Since December 2017 Albert Montillo has been an appointed as Assistant Professor in the Lyda Hill Department of Bioinformatics. He holds secondary appointments in the Department of Radiology, the Advanced Imaging Research Center, and Biomedical Engineering within the Graduate School of Biomedical Sciences.  He also directs the research of the Deep Learning for Precision Health lab.  Before establishing his lab at UTSW, Montillo lead neuroimage and machine learning research in industry at Microsoft Research in Cambridge, UK; the Harvard/MIT Martinos Center for Biomedical Imaging in Boston, and GE Research in New York.

Dr. Montillo advances the theory and application of deep learning using datasets of multi-modal brain images and recordings. He develops de novo machine learning algorithms that can analyze these with better precision. These algorithms integrate brain images with diverse patient data and perform image interpretation to inform accurate diagnoses and prognoses. These precision analyses will provide valuable information for optimization of non-invasive treatments and deepen insight into understanding brain activity in neurological disorders.

Dr. Montillo is actively engaged in supervising, teaching and training of students spanning UT Southwestern, Southern Methodist Univeristy (Statistics and Computer Science ),  UT Dallas (ElectricalBioengineering, and Computer Science) and UT Arlington. He teaches Machine Learning, Biostatistics and the Python Bootcamp at UTSW and at UT Dallas through an adjunct appointment there and serves on Ph.D. thesis committees at these universities.


Rensselaer Polytechnic Inst (1992), Computer Sciences
Graduate School
Rensselaer Polytechnic Inst (1993), Computer Sciences
Graduate School
University of Pennsylvania (2004), Medical Imaging

Research Interest

  • 1. Developing statistical, machine learning approaches that identify significant associations between functional and structural connectomics measures (from fMRI, MEG/EEG and diffusion MRI, ECoG, DBS) and disease severity and treatment outcomes in Autism Spectrum Disorder, neuropsychiatric disorders, movement disorders and Alzheimer?s.
  • 2. Uncovering associations between radiological imaging signatures and gene expression and optimizing their combined use in prognostics. Identifying neurobiological subtypes that have preferential treatment response using machine learning dimensionality reduction methods.
  • 3. Using preclinical disease models with collaborators to validate machine learning-biomarker discovered from above research interests.
  • 4. Developing multi-parametric MRI for improved cancer diagnostics and prognostics.
  • 5. Translating research results into practical tools that improve healthcare in the clinic.


Featured Publications LegendFeatured Publications

Prediction of Individual Progression Rate in Parkinson's Disease Using Clinical Measures and Biomechanical Measures of Gait and Postural Stability.
Raval V, Nguyen KP, Gerald A, Dewey RB, Montillo A, Proc IEEE Int Conf Acoust Speech Signal Process 2020 May 2020 1319-1323


Featured Books Legend Featured Books

Medical Computer Vision: Algorithms for Big Data, Fourth International MICCAI Workshop

Bjoern H. Menze, Georg Langs, Albert Montillo, Michael Kelm, Henning Muller, Shaoting Zhang, Weidong Cai, Dimitri Metaxas (Ed.) (2015). Springer

Medical Computer Vision: Large Data in Medical Imaging, Third International MICCAI Workshop

Bjoern Menze, Georg Langs, Albert Montillo, Henning Muller, Zhuowen Tu (2014). Springer

Medical Computer Vision: Recognition Techniques and Applications in Medical Imaging, Second International MICCAI Workshop

Bjoern Menze, Georg Langs, Le Lu, Albert Montillo, Zhuowen Tu, Antonio Criminisi (Ed.) (2013). Springer

Professional Associations/Affiliations

  • American Society of Neuroradiology (ASNR) (2011)
  • IEEE (senior member) (2002)
  • Medical Image Computing and Computer assisted intervention (MICCAI) (2002)
  • Organization for Human Brain Mapping (OHBM) (2012)