Tolga Tasdizen, PhD
Professor, Electrical and Computer Engineering
Professor, Scientific Computing and Imaging Institute
Adjunct Professor, Computer Science
University of Utah
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Office: WEB 4893 directions
Email: tolga@sci.utah.edu
Research Trainees Co-Mentored By Dr. Tolga Tasdizen and Dr. Beatrice Knudsen
Man M. Ho, PhD
Dr. Ho's interests lie in computer vision, deep learning, pathology image analysis, and photography. His dissertation focused on image and video restoration and compression to address real-world data degradation problems. Dr. Ho's is conducting post-doctoral research to restore pathology images. He trains generative machine learning models to find missing information in frozen section slides to guide cancer surgeries in real-time. Dr. Ho's research is motivated by the opportunity to assist pathologists and provide better patient care. He enjoys taking, editing, and retouching photographs.
Bodong Zhang, MS
Bodong received his B.S. from the University of Science and Technology of China in Electronic Information Engineering and his M.S. from the University of Utah in Electrical and Computer Engineering. He is currently working as a PhD student, focusing on deep learning-based histopathological image analysis, including semi-supervised classification of histopathological images.
Hamid Manoochehri, MS
Hamid has a bachelor’s degree and a master's degree in Electrical Engineering. Hamid is currently a Ph.D. student in the Electrical and Computer Engineering Department at the University of Utah. He is researching medical image analysis projects to predict the risk of metastasis in prostate cancer patients, utilizing advanced deep-learning techniques. Hamid hopes to contribute to some of the most pressing challenges in healthcare and improve the treatment and management of cancer patients. In his leisure time, he enjoys photography, reading, and listening to music.
Alumni
Elham Ghelichkhan, MS
Elham is a PhD student in Computer Science. The overall goal of her work is to solve important healthcare problems using deep learning machine learning models. In current research, she trains models to estimate the extent of chromosomal instability in prostate cancer using images from H&E-stained tissue sections. Elham's work also encompasses other computational pathology tasks such as gland and nuclei segmentation, cancer detection, and cancer grading. The algorithms she develops have the potential to provide a cost-effective platform for development of clinical biomarkers that help with the treatment of cancer patients.
Trainees Co-Mentored By Dr. Shireen Elhabian and Dr. Beatrice Knudsen
Shikha Dubey
Dr. Dubey is a Postdoctoral Research Associate researching medical histopathology images, using advanced machine learning/deep learning and statistical methods. She earned her Ph.D. in Electrical Engineering and Computer Science at GIST (Gwangju Institute of Science and Technology) in South Korea. Her dissertation focused on exploring inductive biases in deep neural networks for visual perception, aiming to understand the contextual relationships between objects and their actions in images/videos, incorporating visual, temporal, and language-based models. Dr. Dubey finds fulfillment in working in the research field of digital pathology, addressing challenging problems in medical image analysis. In her free time, she enjoys playing table tennis, chess, and card games.
Tushar Kataria
Tushar Kataria is a PhD candidate at the University of Utah, School of Computing. He works under the technical guidance of Prof. Shireen Y. Elhabian at the Scientific Computing and Imaging Institute to solve clinical questions using pathology images. His research interests include medical image processing, deep learning, and computer vision. Tushar researches semantic segmentation, instance segmentation, and domain adaptation problems for computational histopathology imaging datasets. Tushar is currently working on automated annotation using immunohistochemical images and virtual staining. In his free time, Tushar enjoys swimming, board games, and reading fiction and fantasy.