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Luca Giancardo, PhD

Assistant Professor

Luca Giancardo is an Assistant Professor at the Center for Precision Health, School of Biomedical Informatics (SBMI), University of Texas Health Science Center at Houston (UTHealth) with co-appointments at the McGovern Medical School and the Institute for Stroke and Cerebrovascular Diseases, UTHealth.

He is a computer scientist with extensive experience in image analysis and machine learning. I have worked on developing new machine learning-based methodologies to discover computational biomarkers from patterns in biomedical data such as optical images, magnetic resonance imaging, X-rays, computer tomography, laboratory animal videos or tracking devices. His work has been applied to a number of biomedical applications, such as stroke diagnosis, diabetic retinopathy screening or neurodegenerative disease tracking and successfully translated to industry with two startups based on his methods. One, Hubble Telemedical, was acquired by Welch Allyn in 2015, another, nQ-Medical has raised multimillion dollars in investment. He has authored/co-authored more than 60 peer-reviewed articles (H-index 20) which were featured by news outlet such as MIT Technology Review, Smithsonian magazine and others. He has received multiple awards, including the prestigious 100k Singapore Challenge (judging panel composed by Nobel Prize and Millennium Technology Prize winners).

He was awarded competitive grants from NIH (R01), Translational Research Institute for Space Health, Michael J Fox Foundation and private companies.

Phone: 713-500-3609
Fax: 713-500-3929

Staff Support

Leticia Flores
Phone: 713-500-3912


  • PhD, Computational Image Analysis, 2011, Oak Ridge National Laboratory and Universit√© de Bourgogne (France)
  • MSc, Computer Vision and Robotics, 2008, Heriot-Watt University, Edinburgh (UK), Universitat de Girona (Spain) and Universit√© de Bourgogne (France)
  • BSc (Hons), Software Engineering, 2005, Southampton Solent University (UK)

Areas of Expertise

  • Medical Image/Signal Processing
  • Machine Learning
  • Big Data
  • Translational Medicine