligence. vol. 45, no. 7, pp. 8081-8093, 1 July 2023.
[bib | doi: 10.1109/TPAMI.2023.3234291 | PMID: 37018678 | code: ARMED documentation ]

Nguyen, K, Raval, A, Minhajuddin, A, Carmody, T, Trivedi, MH, Dewey, RB, Montillo, A BLENDS: Augmentation of Functional Magnetic Resonance Images for Machine Learning Using Anatomically Constrained Warping. Brain connectivity. 2022. pp 1-26.
[bib |doi: 10.1089/brain.2021.0186 | PMID: 36097756 ]

Kooner KS, Angirekula A, Treacher AH, Al-Humimat G, Marzban, MF, Chen A, Pradhan R, Tunga N, Wang C, Ahuja P, Zuberi H, Montillo AA. Glaucoma Diagnosis Through the Integration of Optical Coherence Tomography/Angiography and Machine Learning Diagnostic Models. Clinical ophthalmology (Auckland, N.Z.). 2022 August 18. 16, 2685-2697.
[bib |doi: 10.2147/OPTH.S367722 | PMID: 36003072 | PMCID: PMC9394657 ]

Berto S*, Treacher AH*, Caglayan E*, Luo D, Haney JR, Gandal MJ, Geschwind DH, Montillo AA**, Konopka G**. Association between resting-state functional brain connectivity and gene expression is altered in autism spectrum disorder. Nature Communications. 2022 June 9; Vol 13(1):3328.
[ bib | doi: 10.1038/s41467-022-31053-5 | PMID: 35680911 | PMCID: 9184501 ]

Kalecky, K, German, D, Montillo, A, Bottiglieri, R**. Targeted Metabolomic Analysis in Alzheimer’s Disease Plasma and Brain Tissue in Non Hispanic Whites. Journal of Alzheimer's Disease. 2022 Feb 28.
[ Epub | PMID 35253754 | doi: 10.3233/JAD-215448 ]

Raval, V, Nguyen, KP, Pinho, M, Dewey, RB, Trivedi, M, Montillo, AA**. Pitfalls and Recommended Strategies and Metrics for Suppressing Motion Artifacts in Functional MRI. Neuroinformatics. 2022.
[ pdf | bib | PMID 35291020 | doi: 10.1007/s12021-022-09565-8 ]

Mellema, CJ, Nguyen, KP, Treacher, A, Montillo, A**. Reproducible neuroimaging features for diagnosis of autism spectrum disorder with machine learning. Scientific Reports. 2022.
[ pdf | bib | doi: 10.1038/s41598-022-06459-2 | PMID: 35197468 | PMCID: PMC8866395 | ISBI 2019 slides | ISBI 2020 presentation | code ]

Treacher A, Garg P, Davenport E, Godwin R, Proskovec A, Bezerra LG, Murugesan G, Wagner B, Whitlow CT, Stitzel JD , Maldjian JA, Montillo A**. MEGnet: Automatic ICA-based artifact removal for MEG using spatiotemporal convolutional neural networks. NeuroImage, 2021; Vol 241, pp. 118402.
[ bib | doi: 10.1016/j.neuroimage.2021.118402 | PMID: 33730626 | pdf | code ]

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**. Patterns of Pre-Treatment Reward Task Brain Activation Predict Individual Antidepressant Response: Key Results from the EMBARC Randomized Clinical Trial. Biological Psychiatry. 2022 Mar 15;91(6):550-560.
[ bib | doi: 10.1016/j.biopsych.2021.09.011 | PMID: 34916068 | pdf_preprint | link | supplement | PressRelease ]

Nguyen KP, Raval V, Treacher A, Mellema C, Yu FF, Pinho MC, Subramaniam RM, Dewey RB, Montillo A**. Predicting Parkinson's disease trajectory using clinical and neuroimaging baseline measures. Parkinsonism and Related Disorders. 2021 April; Vol 85, pp. 44-51.
[ bib | doi: 10.1016/j.parkreldis.2021.02.026 | PMID: 33730626 ]

Nguyen S, Polat D, Karbasi P, Moser D, Wang L, Hulsey K, Cobanoglu M, Dogan B, Montillo A**. Preoperative Prediction of Lymph Node Metastasis from Clinical DCE MRI of the Primary Breast Tumor Using a 4D CNN. MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention. 2020 September; Vol 2, pp. 326-334.
[ pdf | bib | doi: 10.1007/978-3-030-59713-9_32 | PMID: 33768221 | PMC7990260 | slides | code | talk ]

Mellema CJ, Treacher A, Nguyen KP, Montillo A**. Architectural configurations, atlas granularity and functional connectivity with diagnostic value in Autism Spectrum Disorder. International Symposium on Biomedical Imaging (ISBI). 2020 April; pp. 1022-1025.
[ pdf | bib | doi: 10.1109/ISBI45749.2020.9098555 | PMC7990265 | slides | talk ]

Raval V, Nguyen KP, Gerald A, Dewey RB, Montillo A**. Improved Motion Correction for Functional MRI using an Omnibus Regression Model. International Symposium on Biomedical Imaging (ISBI). 2020 April; 1044-1047.
[ pdf | bib | doi: 10.1109/ISBI45749.2020.9098688 | PMC7990252 | slides | talk ]

Raval V, Nguyen KP, Gerald A, Dewey RB, Montillo A**. Prediction of Individual Progression Rate in Parkinson's Disease Using Clinical Measures and Biomechanical Measures of Gait and Postural Instability. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). 2020 May; 1319-1323.
[ pdf | bib | doi: 10.1109/ICASSP40776.2020.9054666 | PMC7944712 | slides | talk ]

Nguyen KP, Chin Fatt C, Treacher A, Mellema C, Trivedi MH, Montillo A**. Anatomically-Informed Data Augmentation for Functional MRI with Applications to Deep Learning. SPIE Medical Imaging. 2020 February; 113130T.
[ pdf | bib | doi: 10.1117/12.2548630 | PMC7990266 | slides | talk ]

Nguyen KP, Chin Fatt C, Treacher A, Mellema C, Trivedi MH, Montillo A**. Predicting Response to the Antidepressant Bupropion using Pretreatment fMRI. Medical Image Computing and Computer-Assisted Intervention: PRIME. 2019 October;
[ pdf | bib | doi: 10.1007/978-3-030-32281-6_6 | PMID: 31709423 | PMC6839715 | talk ]

Nguyen KP, Fatt CC, Mellema C, Trivedi MH, Montillo A**. Sensitivity of Derived Clinical Biomarkers to rs-fMRI Preprocessing Software Versions. IEEE International Symposium on Biomedical Imaging. 2019 April; 1:1581-1584.
[ pdf | bib | doi: 10.1109/ISBI.2019.8759526 | PMID: 31741703 | PMC6860361 ]

Treacher A, Beauchamp D, Quadri B, Vij A, Fetzer D, Yokoo T, Montillo A**. Deep learning convolutional neural networks for the estimation of liver fibrosis severity from ultrasound texture. Medical Imaging. Computer-Aided Diagnosis (SPIE). 2019 February; 1:109503E1-8.
[ pdf | bib | doi: 10.1117/12.2512592 | PMID: 31741550 | PMC6859455 ]

Mellema C, Treacher A, Nguyen KP, Montillo A**. Multiple Deep Learning Architectures Achieve Superior Performance Diagnosing Autism Spectrum Disorder Using Features Previously Extracted from Structural and Functional MRI. IEEE International Symposium on Biomedical Imaging. 2019; 1:1891-1895.
[ pdf | bib | doi: 10.1109/ISBI.2019.8759193 | PMID: 31741704 | PMC6859452 ]

Dash D, Ferrari P, Malik S, Montillo A, Maldjian J, Wang J**. Determining the Optimal Number of MEG Trials: A Machine Learning and Speech Decoding Perspective. Brain Informatics. 2018 December; 1:163-172.
[pdf | bib | doi: 10.1007/978-3-030-05587-5_16 | doi: 10.1109/ISBI.2019.8759193 | PMID: 31768504 | PMC6876632 ]

Murugesan GK, Saghafi B, Davenport EM, Wagner BC, Urban-Hobson J, Kelley M, Jones D, Powers A, Whitlow C, Stitzel JD, Maldjian JA, Montillo A**. 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. Medical Imaging: Computer-Aided Diagnosis (SPIE). 2018 February; 1:105750F1-8.
[pdf | bib | doi: 10.1117/12.2293199 | PMID: 31787799 | PMC6884358 ]

Saghafi B, Murugesan G, Davenport E, Wagner B, Urban J, Kelley M, Jones D, Powers A, Whitlow C, Stitzel J, Maldjian J, Montillo A**. Quantifying the Association between White Matter Integrity Changes and Subconcussive Head Impact Exposure from a Single Season of Youth and High School Football using 3D Convolutional Neural Networks. Medical Imaging: Computer-Aided Diagnosis (SPIE). 2018 February; :105750E1-8.
[pdf | bib | doi: 10.1117/12.2293023 | PMID: 31741549 | PMC6859447 ]

O'Neill TJ, Davenport EM, Murugesan G, Montillo A, Maldjian JA**. Applications of Resting State Functional MR Imaging to Traumatic Brain Injury. Neuroimaging Clin N Am. 2017 Nov;27(4):685-696.
[pdf | bib | doi: 10.1016/j.nic.2017.06.006 | PMID: 28985937 | PMC5708891 ]

Famili A, Murugesan G, Wagner B, Smith SC, Xu J, Divers J, Freedman B, Maldjian JA, Montillo A**. Impact of Glycemic Control and Cardiovascular Disease Measures on Hippocampal Functional Connectivity in African Americans with Type 2 Diabetes: a resting state fMRI Study. Radiological Society of North America; 2017 November; c2017
[ pdf | bib ]

Garg P, Davenport EM, Murugesan G, Whitlow C, Maldjian J, Montillo A**. Using Convolutional Neural Networks to Automatically Detect Eye-Blink Artifacts in Magnetoencephalography Without Resorting to Electrooculography. Medical Image Computing and Computer-Assisted Intervention; 2017 September; 3:374-381.
[pdf | bib | doi: 10.1007/978-3-319-66179-7_43 | PMID: 31656959 | PMC6814159 ]

Saghafi B, Garg P, Wagner B, Smith C, Xu J, Divers J, Madhuranthakam A, Freedman B, Maldjian J, Montillo A**. Quantifying the Impact of Type 2 Diabetes on Brain Perfusion using Deep Neural Networks. Medical Image Computing and Computer Assisted Intervention. 2017 September.
[pdf | bib | doi: 10.1007/978-3-319-67558-9_18 | PMID: 31650132 | PMC6812498 ]

Murugesan G, Garg P, O'Neil T, Wagner B, Whitlow C, Maldjian J, Montillo A**. Automatic Labeling of Resting State fMRI Networks using 3D Convolutional Neural Networks. Pattern Recognition in Neuroimaging. 2017 June.
[ bib]

Garg P, Davenport E, Murugesan G, Wagner B, Whitlow C, Maldjian J, Montillo A**. Automatic Multiple MEG Artifact Detection using 1-D Convolutional Neural Networks without Electrooculography or Electrocardiography. Pattern Recognition in Neuroimaging. 2017 June; 1:1-4.
[pdf | bib | doi: 10.1109/PRNI.2017.7981506 | PMID: 31656826 | PMC6814172 ]

Murugesan G, Famili A, Davenport E, Wagner B, Urban J, Kelley M, Jones D, Whitlow C, Stitzel J, Maldjian J, Montillo A**. Changes in resting state MRI networks from a single season of football distinguishes controls, low, and high head impact exposure. IEEE International Symposium on Biomedical Imaging. 2017 May; 1:464-467.
[pdf | bib | doi: 10.1109/ISBI.2017.7950561 | PMID: 31741701 | PMC6859454 ]

Li B, She H, Keupp J, Dimitrov I, Montillo A, Madhuranthakam A, Lenkinski R, Vinogradov E**. Image registration with structuralized Mutual Information: application to CEST. International Society of Magnetic Resonance In Medicine; 2017 April 22; c2017.
[pdf | bib ]

Famili A, Krishnan G, Davenport E, Germi J, Wagner B, Lega B, Montillo A**. Automatic identification of successful memory encoding in stereo-EEG of refractory, mesial temporal lobe epilepsy. IEEE International Symposium on Biomedical Imaging. 2017; 1:587-590.
[pdf | bib | doi: 10.1109/ISBI.2017.7950589 | PMID: 31741702 | PMC6859446 ]

Muller H, Kelm BM, Arbel T, Cai WT, Cardoso MJ, Langs G, Menze B, Metaxas D, Montillo A, Wells III WM, Zhang S, Chung AC, Jenkinson M, Ribbens A, editors. Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging. London: Springer book publishers; 2016. 222p.
[doi: 10.1007/978-3-319-61188-4 ]

Menze B, Langs G, Montillo A, Kelm BM, Muller H, Zhang S, Cai W, Metaxas D, editors. Medical Computer Vision: Algorithms for Big Data: International Workshop, MCV 2015. Switzerland: Springer book publishers; 2016. 182p.
[bib | doi: 10.1007/978-3-319-42016-5 ]

Liu X, Montillo A, inventors. Systems and methods for image segmentation using a deformable atlas. U.S. issued patent #9208572. 2015 December.
[pdf ]

Yin Z, Yao Y, Montillo A, Wu M, Edic PM, Kalra M, De Man B**. Acquisition, preprocessing, and reconstruction of ultralow dose volumetric CT scout for organ-based CT scan planning. Med Phys. 2015 May;42(5):2730-9.
[pdf | bib | doi: 10.1118/1.4921065 | PMID: 25979071 ]

Montillo A**, Song Q, Das B, Yin Z. Hierarchical pictorial structures for simultaneously localizing multiple organs in volumetric pre-scan CT. Medical Imaging: Image processing (SPIE). 2015 March; 1:94130T1-6.
[pdf | bib | doi: 10.1117/12.2082183 | PMID: 31798201 | PMC6886528 ]

Montillo A**, Sharma S, Prastawa P. Feature Selection and Imaging-Genetics Predictions Using a Sparse, Extremely Randomized Forest Regressor. 2014 September. Medical Image Computing and Computer-Assisted Intervention: Workshop on Imaging Genetics, MIT, Boston.
[pdf | bib ]

Menze B, Langs G, Montillo A, Kelm M, Muller H, Zhang S, editors. Medical Computer Vision: Algorithms for Big Data: International Workshop, MCV 2014. Switzerland: Springer; 2014. 211p.
[doi: 10.1007/978-3-319-13972-2 ]

Yin Z, Yao Y, Montillo A, Edic PM, Man BD**. Feasibility study on ultra-low dose 3D scout of organ based CT scan planning. International Conference on Image Formation in X-Ray Computed Tomography. ISBN: 9781510857131. 2014; 1:52-55.
[pdf | PMID: 31788673 | PMC6885018 ]

Yan Z, Zhang S, Liu X, Metaxas D, Montillo A**. Accurate whole-brain segmentation for Alzheimer’s disease combining an adaptive statistical atlas and multi-atlas. Medical Image Computing and Computer-Assisted Intervention. 2013 September;
[pdf | bib | doi: 10.1007/978-3-319-05530-5_7 | PMID: 31723945 | PMC6853627 ]

Montillo A**, Song Q, Bhagalia R, Srikrishnan V. Organ localization using joint AP/LAT view landmark consensus detection and hierarchical active appearance models. Medical Image Computing and Computer-Assisted Intervention. 2013 September; 3:138-147.
[pdf | bib | doi: 10.1007/978-3-319-05530-5_14 | PMID: 31915754 | PMC6947663 ]

Bianchi A, Miller JV, Tan ET, Montillo A**. Brain tumor segmentation with symmetric texture and symmetric intensity-based decision forests. Proc IEEE Int Symp Biomed Imaging. 2013 Apr;2013:748-751.
[pdf | bib | doi: 10.1109/ISBI.2013.6556583 | PMID: 25404996 | PMC4232942 ]

Montillo A**, Song Q, Liu X, Miller JV. Parsing radiographs by integrating landmark set detection and multi-object active appearance models. Medical Imaging : Image processing (SPIE). 2013 Mar 13;8669:86690H.
[pdf | bib | doi: 10.1117/12.2007138 | PMID: 25075265 | PMC4112100 ]

Liu X**, Montillo A, Tan E, Schenck J. iSTAPLE: improved label fusion for segmentation by combining STAPLE with image intensity. Medical Imaging : Image processing (SPIE). 2013 March; 1:86692O1-6.
[pdf | bib | doi: 10.1117/12.2006447 | PMID: 31741552 | PMC6859448 ]

Liu X**, Montillo A, Tan ET, Schenck JF, Mendonca P. Deformable atlas for multi-structure segmentation. Med Image Comput Comput Assist Interv. 2013;16(Pt 1):743-50.
[pdf | bib | doi: 10.1007/978-3-642-40811-3_93 | PMID: 24505734 ]

Yan Z, Zhang S, Liu X, Metaxas DN, Montillo A**. Accurate segmentation of brain images into 34 structures combining a non-stationary adaptive statistical atlas and a multi-atlas with applications to Alzheimer's disease. IEEE International Symposium on Biomedical Imaging. 2013; 2:1202-1205.
[pdf | bib | doi: 10.1109/ISBI.2013.6556696 | PMID: 31788155 | PMC6884356 ]

Montillo A**, Tu J, Shotton J, Winn J, Iglesias JE, Metaxas DN, Criminisi A. Entangled Forests and Differentiable Information Gain Maximization. Decision Forests for Computer Vision and Medical Image Analysis. London: Springer; 2013. Chapter 19; p.273-293.
[pdf | bib | doi: 10.1007/978-1-4471-4929-3_19 ]

Menze B, Langs G, Lu L, Montillo A, Tu Z, Criminisi A, editors. Medical Computer Vision. Large Data in Medical Imaging. Berlin: Springer-Verlag Berlin Heidelberg; 2013. 292p.
[ doi: 10.1007/978-3-319-05530-5 ]

Montillo A**. Context Selective Decision Forests and their application to Lung Segmentation in CT Images. Medical Image Computing and Computer-Assisted Intervention: PIA. 2011 September; 1:201-212.
[pdf | bib | ISBN: 978-1-4662-0016-6 | URL ]

Montillo A**, Shotton J, Winn J, Iglesias JE, Metaxas D, Criminisi A. Entangled decision forests and their application for semantic segmentation of CT images. Inf Process Med Imaging. 2011;22:184-96.
[pdf | bib | PMID: 21761656 ]

Iglesias JE, Konukoglu E, Montillo A, Tu Z, Criminisi A**. Combining generative and discriminative models for semantic segmentation of CT scans via active learning. IPMI 2011, LNCS 6801, pp. 25–36, 2011.
[pdf | bib | PMID: 21761643 ]

Montillo A**, Metaxas DN, Axel L. Incompressible biventricular model construction and heart segmentation of 4D tagged MRI: application to right ventricular hypertrophy. Medical Image Computing and Computer-Assisted Intervention: CBM. 2010 October; 1:143-155.
[pdf | bib | doi: 10.1007/978-1-4419-9619-0_15 | PMID: 31742255 | PMC6860908 ]

Montillo A**, Ling H. Age regression from faces using random forests. IEEE International Conference on Image Processing. IEEE International Conference on Image Processing. 2010 February; 1:1-4.
[pdf | bib | doi: 10.1109/ICIP.2009.5414103 | PMID: 31772508 | PMC6879191 ]

Axel L**, Montillo A, Kim D. Tagged magnetic resonance imaging of the heart: a survey. Med Image Anal. 2005 Aug;9(4):376-93.
[ pdf | bib | doi: 10.1016/j.media.2005.01.003 | PMID: 15878302 ]

Gopalakrishnan V, Montillo A, Bachelder I, inventors. Methods and apparatus for determining the orientation of an object in an image. U.S. issued patent #6898333. 2005 May.

Park K, Montillo A, Metaxas DN, Axel L**. Volumetric Heart Modeling and Analysis. Communications of the ACM. 2005 February; 48(2):43-48.
[pdf | bib | doi: 10.1145/1042091.1042118 | PMID: 31662583 | PMC6818726 ]

Gopalakrishnan V, Montillo A, Bachelder I, inventors. Methods and apparatuses for generating a model of an object from an image of the object. U.S. issued patent #6813377. 2004 November.

Manglik T, Axel L, Pai VM, Kim D, Dugal P, Montillo A, Zhen Q**. Use of Bandpass Gabor Filters for Enhancing Blood-Myocardium Contrast and Filling-in tags in tagged MR Images. International Society of Magnetic Resonance In Medicine; 2004 May; c2004.
[pdf ]

Montillo A**, Metaxas D, Axel L. Extracting tissue deformation using Gabor filter banks. Medical Imaging: Physiology, Function, and Structure from Medical Images (SPIE). 2004 April; 1:1-9.
[pdf | bib | doi: 10.1117/12.536860 | PMID: 31824125 | PMC6902438 ]

Montillo A, Bachelder I, inventors**. Methods and apparatuses for identifying regions of similar texture in an image. U.S. issued patent #6647132. 2003 November.

Montillo A**, Metaxas DN, Axel L. Automated deformable model-based segmentation of the left and right ventricles in tagged cardiac MRI. Medical Image Computing and Computer-Assisted Intervention. 2003 October; 1:507-515.
[pdf | bib | doi: 10.1007/978-3-540-39899-8_63 | PMID: 31663082 | PMC6818716 ]

Qian Z, Montillo A, Metaxas D, Axel L**. Segmenting cardiac MRI tagging lines using Gabor filter banks. IEEE Engineering in Medicine and Biology Society. 2003 September; 1:630-633.
[pdf | bib | doi: 10.1109/IEMBS.2003.1279834 ]

Montillo A**, Axel L, Metaxas D. Automated Correction of Background Intensity Variation and Image Scale Standardization in 4D Cardiac SPAMM-MRI. International Society of Magnetic Resonance In Medicine; 2003 July; c2003.
[pdf | bib ]

Montillo A**, Udupa J, Axel L, Metaxas D. Interaction between noise suppression and inhomogeneity correction in MRI. Medical Imaging: Image Processing (SPIE). 2003 May; 1:1-12.
[pdf | bib | doi: 10.1117/12.483555 | PMID: 31745377 | PMC6863362 ]

Montillo A, Bachelder I, Marrion CC, inventors. Methods and apparatuses for measuring an extent of a group of objects within an image. U.S. issued patent #6571006. 2003 February.

Montillo A, Bachelder I, Marrion CC, inventors. Methods and apparatuses for refining a geometric description of an object having a plurality of extensions. U.S. issued patent #6526165. 2003 February.

Montillo A**, Metaxas DN, Axel L. Automated segmentation of the left and right ventricles in 4D cardiac SPAMM images. Medical Image Computing and Computer-Assisted Intervention. 2002 September; 1:620-633.
[pdf | bib | doi: 10.1007/3-540-45786-0_77 | PMID: 31737869 | PMC6857637 ]

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**. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron. 2002 Jan 31;33(3):341-55.
[pdf | bib | doi: 10.1016/s0896-6273(02)00569-x | PMID: 11832223 ]

Copyright Notice: The materials are presented here may only be used for research purposes. Copyright and all rights therein are retained by authors or by other copyright holders. In most cases, these works may not be reposted or distributed without the explicit permission of the copyright holder.

Teaching

Curriculum development and instruction

Since joining the university, I have both contributed to existing courses and developed brand-new curricula. Here’s a summary. Machine Learning Theory and Methods This course begins with the foundations of statistical learning theory, teaches model training, optimization, and regularization, (un)supervised learning, and progresses through empirical and structural risk minimization, clustering, neural networks, and survival analysis, and concludes with transfer learning, explainable AI/ML, and causal discovery and inference. Advanced Deep Learning This course covers a broad array of practical architectures in deep learning, beginning with deep neural networks for regression, classification, and segmentation in image and sequence data (e.g., convolutional and recurrent neural networks). It progresses to graph neural networks, fair and trustworthy learning, contrastive learning, hyperparameter optimization, and generative modeling, including GANs, autoencoders, and diffusion models. The course concludes with transformers, foundation models, and methods for parameter efficient fine tuning. Biomedical Informatics and Biostatistics This course spans all core aspects of biomedical informatics and biostatistics. Topics taught include fundamental descriptive statistics, probability theory, hypothesis testing, ANOVA, correlation/regression for high dimensional data, confidence intervals, experiment design, multiple comparisons correction, resampling methods, and Bayesian Decision theory. Hands-on labs and problem sessions are interleaved with didactic lectures throughout the course. Software Engineering for Research This graduate level course covers the best programming practices for producing maintainable research code. This includes code review, software version control, fundamentals of object-oriented code design and implementation, debugging techniques, and experiment acceleration via distributed CPU and GPU hardware. All topics include hands-on labs illustrating generative AI examples (e.g., VAE models, GANs, and hybrid models, and their hyperparameter optimization) using TensorFlow and PyTorch. Causality Journal Club I lead this cross-campus club where we discuss the latest statistical and machine learning-driven causal analysis literature, including causal discovery, causal inference, and causal deep learning.

Mentoring methodology

I have the privilege of advising trainees of all levels, including doctoral, MD/PhD students, and master’s and undergraduate students, and postdoctoral fellows. Several of these are co-advised with colleagues in Neurology, Neuroscience, and Radiology. I have supervised master’s theses and served on doctoral committees and qualifying exam committees (BME, Computer Science, Biomedical informatics, Physics, and Electrical Engineering). I have closely mentored many of these students and have co-authored publications with several.
Results: Students I have mentored have gone on to prestigious jobs in companies like Apple Inc., Texas Instruments, Capital One, and Intuitive Surgical, in government agencies (NIH), and to graduate programs/postdoc positions at GA Tech, the University of Washington, UCLA, UCSF, and the University of Pittsburgh, and have received prestigious fellowships (e.g., NIH F31 and the Turing Scholar Award). Goals As a mentor, I emphasize several goals, including but not limited to student-centered 1:1 mentoring and promoting honest, and vibrant scientific community citizenship.

Diversification strategy and community outreach

As an active member of the scientific community, I aim to attain the following objectives. Promote diversity at the university, department, and lab levels Creating pathways to success for students from all backgrounds fundamentally improves research impact. To increase diversity and inclusion in STEAM fields, I aim to provide mentorship, educational resources, and research opportunities to groups that have traditionally been underrepresented, including women, racial and ethnic minorities, and individuals from economically disadvantaged backgrounds. K-12 outreach I aim to spark curiosity, encourage critical thinking, and help K-12 students see themselves as future scientists, engineers, or innovators. Scientific community service It is my pleasure to support the next generation of scientists through the scientific community.

Code Repositories

We embrace open-source development and are pleased to support and contribute to the community. Codes and other resources for our publications and research efforts can be found on github through the following link: https://github.com/DeepLearningForPrecisionHealthLab

Positions Available

The Montillo Lab (www.montillolab.org ) in the Departments of Bioinformatics & Biomedical Engineering at the University of Texas Southwestern in Dallas, TX is looking for full-time postdocs and research scientists to develop novel machine learning (ML) approaches for analyzing medical images, clinical, multi-omic, and speech data. Our lab's primary focus is on developing the theory and application of ML and causal modeling to guide prognosis and treatment decisions and to elucidate treatment mechanisms for applications in neurological disorders and oncology. We develop the theory of ML by improving how ML models learn. Existing models merely quantify predictor-target correlations and fail to quantify causal relationships. These models do not handle aleatoric and epistemic uncertainty and don’t provide statistically meaningful covariate significance. Using our experience developing new deep learning (DL) frameworks that enable any neural network to handle sample clustering from repeat-measure (non-iid) data, we aim to develop approaches integrating ideas from causal discovery with Bayesian DL. In our clinical applications, for example in Parkinson’s Disease (PD), when standard drugs fail to provide adequate relief, deep brain stimulation (DBS) surgery can be restorative; however, there is no tool to identify who will respond or how it works. Based on our success in developing causal ML measures that predict PD trajectory, we aim to develop further models predictive of outcomes by fusing neurologists’ knowledge with probabilistic, interpretable deep learning. With cutting-edge computational infrastructure, access to leading neuropathophysiology and oncology experts, and an unparalleled trove of medical images, multi-omic data, and speech samples, our machine learning lab in the BME and bioinformatics departments of a leading academic medical center is poised for success in these research endeavors. What we need now are brilliant postdocs and a research scientist who are eager to innovate, think beyond traditional models, and explore bold new directions in biomedical research. Through close collaborations with neurologists, psychiatrists, surgeons, and neuroscientists, our lab offers truly interdisciplinary training: you will work on problems at the cutting edge of machine learning and pathophysiology. We are a dynamic and forward-thinking lab situated at the forefront of two rapidly growing departments committed to an entrepreneurial approach to research, with a flexible work culture and competitive compensation. Additionally, our university provides world-class computational resources and research-dedicated high field imaging so that your efforts are focused solely on scientific innovation.

To learn more about and apply to our positions, use the POSITIONS AVAILABLE menu (above) to navigate to the appropriate subsection.

UT Southwestern Medical Center is committed to an educational and working environment that provides equal opportunity to all members of the University community. As an equal opportunity employer, UT Southwestern prohibits unlawful discrimination, including discrimination on the basis of race, color, religion, national origin, sex, sexual orientation, gender identity, gender expression, age, disability, genetic information, citizenship status, or veteran status. To learn more, please visit this link.

Postdoctoral Fellowship Positions

Use the links below to read about and apply to our open postdoctoral fellowship positions:

  1. Explainable, causal machine learning
  2. Neuroimage and medical image analysis using foundation models
  3. Foundation models for image-guided cancer surgery
  4. Computational linguistics for speech impairment characterization

Explainable, causal machine learning for biomedicine

Job responsibilities
The central goal of this position is to develop new machine learning frameworks to overcome the limitations of current models. Some of these limitations include that users do not know when to trust the models since they don’t generally output a true probabilistic confidence and that models don’t account for epistemic and aleatoric uncertainty, nor biases in the dataset. In addition, models merely identify predictor-target correlations, rather than causal relationships. This position entails devising new Bayesian deep learning (DL) frameworks that provide statistically meaningful models that alleviate these limitations, while increasing performance and interpretability. Additionally, we will encode knowledge of pathophysiological processes to guide Bayesian causal discovery and deep learning inference, to attain a human + AI intelligence integration that is explainable and offers the greatest capacity to answer interventional and counterfactual queries.

Requirements
  • A PhD or MD/PhD in biomedical informatics, computer science, biomedical or electrical engineering, statistics, physics, or any related field providing a firm computational/analytical background.
  • A track record of publishing high-quality research papers in any of these fields.
  • Strong expertise and hands-on experience innovating new machine learning frameworks or individual novel methods.
  • Strong programming ability in python, R, C/C++, or another programming language used in data science.

Previous experience in explainable AI, causal inference/discovery, Bayesian neural networks, or probabilistic machine learning, is advantageous, but not mandatory. Advanced probability and statistics are also strengths for this position, particularly when combined with a commitment to mastering machine learning.

Apply

The postdoctoral fellowship position is available immediately and we will accept applications until the position is filled; early application is strongly recommended. To apply, please send (1) a detailed curriculum vitae with publication list, (2) the names and contact information of three references, (3) and PDFs of your two most significant publications or preprints using this link: Albert A. Montillo, Ph.D. (albert.montillo@utsouthwestern.edu).

Neuroimage and medical image analysis using foundation models

Job responsibilities

A central objective of this position is to develop novel deep learning models to predict diagnoses and outcomes from patient data including imaging, such as multicontrast MRI (fMRI, diffusion MRI, MEG/EEG, PET/SPECT) and corresponding genomic, metabolic and clinical data.
By helping to discover image-based biomarkers, including advanced brain connectivity measures, and differentially expressed metabolic markers and genes, you will help improve early and accurate disease diagnosis, and develop tools to predict treatment outcomes in mental & neurodevelopmental disorders and neurodegenerative diseases.
Your methods will also be used to optimize non-invasive brain stimulation therapies.

Requirements
  • A PhD or MD/PhD in biomedical informatics, computer science, biomedical or electrical engineering, statistics, physics, or any related field providing a firm computational/analytical background.
  • A track record of publishing high-quality research papers in any of these fields.
  • Strong expertise and hands-on experience processing medical image data using machine learning.
  • Strong programming ability in python, R, C/C++, or another programming language used in data science.

Previous experience in neuroimage analysis (image formats and preprocessing pipelines), and explainable AI methods is advantageous, but not mandatory. Outstanding candidates with a strong neuroscience or radiology background may also be considered if they have exhibited a commitment to mastering machine learning.

Apply

The postdoctoral fellowship position is available immediately and we will accept applications until the position is filled; early application is strongly recommended. To apply, please send (1) a detailed curriculum vitae with publication list, (2) the names and contact information of three references, (3) and PDFs of your two most significant publications or preprints using this link: Albert A. Montillo, Ph.D. (albert.montillo@utsouthwestern.edu).

Foundation models for image-guided cancer surgery

Job responsibilities
A central goal of this position is to improve outcomes in endoscopy-guided cancer surgery, working closely with our surgeons in the Otolaryngology - Head & Neck surgery department. The complexity of soft-tissue endoscopy video has meant that the analysis of this rich source of information using traditional machine learning is inadequate to guide surgery and improve outcomes. We are well-positioned to change the status quo by developing performant DL-based models and segmentation methods. We aim to do this by (1) exploiting a successful pan-cancer contrast agent (dye) developed at UTSW as grounding information and (2) integrating expert surgeon knowledge, foundation models, and our large endoscopy database.

Requirements
  • A PhD or MD/PhD in biomedical informatics, computer science, biomedical or electrical engineering, statistics, physics, or any related field providing a firm computational/analytical background.
  • A track record of publishing high-quality research papers in any of these fields.
  • Strong expertise and hands-on experience processing image and/or video data.
  • Strong programming ability in python, R, C/C++, or another programming language used in data science.

Previous experience in segmentation, endoscopy analysis, and foundation models is advantageous, but not mandatory. Outstanding candidates with a strong oncology or radiology background may also be considered if they have exhibited a commitment to mastering machine learning.

Apply

The postdoctoral fellowship position is available immediately and we will accept applications until the position is filled; early application is strongly recommended. To apply, please send (1) a detailed curriculum vitae with publication list, (2) the names and contact information of three references, (3) and PDFs of your two most significant publications or preprints using this link: Albert A. Montillo, Ph.D. (albert.montillo@utsouthwestern.edu).

Computational linguistics for speech impairment characterization

We are seeking a highly motivated and skilled Postdoctoral Fellow to join the lab of Dr. Albert Montillo at the University of Texas Southwestern Departments of Bioinformatics and BME. This position offers an exciting opportunity to contribute to cutting-edge research in the development of diagnostic tools using Large Language Models (LLM). The successful candidate will play a pivotal role in advancing our understanding of LLM applications in neurological disorders such as Alzheimer’s.

Job responsibilities
  • Conduct research and development activities using state-of-the-art Large Language Models (LLM) for processing speech in a clinical environment. Orchestrate LLMs running locally and remotely.
  • Collaborate with a multidisciplinary team of researchers to design and implement novel algorithms and models for the analysis of clearly and unclearly spoken text.
  • Analyze large-scale datasets, extract relevant biomarkers, and develop innovative approaches to improve the accuracy and efficiency of diagnostic information from speech.
  • Evaluate algorithm performance using rigorous experiment design and benchmarks.
  • Publish research findings in high-impact journals and present at scientific conferences and seminars.
  • Assist in mentoring junior researchers.
  • Develop grant writing skills.
Requirements
  • A PhD or MD/PhD in biomedical informatics, computer science, biomedical or electrical engineering, statistics, physics, or any related field providing a firm computational/analytical background.
  • A track record of publishing high-quality research papers in any of these fields.
  • Strong expertise and hands-on experience in using LLMs for text processing.
  • Strong programming ability in python, R, C/C++, or another programming language used in data science.

Previous experience in speech analysis, computational linguistics, and audio/voice analysis is highly advantageous, but not mandatory. Experience in speech impairment is desirable. Outstanding candidates with a strong neuroscience, neuropsychology, or cognitive psychology background may also be considered if they have exhibited a commitment to mastering machine learning.

Apply

The postdoctoral fellowship position is available immediately and we will accept applications until the position is filled; early application is strongly recommended. To apply, please send (1) a detailed curriculum vitae with publication list, (2) the names and contact information of three references, (3) and PDFs of your two most significant publications or preprints using this link: Albert A. Montillo, Ph.D. (albert.montillo@utsouthwestern.edu).

Research Scientist Positions

Use the links below to read about and apply to our open research scientist positions:

  1. Machine learning for biomedical data analysis, foundation models and explainable AI

Machine learning for biomedical data analysis, foundation models, and explainable AI

Our lab’s focus is on developing the theory and application of deep learning (DL) and causal modeling to elucidate treatment mechanisms, and to guide prognosis and treatment decisions with applications in neurological disorders and oncology. With cutting-edge computational infrastructure, access to leading experts in neurology, neuroscience, and cancer surgery, and an unparalleled trove of medical images and multi-omic data, our machine learning lab in the BME and bioinformatics departments of a leading university and academic medical center is poised for success in these research endeavors. What we need now are motivated research scientists who are eager to apply their skills, think beyond traditional approaches, and develop bold new applications in biomedical research.

Job responsibilities
  • Construction of machine learning models for biomedical data analysis.
  • Development and implementation of methods for explainable AI revealing intricacies about what our models have learned.
  • Neuroimage and medical image analysis pipeline development.

It is expected that the research scientist will work closely with postdoctoral research fellows and the PI, implementing solutions for our clinical and basic science collaborators. Our clinical collaborations entail developing tools to help physicians select the best treatment for conditions related to mental health, neurodegeneration, and neurodevelopmental disorders. In our basic science collaborations, we are identifying new causal biomarkers of disease pathophysiology.

Requirements
  • A master’s or PhD in computer science, biomedical or electrical engineering, statistics, physics, or any related field providing a firm computational/analytical background.
  • Publications demonstrating the application of leading methods and/or algorithmic innovation.
  • Strong programming ability and experience with machine learning.
  • Software maintenance with soruce control (e.g., git), documentation, containerization, and efficient builds (e.g., cmake).

Previous experience in image analysis (e.g. MRI, endoscopy), PEFT for FMs, explainable AI, causal discovery/ inference is advantageous, but not mandatory. Candidates with a strong neuroscience, oncology, or radiology background may be considered if they have exhibited a commitment to mastering ML.

Apply

This research scientist position is available immediately and we will accept applications until the position is filled; early application is strongly recommended. To apply, please send (1) your resume or CV including a list of publications, (2) transcript of college courses completed if available (unofficial is acceptable), (3) links to code repositories you have authored, and (4) contact information for three references using this link: Albert A. Montillo, Ph.D. (albert.montillo@utsouthwestern.edu).

PhD Student Research Assistant Positions

Current students (Ph.D. students at UT Southwestern, University of Texas Dallas (UTD), University of Texas Arlington (UTA), Southern Methodist University (SMU), and MSTP MD/Ph.D. students) interested in joining our team, should arrange a meeting by reaching out via this link Albert A. Montillo, Ph.D. (albert.montillo@utsouthwestern.edu). The research experience in our lab provides a great opportunity to supplement your background in computer science, engineering, statistics, physics or neuroscience with robust training in scientific algorithm development and computational modeling while conducting cutting-edge research to advance the theory and application of machine learning for medical image analysis.

Prospective students should mention my name (Albert Montillo) on their application and need to apply for UTSW graduate school admission before the university’s FIXED December 1st deadline, and preferably by November 1st. Be sure to explicitly indicate your interest in my lab in your application. For prospective Ph.D. students, the Ph.D. program in Biomedical Engineering, and in particular the Imaging Track, is one often pursued by students in our lab. Other suitable tracks include Medical Physics, Molecular Biophysics, and Computational Biology. Computer Science and Engineering (BME/EE) programs at UTD, UTA, and SMU are also suitable programs for our lab. For M.D./Ph.D. applicants, the MSTP admission deadline is November 1st.

News


December 2024

Albert to attend NeurIPS.

November 2024

Our breast cancer prognostics paper is featured in this NVIDIA news article.

October 2024

Aixa wins a travel award for the Best Bioinformatics Scientific Presentation. Way to go, Aixa!!
Albert, Aixa, and Ameer instruct scientists, students and postdocs in advanced deep learning.

September 2024

Youngest member of our lab is born, Khang An Nguyen. Congrats to Son & Thuong!
Aixa unconditionally passes qualifying exam 2, dissertation proposal. Nice job Aixa!

May 2024

A CBIIT news article featuring our Breast Cancer prognostics machine learning model is live at this link
Albert serves as a grant reviewer on an NSF panel and on an NIH study section.

April 2024

Our breast cancer research on: Machine Learning Prediction of Lymph Node Metastasis in Breast Cancer: Performance of a Multi-institutional MRI-based 4D Convolutional Neural Network, appears in the journal, Radiology:Imaging Cancer and is accessible via this link

March 2024

Albert is promoted to Associate Professor with Tenure.

February 2024

Our method for the Longitudinal prognosis of Parkinson’s outcomes using causal connectivity appears in the journal NeuroImage:Clinical and is accessible via this link

December 2023

Our paper on machine learning for improving the reproducibility of functional and causal connectivity from functional MRI is accepted into the Journal of Neural Engineering. Congratulations Cooper!

August 2023

Dr. Montillo teaches scientific programming with Python in our bootcamp to incoming PhD students.

June 2023

Dr. Montillo gives an invited talk at Oxford University in the UK. Albert serves as a grant reviewer on NIH study section.
Adam Wang joins the lab. Welcome Adam!

May 2023

Dr. Montillo gives an invited talk at NYU in NYC, NY. Albert, Son and Austin teach SW engineering for Research to incoming PhD students.
Dr. Montillo attends Royal Society Mtg, London, UK.

March 2023

More progress on our Parkinson’s Imaging study. Way to go team!

February 2023

Our paper on Mixed Effects Deep Learning is accepted into IEEE TPAMI. Congratulations Kevin!
Dr Montillo gives an invited talk at SMU. Dr Montillo teaches Biomedical Informatics and Biostatistics this semester.

January 2023

Alex joins PCCI as a research scientist. Congratulations! Montillo lab is awarded an R01 grant from NIGMS for the next 5 years.

December 2022

Alex successfully defends. Way to go Alex.
Austin joins the lab. Welcome Austin!

November 2022

Dr Montillo gives a seminar to the Neurology dept, UTSW and an invited talk at the Bioengineering Dept at UTD.
Cooper gives an invited presentation to NIH NINDS. Nice job Cooper!

August 2022

Alex's work on machine learning for glaucoma diagnosis is published in Clinical Ophthalmology. Great job Alex!
Dr. Montillo teaches mathematical modeling with python in the Programming Bootcamp at UTSW along with TAs: Aixa and Alex.

June 2022

Cooper and Kevin successfully defend their PhD theses. Congratulations!
Summer lab pool party at the Montillo's residence. Fun in the sun celebrating our many successes this year!

May 2022

Dr. Montillo gives an invited talk on MEG artifact suppression via spatiotemporal deep learning at the ASNR conference.
Krishna's paper on brain segmentation via deep learning is accepted into this year's OHBM conference.
Dr. Montillo teaches module 2 (Object Oriented Programming) in the Bioinformatics Software Engineering course

April 2022

PhD student, Aixa X. Andrade joins the lab. Welcome Aixa!
Albert prepares a new course on Architectures and Applications of Deep Learning with a focus on GANs and VAEs.

March 2022

Welcome to rotation students Austin Marckx and Conor McFadden!

February 2022

Kevins's manuscript is featured in multiple press releases at UTSW, Forbes, and Science Daily. Awesome, Kevin!
Karel's manuscript defining metabolites predictive of Alzheimer’s Disease in blood plasma and donated brain tissue was accepted into the Journal of Alzheimer’s Disease. Nice, Karel!

January 2022

Vyom's manuscript on the pitfalls and recommended strategies and metrics to suppress fMRI motion artifacts is accepted into Neuroinformatics. Excellent work, Vyom!
Cooper's manuscript detailing the reproducible neuroimaging features that enable the diagnosis of Autism Spectrum Disorder with machine learning is accepted into the journal Scientific Reports. Nice job Cooper.

November 2021

Alex’s manuscript on automated removal of artifact from magnetoencephalography is published in NeuroImage. Awesome job Alex!


September 2021

Kevin Nguyen’s manuscript on the prediction of Antidepressant outcomes for Major Depressive Disorder patients from fMRI is published in Biological Psychiatry. Well done Kevin!


August 2021


Albert teaches Python Programming Bootcamp to incoming PhD students at UTSW.


March 2021

Congratulations to former undergraduate student Yenho Chen who has been admitted to the Machine Learning Ph.D. program at Georgia Tech with the President’s Fellowship award.
Congratulations to former high school student Meyer Zinn who was admitted to the Computer Science BS program at the University of Texas Austin with the Turing Scholarship award.


February 2021

Kevin Nguyen’s manuscript on Parkinson’s disease prognostics from fMRI is published in Parkinsonism and Related Disorders. Nice job Kevin!


January 2021

Welcome to undergraduate Atef Ali, who has joined the lab as part of the UTSW Bioinformatics Gap Year program! Also welcome to rotation student Mahak Virlley!


September 2020

Son Nguyen gives a talk at MICCAI on predicting breast cancer metastases to the axillary lymph nodes. Well done Son!


August 2020

Group outing: team Montillo takes to the Katy trail for a morning bike ride. Fun!


July 2020

Team Montillo hosts several codefests exchanging best ML implementation practices.


June 2020

Vyom graduates and is accepted into the MD/PhD program at the University of Washington. Great news!


May 2020

Son Nguyen's breast cancer prognostics full-length peer reviewed paper is accepted into premier conference, MICCAI. Awesome!


April 2020

Cooper reveals features important for deep learning model to diagnose Autism at ISBI. Vyom presents an omnibus model for improved motion suppression in fMRI. Excellent work!


March 2020

Vyom presents prediction of individual's rate of Parkinson's progression at ICASSP from biomechanics. Well done.


February 2020

Kevin Nguyen presents first ever data augmentation approach for 4D fMRI which improves prediction performance at SPIE Medical Imaging. Nice job Kevin!


January 2020


Kevin and Albert present methods for Major Depression Disorder treatment response prediction at UTSW.


November 2019

Welcome Postdoctoral Fellow, Son Nam Nguyen!
Albert chairs the session on Artificial Intelligence in Radiology at the annual ASFNR meeting in San Francisco, CA.


September 2019

NIH F31 fellowship awarded to Cooper. Congratulations Cooper!


June 2019

Albert attends ICML and CVPR in Los Angeles


June 2019

Yenho Chen receives Postbaccalaureate Intramural Research Training Award and starts a research scientist position at NIH's Center for Multimodal Neuroimaging within Dr Pereira's Machine Learning Team. Way to go Yenho!


May 2019

Albert gives invited talk on Machine Learning to radiologists at the American Society of Neuroradiology (ASNR) in Boston, MA


April 2019

Multiple F30 and F31 fellowships submitted. Way to go students!!
Cooper presents his research on Autism diagnosis and Kevin’s research on Major Depression Disorder at ISBI in Italy.
Wedding bells for Alex. Congrats Alex!!


March 2019

Albert teaches deep learning in the UTSW Bioinformatics nanocourse, Machine Learning.
It’s a boy! Baby Anthony born to Albert and Andrea. Wohoo!!


February 2019

Alex Treacher presents on Liver Fibrosity diagnosis at SPIE Medical Imaging. Go Alex!


January 2019

Welcome Green Fellow student Vyom Raval!


December 2018

New website goes live! Thank you for visiting!
Albert gives invited conference talk at Brain Informatics conference.


November 2018

Bioinformatics Dept Hackathon 2018 is a super success; congrats Alex for winning an award!


October 2018

Welcome new MSTP graduate student Cooper!
Welcome rotation student Paul!


July 2018

Welcome new MSTP graduate student Kevin!


May 2018

Welcome new graduate student Alex!


April 2018

Albert gives invited talk, Deep learning for artifact detection in MEG, at the 2018 International Workshop on Interactive and Spatial Computing (IWISC).


March 2018

Albert joins program committee of the SPIE Medical Imaging conference.


February 2018

Behrouz and Gowtham deliver oral presentations at SPIE Medical Imaging conference in Houston, TX.
A team of bioinformatics researchers (Drs. Montillo, Rajaram and Cobanoglu) develop and teach a new nanocourse, Machine Learning I, to researchers (grad students, postdocs, faculty) from across the UTSW. Highly positive reviews! Plans underway for subsequent offerings.
Welcome to our new scientific programmer Danni!


January 2018

Albert gives invited talk, Deep learning: a new tool for analyzing Big Neuroimaging Datasets at the jointly (UTD, UTSW) sponsored symposium, Neuroimaging is a team sport.


December 2017

Albert receives appointment in the newly formed Bioinformatics Department at UTSW
Albert trains researchers in neuroimage analysis with SPM.


November 2017

Albert is interviewed for research contributions in machine learning for radiology at RSNA.


September 2017

2 papers presented at the International conference, Medical Image Computing and Computer Assisted Intervention (MICCAI) including Convolutional Neural Networks for artifact detection in MEG, and deep neural networks for quantifying the association between type-2 diabetes management and brain perfusion measured via ASL MRI.


August 2017

2 abstracts accepted to American Society for Functional Neuroradiology (ASFNR). Congratulations Behrouz and Gowtham!


July 2017

2 papers presented at Pattern Recognition for Neuro Imaging (PRNI) on 3D convolutional neural networks for resting state network labeling for rs-fMRI and deep convolutional neural networks for MEG cardiac artifact detection.
1 paper presented at Human Brain Mapping conference on machine learning that uses resting state fMRI to accurately predict head impact exposure in youths playing a single season of football.


May 2017

Albert joins the Research Committee of American Society of Neuroradiology.
Albert teaches Mathematics for Medicine to medical students at UTSW including topics of Bayesian Decision Theory and Deep Learning.


April 2017

Afarin Famili successfully defends master’s thesis using machine learning to detect functional connectivity changes in epilepsy & diabetes. Congrats Afarin!
Albert gives invited conference talk: Machine Learning in functional Neuroimaging at the American Society of Neuroradiology in Los Angeles.
4 papers presented at UTSW Radiology Research Day by Prabhat Garg, Gowtham Murugesan, Afarin Famili!
Gowtham Murugesan presents 2 papers at IEEE International Symposium on Biomedical Imaging (ISBI) in Melbourne, Australia


March 2017

Abstract accepted to Organization for Human Brain Mapping (OHBM) conference


February 2017

Two papers Accepted to IEEE International Symposium on Biomedical Imaging (ISBI) Meeting Conference!
Albert attends annual mtg of American clinical MEG society (ACMEGS).


January 2017

Welcome to our new postdoc, Behrouz and research scientist, Anand to the lab!


December 2016

Welcome aboard new trainee, Gowtham!


Sept 2016

Welcome aboard graduate student, Afarin to the lab!


June 2016

King Foundation grant awarded to Drs. Montillo and Moore.


May 2016

Albert gives invited conference talk at American Society of Neuroradiology (ASNR) in Washington DC: Machine Learning for Neuroimaging.


February 2016

Albert gives seminar talks to Mathematics Dept at UT Dallas and Statistics Dept at Southern Methodist University.


November 2015

Albert attends MEG training at McGill.



Former Lab Members and Trainees

Alex Treacher

Molecular Biophysics

PhD student

Kevin Nguyen

Biomedical Engineering

MD/PhD student

Cooper Mellema

Biomedical Engineering

MD/PhD student

Vyom Raval, B.S.

Neuroscience. UTSW Greenfellow

Undergraduate Researcher

Prabhat Garg, M.D.

School of Medicine

Physician Scientist

Janis Iourovitski

Biomedical Engineering

AMGEN scholar

James Yu

School of Medicine.

Radiology resident

Meyer Zinn

St. Mark's School of Texas

High school researcher

Yenho Chen, B.S.

UTD/UTSW Greenfellow

Undergraduate Researcher

Behrouz Saghafi, Ph.D.

Postdoctoral Fellow

Anand Kadumberi, M.S.

Research Scientist

Mahak Virlley

Neuroscience

PhD student

Afarin Famili, B.S.

Masters Student

Danni Luo, M.S.

Research Scientist

Get in touch

Deep Learning for Precision Health lab (Montillo Lab)
Department of Bioinformatics
Department of Biomedical Engineering
UT Southwestern Medical Center
5323 Harry Hines Blvd.
J9.130b Dallas, TX 75390
Ph: (469) 684-2852 (Isha Shah, our administrator)
Email

Isha.Shah@utsouthwestern.edu

The Department of Bioinformatics is located in the J building at 5323 Harry Hines Blvd., Dallas, Texas. For visitors driving here, from Harry Hines Blvd., turn southwest onto Sen. Kay Bailey Hutchison Drive. Take the first right onto a drive that leads to Lot 7, Visitor Parking. See Rebekah Craig during your visit for a parking pass. From Visitor Parking, cross the street to the Donald Seldin Plaza. Walk across the plaza to the right, go down the steps and walk past the koi fish pond, across the next courtyard, in between the archway formed by the G and J buildings. At the right side under the archway, enter the J building and take the elevator to the 9th floor. Exit the elevators and then turn left to find the Department of Bioinformatics entrance consisting of a double glass doorway. Our offices are halfway down on the right and a team member can escort you to your meeting.

UT Southwestern has full information on parking.

We are accessible by public transportation.

We are an 8 minute walk from the Southwestern Medical District/Parkland Station on the DART green and orange rail lines and 5 min walk from the Medical/Market Station on the Trinity Railway Express.