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.
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.
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.
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.
Associate Professor
Biomedical Informatics & Biomedical Engineering
Principal Investigator
Faculty Page
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
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
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.
Use the links below to read about and apply to our open postdoctoral fellowship positions:
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.
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).
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.
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.
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).
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.
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).
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.
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.
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).
Use the links below to read about and apply to our open research scientist positions:
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.
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.
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.
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).
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.
Albert to attend NeurIPS.
Our breast cancer prognostics paper is featured in this NVIDIA news article.
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.
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!
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.
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
Albert is promoted to Associate Professor with Tenure.
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
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!
Dr. Montillo teaches scientific programming with Python in our bootcamp to incoming PhD students.
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!
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.
More progress on our Parkinson’s Imaging study. Way to go team!
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.
Alex joins PCCI as a research scientist. Congratulations! Montillo lab is awarded an R01 grant from NIGMS for the next 5 years.
Alex successfully defends. Way to go Alex.
Austin joins the lab. Welcome Austin!
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!
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.
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!
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
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.
Welcome to rotation students Austin Marckx and Conor McFadden!
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!
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.
New website goes live! Thank you for visiting!
Albert gives invited conference talk at Brain Informatics conference.
Bioinformatics Dept Hackathon 2018 is a super success; congrats Alex for winning an award!
Welcome new MSTP graduate student Cooper!
Welcome rotation student Paul!
Welcome new MSTP graduate student Kevin!
Welcome new graduate student Alex!
Albert gives invited talk, Deep learning for artifact detection in MEG, at the 2018 International Workshop on Interactive and Spatial Computing (IWISC).
Albert joins program committee of the SPIE Medical Imaging conference.
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!
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.
Albert receives appointment in the newly formed Bioinformatics Department at UTSW
Albert trains researchers in neuroimage analysis with SPM.
Albert is interviewed for research contributions in machine learning for radiology at RSNA.
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.
2 abstracts accepted to American Society for Functional Neuroradiology (ASFNR). Congratulations Behrouz and Gowtham!
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.
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.
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
Abstract accepted to Organization for Human Brain Mapping (OHBM) conference
Two papers Accepted to IEEE International Symposium on Biomedical Imaging (ISBI) Meeting Conference!
Albert attends annual mtg of American clinical MEG society (ACMEGS).
Welcome to our new postdoc, Behrouz and research scientist, Anand to the lab!
Welcome aboard new trainee, Gowtham!
Welcome aboard graduate student, Afarin to the lab!
King Foundation grant awarded to Drs. Montillo and Moore.
Albert gives invited conference talk at American Society of Neuroradiology (ASNR) in Washington DC: Machine Learning for Neuroimaging.
Albert gives seminar talks to Mathematics Dept at UT Dallas and Statistics Dept at Southern Methodist University.
Albert attends MEG training at McGill.
Molecular Biophysics
PhD student
Biomedical Engineering
MD/PhD student
Biomedical Engineering
MD/PhD student
Neuroscience. UTSW Greenfellow
Undergraduate Researcher
School of Medicine
Physician Scientist
Biomedical Engineering
AMGEN scholar
School of Medicine.
Radiology resident
St. Mark's School of Texas
High school researcher
UTD/UTSW Greenfellow
Undergraduate Researcher
Postdoctoral Fellow
Research Scientist
Neuroscience
PhD student
Masters Student
Research Scientist
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.