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Albert Montillo, Ph.D.

Albert Montillo, Ph.D.

Titles and Appointments

Associate Professor

School
Medical School
Department
Lyda Hill Department of Bioinformatics | Biomedical Engineering
Graduate Programs
Biomedical Engineering, Molecular Biophysics, Neuroscience
  • Biography

    Since September 2024, Albert Montillo has been appointed as Associate Professor in the Lyda Hill Departments of Bioinformatics and Biomedical Engineering. He completed his Assistant Professor tenure at UTSW from 2017 to 2024. Prior to to UTSW, Dr. Montillo lead neuroimage and machine learning research in industry at Microsoft Research in Cambridge, UK; the Martinos Center for Biomedical Imaging at Harvard/MIT in Boston, and General Electric Research in New York.

    The research of the Montillo Laboratory focuses on developing the theory and practical solutions to address the main challenges of AI for healthcare and life science. This research provides clinical AI-based tools that support and inform physicians' diagnoses, prognoses, and treatment decisions. The research also develops computational neuroscience approaches to increase our understanding of (patho)neurophysiology.  Dr. Montillo is actively engaged in mentoring researchers with interests in AI/ML for healthcare, across the departments of UT Southwestern, as well as from UT Dallas, UT Arlington, and Southern Methodist University. 

  • Education
    Undergraduate
    Rensselaer Polytechnic Inst (1992), Computer Sciences
    Graduate School
    Rensselaer Polytechnic Inst (1993), Computer Sciences
    Graduate School
    University of Pennsylvania (2004), Medical Imaging
  • Research Interest
    • Delivering impactful AI-based tools for the clinic that support and inform physicians' diagnoses, prognoses, and treatment decisions, improving healthcare outcomes for neurological disorders and oncology.
    • Developing computational neuroscience approaches to increase our understanding of (patho)neurophysiology, working at the intersection of medical image analysis, biomedical informatics/machine learning, and causal analysis.
    • Developing the theory and practical algorithmic solutions that address the main challenges of AI for healthcare and life science: (1) deep multimodal fusion of neuroimaging (MRI, PET, EEG) and multi-omic (genomic, proteomic) data , (2) explainable AI, and (3) causal analysis --integrating observational and experimental data, with (4) low sample efficiency.
  • Publications

    Star Featured Publications

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    Targeted Metabolomic Analysis in Alzheimer's Disease Plasma and Brain Tissue in Non-Hispanic Whites.
    Kalecký K, German DC, Montillo AA, Bottiglieri T, J Alzheimers Dis 2022 Feb
  • Honors & Awards
    • NIH NIA North Texas Alzheimers Disease Research Center
      (2025)
    • NIH NIGMS Correcting Biases in Deep Learning
      (2023)
    • NIH NIA Developing Digital Biomarkers for Alzheimers Disease
      (2021)
    • NIH NIA Blood Biomarker for Alzheimers Disease
      (2019-2023)
    • NIH NINDS Predicting Parkinsons Disease Progression Rate
      (2019-2023)