Rate My Professor Bulat Ibragimov

Bulat Ibragimov is an Associate Professor in the Department of Computer Science at the University of Copenhagen, specializing in Image Analysis, Computational Modelling and Geometry. He holds the position under the Promotion Programme and leads research in machine learning and medical imaging. Additionally, he serves as Lead Research Scientist at Innopolis University. Ibragimov earned his Ph.D. in Electrical Engineering from the University of Ljubljana in 2014. His professional career includes a Postdoctoral Fellowship at Stanford University from 2016 to 2018, followed by a role as Senior Research Scientist at Auris Health, part of Johnson & Johnson, from 2018 to 2019.

Ibragimov's research focuses on artificial intelligence applications in medical imaging and healthcare, including AI-assisted cancer treatment planning, prediction of treatment side effects through his ERC Consolidator Grant awarded in January 2025, pancreatic cancer treatment planning, image-guided therapy for inflammatory bowel disease, eye tracking analysis of radiologists, and AI-driven dental treatment planning. He has produced a substantial body of work with high impact, amassing over 5,400 citations on Google Scholar. Key publications include "Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks" (Medical Physics, 2017, 630 citations), "A benchmark for comparison of dental radiography analysis algorithms" (Medical Image Analysis, 2016, 505 citations), "Fully automated quantitative cephalometry using convolutional neural networks" (Journal of Medical Imaging, 2017, 357 citations), "Deep neural network ensemble for pneumonia localization from a large-scale chest x-ray database" (Computers & Electrical Engineering, 2019, 297 citations), "Evaluation and comparison of anatomical landmark detection methods for cephalometric x-ray images: a grand challenge" (IEEE Transactions on Medical Imaging, 2015, 271 citations), "Auto-segmentation of organs at risk for head and neck radiotherapy planning: from atlas-based to deep learning methods" (Medical Physics, 2020, 182 citations), "A multi-center milestone study of clinical vertebral CT segmentation" (Computerized Medical Imaging and Graphics, 2016, 166 citations), "Prostate cancer classification with multiparametric MRI transfer learning model" (Medical Physics, 2019, 164 citations), and "Development of deep neural network for individualized hepatobiliary toxicity prediction after liver SBRT" (Medical Physics, 2018, 148 citations). His contributions advance automated segmentation, diagnostic tools, and treatment optimization in radiation oncology and beyond.