Utku Ozbulak
Research Professor at Ghent University Global Campus
Incheon, South Korea
Summary
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Rockstar🎓
Top SchoolUtku Ozbulak is a Research Professor at Ghent University Global Campus and an Adjunct Professor at George Mason University Korea with nine years of experience at the intersection of medical/biomedical AI and data visualization. He develops AI-driven solutions to improve clinical decision-making, focusing on interpretability, reliability, and real-world deployment in areas like surgical video understanding, flow-imaging microscopy, and sub-visible particle analysis. His work combines core ML research—illustrated by a widely used PyTorch repo for CNN visualizations—with practical model auditing to detect spurious correlations and enhance trustworthiness. Comfortable in both academic and industry settings, he has a background in business intelligence from earlier roles at Coca-Cola and Turkish Airlines that informs his data-driven, impact-oriented approach. He holds a PhD in Computer Science and Engineering from Ghent University and an MSc in Data Science from Southampton, and he actively shares tools and insights via an open GitHub portfolio and personal website.
9 years of coding experience
5 years of employment as a software developer
MSc Data Science, MSc Data Science at University of SouthamptonPhD Computer Science and Engineering, PhD Computer Science and Engineering at Ghent UniversityBSc Computer Engineering, BSc Computer Engineering at Yaşar University
English, Turkish, German, Korean
Github Skills (6)
mask-rcnn10
pytorch10
faster-rcnn10
python9
deep-learning9
computer-vision9
Programming languages (1)
Github contributions (5)
utkuozbulak/pytorch-cnn-visualizations
Oct 2017 - Mar 2022
Pytorch implementation of convolutional neural network visualization techniques
Role in this project:
ML Engineer
ML EngineerContributions:147 commits, 12 PRs, 125 pushes in 4 years 5 months
Contributions summary:Utku primarily contributed to the development of convolutional neural network visualization techniques, as indicated by the code implementing Grad-CAM, Guided Backpropagation, DeepDream, and other related methods. Their work focused on the practical application of PyTorch for visualizing CNNs, incorporating image preprocessing, forward/backward passes, and the generation of visual outputs. The commits show a strong focus on understanding and implementing techniques for interpreting and debugging CNN models.
camguided-grad-camgrad-camconvolutional-neural-networksegmentation
utkuozbulak/unsupervised-learning-document-clustering
Mar 2017 - Mar 2018
Contributions:6 commits, 5 pushes, 1 branch in 11 months
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