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 therapy decision making. We move 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 acquisitions techniques and develop optimized post-processing for: multi-contrast MRI, EEG/MEG, PET/SPECT.


Advancing the Theory of Deep Learning

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How can deep learning models be optimally tailored to each new problem to maximize prediction performance? How can domain expertise from clinicians be embedded into deep learning models? How can causal information be extracted in longitudinal deep learning data analyses to avoid reliance on purely correlation based information? We are tackling these problems 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 the application of deep learning

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How can we improve our currently subjective diagnoses of subtypes brain disorders and diseases that have overlapping symptoms? How can we identify new gene targets for spectrum disorders using the exquisite phenotypes provided by 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 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 facilitates getting patients 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 and identify the best treatment for each individual patient.


About the lab

The lab is an active part of the Lyda Hill Department of Bioinformatics. We are also affiliated with the Research Division of the Radiology Department and the Advanced Imaging Research Center.



LAB MEMBERS


Albert Montillo, Ph.D.

Principal Investigator

Faculty Page


Alex Treacher

Bio-Physics

PhD student

Kevin Nguyen

Biomedical Engineering

MD/PhD student

Cooper Mellema

Biomedical Engineering

MD/PhD student

Danni Luo

Bioinformatics

Scientific Programmer

Yenho Chen

UTD/UTSW Greenfellow

Undergraduate Researcher

Vyom Raval

UTD/UTSW Greenfellow

Undergraduate Researcher


Meet the PI

Our PI, Albert Montillo is an Assistant Professor in the Lyda Hill Department of Bioinformatics with secondary appointments in the Department of Radiology, the Advanced Imaging Research Center, and Biomedical Engineering within the Graduate School of Biomedical Sciences. He also directs the research of the Deep Learning for Precision Health lab. He is also an Adjunct Professor at UT Dallas in the School of Engineering and Computer Science.

Dr. Montillo received bachelor of science and master of science degrees in Computer Science from RPI and minor concentrations in Electrical Engineering and Cognitive science/Psychology. He obtained his PhD in 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. During his studies, Dr. Montillo 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. After his studies, Dr. Montillo developed a deep learning approach for the decision forest, known as entanglement, which improves prediction accuracy and increases prediction speed while a researcher at the Machine Intelligence and Perception group of Microsoft Research in Cambridge, United Kingdom. Subsequently he joined as a Lead Scientist at General Electric Research Center in upstate New York where 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 for automated individualized disease progression prediction. His efforts led to fully automated, machine learning based identification of the content of a imaging scan which prepares a wide range of clinical image data for automated analyses and enables radiation dosage reduction in computed tomography via scout-scans.


Publications


** = Corresponding author

Alex Treacher, Daniel Beauchamp, Bilal Quadri, Abhinav Vij, David Fetzer, Takeshi Yokoo, Albert Montillo**, Application of Deep Learning Convolutional Neural Networks to the Estimation of Liver Fibrosis Severity from Ultrasound Texture, SPIE Medical Imaging, 2019.

Debadatta Dash, Paul Ferrari, Saleem Malik, Albert Montillo, Joseph Maldjian, Jun Wang, Determining the Optimal Number of MEG Trials: A Machine Learning and Speech Decoding Perspective, International Conference on Brain Informatics, 2018

Gowtham Murugesan, Behrouz Saghafi, Elizabeth Davenport, Ben Wagner, Jillian Urban, Christopher Whitlow, Joel Stitzel, Joseph A. Maldjian, Albert Montillo**, Single football season changes in resting state fMRI of the Default Mode Network and hippocampal regions correlate with Visuospatial Inhibitory Attention Performance in Youth and High School Players, International Symposium on Biomedical Imaging (ISBI), 2018.

Gowtham Murugesan, Behrouz Saghafi, Elizabeth Davenport, Ben Wagner, Jillian Urban, Mireille Kelley, Derek Jones, Alex Powers, Christopher Whitlow, Joel Stitzel, Joseph Maldjian, Albert Montillo**, 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, SPIE Medical Imaging, 2018.

Behrouz Saghafi, Gowtham Murugesan, Elizabeth Davenport, Ben Wagner, Jillian Urban, Mireille Kelley, Derek Jones, Alexander Powers, Christopher Whitlow, Joel Stitzel, Joseph Maldjian, Albert Montillo**, 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, SPIE Medical Imaging, 2018.

Prabhat Garg, Elizabeth M. Davenport, Gowtham Murugesan, Christopher Whitlow, Joseph Maldjian, Albert Montillo**, Using Convolutional Neural Networks to Automatically Detect Eye-Blink Artifacts in Magnetoencephalography, Medical Image Computing and Computer Assisted Intervention (MICCAI), September 2017

Behrouz Saghafi, Benjamin C. Wagner, S. Carrie Smith, Jianzhao Xu, Jasmin Divers, Ananth Madhuranthakam, Barry I. Freedman, Joseph A. Maldjian, and Albert A. Montillo**, Deep Fully Connected Neural Network for Estimation of Caudate Perfusion from Clinical Parameters in African Americans with Type 2 Diabetes, Medical Image Computing and Computer Assisted Intervention (MICCAI) Deep Learning for Medical Image Analysis, 2017

Behrouz Saghafi, Benjamin C. Wagner, S. Carrie Smith, Jianzhao Xu, Jasmin Divers, Ananth Madhuranthakam, Barry I. Freedman, Joseph A. Maldjian, Albert A. Montillo**, Association of Regional Brain Perfusion with Diabetes, Renal and Cardiovascular Disease measures in African Americans with Type 2 Diabetes, American Society for Functional Neuroradiology (ASFNR), 2017

Gowtham Murugesan, Thomas O’Neill, Elizabeth Davenport, Ben Wagner, Jillian Urban, Mireille Kelley, Derek Jones, Alex Powers, Christopher T. Whitlow, Joel D. Stitzel, Joseph A. Maldjian, Albert Montillo, Automatic Labeling of Resting State fMRI Networks using 3D Convolutional Neural Networks, American Society for Functional Neuroradiology (ASFNR), 2017

Thomas J. O’Neill, Elizabeth M. Davenport, Gowtham Murugesan, Joseph Maldjian, Albert Montillo**, Applications of rs-fMRI to Traumatic Brain Injury, Neuroimaging Clinics, 2017

Prabhat Garg, Elizabeth Davenport, Gowtham Murugesan, Ben Wagner, Christopher Whitlow, Joseph Maldjian, Albert Montillo**, Automatic Multiple MEG Artifact Detection using 1-D Convolutional Neural Networks without Electrooculography or Electrocardiography, Pattern Recognition in Neuroimaging (PRNI), June 2017

Gowtham Murugesan, Afarin Famili, Elizabeth Davenport, Ben Wagner, Jillian Urban, Mireille Kelley, Derek Jones, Christopher Whitlow, Joel Stitzel, Joseph Maldjian, Albert Montillo**, Intra-Default Mode Network Connectivity Changes from a Single Season of Youth Football Distinguish Levels of Head Impact Exposure, Radiological Society of North America, 2017

Afarin Famili, Gowtham Murugesan, Ben Wagner, S. Carry Smith, Jianzhao Xu, Jasmin Divers, Barry I. Freedman, Joseph A. Maldjian, Albert A. Montillo**, 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

Gowtham Murugesan, Prabhat Garg, Thomas O’Neil1, Ben Wagner, Christopher Whitlow, Joseph Maldjian, Albert Montillo**, Automatic Labeling of Resting State fMRI Networks using 3D Convolutional Neural Networks, Pattern Recognition in Neuroimaging (PRNI), 2017.

Gowtham Murugesan, Afarin Famili, Elizabeth Davenport, Ben Wagner, Jillian Urban, Mireille Kelley, Derek Jones, Christopher Whitlow, Joel Stitzel, Joseph Maldjian, Albert Montillo**, Changes in resting state fMRI in one season of youth football distinguish non-impact athletes, low, and high impact exposure players, Human Brain Mapping Conference, 2017

Afarin Famili, Gowtham Krishnan, Elizabeth Davenport, James Germi, Ben Wagner, Bradley Lega, Albert Montillo**, Automatic Identification of Successful Memory Encoding In Stereo EEG Of Refractory, Mesial Temporal Lobe Epilepsy, International Symposium on Biomedical Imaging, 2017

Gowtham Murugesan, Afarin Famili, Elizabeth Davenport, Ben Wagner, Jillian Urban, Mireille Kelley, Derek Jones, Christopher Whitlow, Joel Stitzel, Joseph Maldjian, Albert Montillo**, Changes in Resting State MRI Networks from a Single Season Of Football Distinguishes Controls, Low, And High Head Impact Exposure, International Symposium on Biomedical Imaging, 2017

Menze, Bjoern; Langs, Georg; Montillo, Albert; Kelm, Michael; Müller, Henning; Zhang, Shaoting; Cai, Weidong; Metaxas, Dimitris, Medical Computer Vision: Algorithms for Big Data, Springer Lecture Notes in Computer Science Vol 9601, 2016

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. Medical Physics Vol 42 No 5 2730-9, 2015

Albert Montillo, Qi Song, Bipul Das, Zhye Yin, Hierarchical Pictorial Structures for Simultaneously Localizing Multiple Organs in Volumetric Pre-Scan CT, Medical Imaging 2015

Menze, Bjoern; Langs, Georg; Montillo, Albert; Kelm, Michael; Muller, Henning; Zhang, Shaoting; Weidong, Cai; Metaxas, Dimitri, Medical Computer Vision: Algorithms for Big Data, Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer 2015.

Albert Montillo**, Shantanu Sharma, Marcel Prastawa, Feature Selection and Imaging-Genetics Predictions Using a Sparse, Extremely Randomized Forest Regressor with application to Alzheimer's disease, Medical Image Computing and Computer-Assisted Intervention 2014

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 pp 748-751, 2013

Montillo A**, Song Q, Liu X, Miller JV, Parsing radiographs by integrating landmark set detection and multi-object active appearance models, Proc SPIE Int Soc Opt Eng Mar 8669 86690H, 2013

Liu X, Montillo A, Tan ET, Schenck JF, Mendonca P, Deformable atlas for multi-structure segmentation, Med Image Comput Comput Assist Interv 16 Pt 1 743-50, 2013

Zhennan Yan, Shaoting Zhang, Xiaofeng Liu, Dimitris N. Metaxas, Albert Montillo**, Accurate whole-brain segmentation for Alzheimer's disease combining an adaptive statistical atlas and multi-atlas, Medical Image Computing and Computer-Assisted Intervention 2013

Albert Montillo**, Qi Song, Roshni Bhagalia, and 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

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 22 pp 184-96, 2011

Iglesias JE, Konukoglu E, Montillo A, Tu Z, Criminisi A, Combining generative and discriminative models for semantic segmentation of CT scans via active learning, Inf Process Med Imaging 22 pp 25-36, 2011

Albert Montillo**, Dimitris Metaxas, Leon Axel, Incompressible biventricular model construction and heart segmentation of 4D tagged MRI: application to right ventricular hypertrophy, Medical Image Computing and Computer-Assisted Intervention 2010

Axel L, Montillo A, Kim D, Tagged magnetic resonance imaging of the heart: a survey, Med Image Anal Aug 9 4 pp 376-93, 2005

Kyoungju Park, Albert Montillo, Dimitris Metaxas, Leon Axel, An anatomical heart model for segmentation, analysis and classification, Communications of the ACM 2005

Albert Montillo**, Leon Axel, Dimitris Metaxas, Extracting tissue deformation using Gabor filter banks, Medical Imaging 2004

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 Jan 33 3 pp 341-55, 2002



Positions Available


The laboratory of Albert Montillo in the Bioinformatics Department at the UT Southwestern Medical Center is an interactive and collaborative team conducting cutting-edge research to advance the theory and application of machine learning for medical image analysis. We address unmet clinical needs by forming predictive models that make diagnoses and prognoses more precise and advance neuroscience by furthering the understanding of mechanisms in disease and intervention. Medical image analysis software the lab has developed include machine learning-based methods for labeling structures throughout the brain (parcellation), versions of which are used worldwide and FDA approved. The lab has built deep learning methods to label networks in resting state fMRI and detect artifacts in MEG. The lab has pioneered deep learning decision forests that increase prediction accuracy while reducing prediction time and outcome prediction methods using structural and functional connectomics. Building off these capabilities, we plan to develop novel modeling and outcome prediction tools for mental & neurodevelopmental disorders, and neurodegenerative diseases.

The lab is co-located within the Bioinformatics Department on UT Southwestern’s south campus and embedded in the Radiology Department on north campus. We are an integral part of the Advanced Imaging Research Center, and work closely with research groups within Neuroscience, Neurology, Psychiatry, Radiation Oncology, and Surgery. Lab members have access to extensive computational resources, including the >6,800-core cluster with >8 Petabyte of storage available through UTSW’s high-performance infrastructure ( BioHPC ). Members have access to multiple research-dedicated scanners (such as 7T and 3T MRI) and the opportunity to work on a range of image analysis, machine learning and modeling projects on interdisciplinary teams, and participate in all aspects of method development and data analysis with collaborators.

UT Southwestern Medical Center is an Affirmative Action/Equal Opportunity Employer. Women, minorities, veterans and individuals with disabilities are encouraged to apply.


Current and Prospective Ph.D. and M.D/Ph.D. Students

Current students (Ph.D. students at UT Southwestern, UTD, UTA, SMU and MSTP MD/Ph.D. students) are welcome to join our team by emailing me at Albert.Montillo@UTSouthwestern.edu to arrange a meeting. The research experience in our lab provides a great opportunity to supplement your background in computer science, engineering, applied math, 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 looking to apply for UTSW graduate school admission must apply by the university’s 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. For M.D./Ph.D. applicants, the MSTP admission deadline is November 1st.


Postdoctoral Researchers

Two postdoctoral positions are available in the Deep Learning for Precision Health lab. Applications are invited for a 2 to 3-year computational postdoctoral research position. The researchers will develop novel deep learning models to predict diagnoses and outcomes from patient data including imaging (fMRI, diffusion MRI, MEG/EEG, PET/SPECT) and corresponding genomic, metabolic and clinical data. Potential projects include theoretical or applied method development. Theoretical projects target the development of 1) improved visualization of network learned abstractions, and 2) streamlined network parameter optimization. Applied projects include advancing the state-of-the-art in methods for: 1) discovering image-based biomarkers, including advanced brain connectivity measures, and differentially expressed metabolic markers and genes for disease diagnosis, and treatment outcome prediction in mental & neurodevelopmental disorders and neurodegenerative diseases. And 2) optimizing non-invasive brain stimulation therapies.

Ideal applicants will have:
- Ph.D. degree in Computer Science, Electrical or Biomedical Engineering, or related field.
- Experience in medical image analysis including familiarity with at least 1 image data type: MRI, PET, CT, MEG/EEG.
- Machine learning experience in one or more of the following: deep learning: neural nets (RNN,CNN,DNN), DCGAN, deep RL, transfer learning, autoencoders; classical or shallow learning methods; probabilistic graphical models; optimization; image recognition, registration & segmentation.
- Strong programming skills including experience with at least 1 ML Python library: Keras, scikit-learn, TensorFlow, PyTorch, Nilearn.
- At least 2 first author papers published and writing skills in English.

To apply, email to Dr. Montillo [Albert.Montillo@UTSouthwestern.edu] and include your CV, names and addresses of three references, statement of research accomplishments and future goals, preferably as one single PDF-document. Use the subject line “PostdocApplicant: ”.

For additional details download Postdoctoral research position (PDF).


Scientific Programmer

The laboratory of Albert Montillo ( http://www.utsouthwestern.edu/labs/montillo ) in the Bioinformatics Department of UT Southwestern Medical Center is seeking a full time Scientific Programmer for studies of mental & neurodevelopmental disorders and neurodegenerative diseases. The Scientific Programmer will use multimodal MRI, and MEG/EEG data to study structural and functional circuit changes, and PET/SPECT, CT to study metabolic and pathophysiological changes associated with diagnosis and prognoses. The main responsibilities of the position include: implementing and optimizing image processing, computational and analyses pipelines for large-scale multimodal brain imaging data and corresponding clinical data. The lab is an interactive and collaborative team directed by Albert Montillo, Ph.D., conducting cutting-edge research to advance the theory and application of machine learning for the analysis of medical images. The lab addresses unmet clinical needs by forming predictive models that make diagnoses and prognoses more precise and advance neuroscience by furthering the understanding of mechanisms in disease and intervention. You will work directly with him and an array of principle investigators, collaborators and trainees.

Ideal applicants will have:

- B.A. or B.S. Degree in Computer Science, Electrical Engineering, Biomedical Engineering or a related field with three (3) years scientific software development; Master’s or Ph.D. preferred. Software development experience on high performance compute clusters or GPU-based machine learning is a strong plus. Will consider record of success in publishing computational results in lieu of experience.
- Familiarity with at least 1 image data type: MRI, PET/SPECT, CT, MEG/EEG & format: NIFTI, DICOM.
- Experience in at least 1 neuroimage analysis pipeline: NiPype, SPM, FSL, AFNI, FreeSurfer; for diffusion MRI: Camino, DTI-TK, DiPy, TrackVis, DTI/DSI studio, ExploreDTI; for MEG/EEG: Brainstorm, EEGLAB, FieldTrip, MNE, NUTMEG.
- At least 2 years of experience in Linux, Python and 1 other language (Matlab, R, C/C++).
- Optional but helpful: Practical experience in machine learning, Git, and C++/cMake software development.


To apply, email to Dr. Montillo [Albert.Montillo@UTSouthwestern.edu] and include your CV and names and addresses of three references, preferably as one single PDF-document. Use the subject line “ScientificProgrammer: ”.

For additional details download Scientific programmer position (PDF).

News


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

Behrouz Saghafi, PhD

Postdoctoral Researcher

Anand Kadumberi, M.S.

Senior Research Associate

Afarin Famili, B.S.

Graduate Research Assistant

Get in touch

Deep Learning for Precision Health lab
Lyda Hill Department of Bioinformatics
UT Southwestern Medical Center
5323 Harry Hines Blvd. E-Building, E4.350
Dallas, TX 75390
Ph: 214-645-1726
Email

Albert.Montillo[at]UTSouthwestern[dot]edu

The Department of Bioinformatics is located in the E building at 5323 Harry Hines Blvd., Dallas, Texas. 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, across the area marked D building, which is underground. At the right side to the E building, take an external stairwell down one level to a garden/koi pond area. Enter through the grey double doors and take the elevator to 4th floor. The Department of Bioinformatics entrance is the glass door at the end of the hallway. Please proceed straight thru to the admin team area and a member of the admin team will 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.