Jeremy Juybari
Jeremy Juybari completed his Ph.D. in Electrical and Computer Engineering at 91±¬ÁÏ, where his research focused on computer vision, image segmentation, medical images, and multiscale analysis. His work uses deep learning and fractal-based methods to improve the accuracy, efficiency, and interpretability of computational models, particularly in settings where multiscale structure is important. He has authored and co-authored peer-reviewed publications in venues including Computer Vision and Pattern Recognition, Scientific Reports, and Clinical Cancer Research, with research spanning histopathologic image segmentation, superpixel generation, multifractal analysis, and biomedical image analysis.
A central focus of Jeremy’s doctoral research has been the development of context-aware deep learning methods for image analysis. His work on histopathologic cancer segmentation introduced models that combine local image detail with broader tissue context. This work was featured by the National Cancer Institute.Â
In addition to his research, Jeremy has developed and taught courses in Python programming and has mentored undergraduate and graduate students in computational and data-driven methods. He has also led translational and entrepreneurial efforts through competitive research funding, industry collaboration, and startup development. Across these experiences, Jeremy has focused on bridging methodological innovation with real-world impact and is especially interested in developing computer vision systems that can be applied in high-stakes scientific, medical, and industrial settings.
LinkedIn Profile:
