Yousef Al-Kofahi

PhD, Computer Systems Engineering (Rensselaer Polytechnic Institute)

Dr. Al-Kofahi brings 15 years of experience in developing Artificial Intelligence (AI) and computer vision solutions for a variety of healthcare and industrial applications. He received his Ph.D in Computer Systems Engineering from Rensselaer Polytechnic Institute in 2009. During his PhD and industrial work prior to GE, he developed algorithms to analyze 2-D and 3-D biomedical images and integrated them into products used by clinical laboratory professionals and life scientists.
After joining GE Research in 2011, Dr. Al-Kofahi worked on several projects focusing on developing algorithms to analyze images of cancer tissue samples, including algorithms to detect and predict the severity of the cancer. He also led a multi-year project to build next generation AI capabilities for GE Lifesciences’ products, such as deep and active learning algorithms for image segmentation and classification.
Dr. Al-Kofahi has also developed AI-based solutions for inspection and monitoring in Additive Manufacturing applications. Most recently, he has been developing Automatic Defect Recognition and real-time monitoring systems to detect defects in 3-D printed parts while being printed, and to make control decisions that reduce post-process inspection time and cost. 


Cell Dive Analytics, Argus Image Analytics, AM Recoat ADR and monitoring, Platform for tumor heterogeneity analysis (Thrive)


GE Global research is a unique place where you can work with some of the world’s finest minds from different scientific disciplines, solve challenging problems, learn every day, and above all, build technologies that add value to people's lives.

  1. Lin, G., Al Kofahi, Y., Tyrrell, J.A., Bjornsson, C., Shain, W. and Roysam, B., 2007, April. Automated 3-D quantification of brain tissue at the cellular scale from multi-parameter confocal microscopy images. In 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (pp. 1040-1043). IEEE.
  2. Al-Kofahi, Y., Lin, G., Bjornsson, C., Smith, K., Shain, W. and Roysam, B., 2007. Computer-Aided Classification of Cells in Complex Brain Tissue From 5-Channel 3-D Confocal Datasets. Microscopy and Microanalysis, 13(S02), pp.384-385.
  3. Narayanaswamy, A., Ladi, E., Al-Kofahi, Y., Chen, Y., Carothers, C., Robey, E. and Roysam, B., 2010, April. 5-D imaging and parallel automated analysis of cellular events in living immune tissue microenvironments. In 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro (pp. 1435-1438). IEEE.
  4. Ajemba, P., Al-Kofahi, Y., Scott, R., Donovan, M. and Fernandez, G., 2011, March. Integrated segmentation of cellular structures. In Medical Imaging 2011: Image Processing (Vol. 7962, p. 79620I). International Society for Optics and Photonics.
  5. Al-Kofahi, Y., Padfield, D. and Seppo, A., 2013, March. An automated algorithm for cell-level FISH dot counting. In Medical Imaging 2013: Image Processing (Vol. 8669, p. 866903). International Society for Optics and Photonics.
  6. Kumar, V.S., Williams, J.W., Aggour, K.S., Sarachan, B., Al-Kofahi, Y. and Santamaria-Pang, A., 2015. Collaborative Analysis of High-Content Image Data. In NIST BioImage Informatics Conference.
  7. Al-Kofahi, Y., Sevinsky, C., Santamaria-Pang, A., Ginty, F., Sood, A. and Li, Q., 2016, April. Multi-channel algorithm for segmentation of tumor blood vessels using multiplexed image data. In 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) (pp. 213-216). IEEE.
  8. Al-Kofahi, Y. and Fiona, G., 2018, April. Image analytic algorithms for automated cell segmentation quality control. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) (pp. 423-426). IEEE.
  9. Roysam, B., Shain, W., Barnes, C., Mohler, W., Lin, G., Bjornsson, C., Chen, Y., Al-Kofahi, Y. and Narayanaswamy, A., 2008. FARSIGHT: A divide and conquer methodology for analyzing complex and dynamic biological microenvironments.
  10. Henriksen, M., Miller, B., Newmark, J., Al-Kofahi, Y. and Holden, E., 2011. Laser scanning cytometry and its applications: a pioneering technology in the field of quantitative imaging cytometry. In Methods in cell biology (Vol. 102, pp. 159-205). Academic Press.

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