Tolga Tasdizen
Professor of Electrical and Computer Engineering
University of Utah
Associate Chair, Electrical and Computer Engineering
Faculty, Scientific Computing and Imaging Institute
Adjunct Professor, Kahlert School of Computing
Machine learning and image analysis for healthcare, public health, and scientific imaging.
Research Themes
Core areas of investigation spanning machine learning, imaging, and scientific applications.
Limited Supervision
Methods for training deep networks when annotated data is scarce, expensive, or unreliable, including semi-supervised, self-supervised, and weakly supervised learning.
Semi-supervised learningSelf-supervised learningContrastive learning
Healthcare Imaging
Machine learning for radiology, pathology, and cardiology with emphasis on clinically meaningful prediction, localization, and explainability.
Semi-supervised learningSelf-supervised learningWeak supervision
Scientific Imaging
Machine learning and image analysis for microscopy from nuclear-materials to connectomics. Converting dense ultrastructural EM into segmentations and reconstructions for neural circuit analysis.
Image segmentationSelf-supervised learningMorphology analysis
Selected Projects
Contrastive Learning for Histopathological Image Analysis
Representation learning and weak / limited supervision for robust pathology classification and grading.
View project →
Interpretable AI for Radiology
Interpretable deep learning for radiology—combining clinical workflow signals, localization supervision, and expert-aligned explanations so imaging models are accurate and inspectable.
View project →
Computer Vision for the Built Environment
Using street-level imagery and deep learning to quantify built environment features relevant to pedestrian safety and public health outcomes.
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Automated Morphology Analysis for Nuclear Materials
Self-supervised and semi-supervised approaches for segmentation and characterization of microstructures in nuclear fuel materials from electron microscopy.
View project →Selected Publications
CLASS-M: Adaptive stain separation-based contrastive learning with pseudo-labeling for histopathological image classification
HistoEM: A Pathologist-Guided and Explainable Workflow for Histopathological Image Analysis Using Expectation Maximization
Improving uranium oxide pathway discernment and generalizability using contrastive self-supervised learning
Current Students
Jakob Johnson
Ph.D. candidate
Kahlert School of Computing
Elham Ghelichkhan
Ph.D. candidate
Kahlert School of Computing
Hamid Manoochehri
Ph.D. candidate
Electrical and Computer Engineering
Xiaoya Tang
Ph.D. candidate
Kahlert School of Computing
Xiwen Li
Ph.D. candidate
Kahlert School of Computing
Sandesh Pokhrel
Ph.D. candidate
Electrical and Computer Engineering
Highlights
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Associate Editor, MIDL 2026
Serving as Associate Editor for the Medical Imaging with Deep Learning (MIDL) 2026 conference.
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Serving as General Chair for the Medical Imaging with Deep Learning (MIDL) 2025 conference.
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CLASS-M Published in Medical Image Analysis
CLASS-M paper on contrastive learning with pseudo-labeling for histopathology accepted and published in Medical Image Analysis.
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Outstanding Member of the IEEE TIP Editorial Board
Recognized for editorial service on IEEE Transactions on Image Processing.