About me
Currently working as a Research Professor at the Korean Campus of Ghent University, where I am engaged in research on two distinct topics: (1) image-based self-supervised learning, particularly with vision transformers, and (2) explainable AI, often in the context of biomedical imaging.
Here is a brief overview of my recent activities:
2023 - Current: Research Professor, Ghent University Global Campus, South Korea
Research focus:
- Trustworthy and explainable AI (XAI)
- Biomedical imaging (B.img)
2022 - 2023: Postdoctoral Fellow, Ghent University Global Campus, South Korea
Research focus:
- Bioinformatics (B.inf)
- Image-based self-supervised learning (SSL)
Publications:
- (SSL) Know Your Self-supervised Learning: A Survey on Image-based Generative and Discriminative Training
2023, Transactions on Machine Learning Research - (B.inf) BRCA Gene Mutations in dbSNP: A Visual Exploration of Genetic Variants
2023, Arxiv - (B.inf-XAI) Mutate and Observe: Utilizing Deep Neural Networks to Investigate the Impact of Mutations on Translation Initiation
2023, Bioinformatics, Oxford Press - (B.inf-XAI) Utilizing Mutations to Evaluate Interpretability of Neural Networks on Genomic Data
2023, NeurIPS - LMRL Workshop
Repositories:
2017 - 2022: PhD in Computer Science, Ghent University, Belgium
Research focus:
- Adversarial examples: attacks, defenses, and properties (Adversarial)
Publications:
- (Adversarial) Prevalence of Adversarial Examples in Neural Networks: Attacks, Defenses, and Opportunities
2022, Ghent University, PhD thesis - (Adversarial-XAI) Evaluating Adversarial Attacks on ImageNet: A Reality Check on Misclassification Classes
2022, NeurIPS - Workshop on ImageNet: Past, Present, and Future - (Adversarial) Selection of Source Images Heavily Influences the Effectiveness of Adversarial Attacks
2021, BMVC, oral presentation - (Adversarial-XAI) Investigating the Significance of Adversarial Attacks and Their Relation to Interpretability for Radar-based Human Activity Recognition Systems
2021, Computer Vision and Image Understanding, Elsevier - (Adversarial) Regional Image Perturbation Reduces Lp Norms of Adversarial Examples While Maintaining Model-to-model Transferability
2020, ICML - UDL Workshop - (Adversarial) Perturbation Analysis of Gradient-based Adversarial Attacks
2020, Pattern Recognition Letters, Elsevier - (Adversarial) Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation
2019, MICCAI, poster presentation - (Adversarial) Not All Adversarial Examples Require a Complex Defense: Identifying Over-optimized Adversarial Examples with IQR-based Logit Thresholding
2019, IJCNN, oral presentation - (Adversarial) How the Softmax Output is Misleading for Evaluating the Strength of Adversarial Examples
2018, NeurIPS - SecML Workshop
Repositories:
- (XAI) github.com/utkuozbulak/pytorch-cnn-visualizations
- (Adversarial) github.com/utkuozbulak/adaptive-segmentation-mask-attack
- (Adversarial) github.com/utkuozbulak/pytorch-cnn-adversarial-attacks
- (Adversarial) github.com/utkuozbulak/imagenet-adversarial-image-evaluation
- (Adversarial) github.com/utkuozbulak/regional-adversarial-perturbation
- (Other) github.com/utkuozbulak/pytorch-custom-datasets
2016 - 2017: MSc. in Data Science, University of Southampton, UK
2014 - 2016: SAP Business Intelligence Consultant, Acron Consulting, Turkey
Notable clients:
- The Coca Cola Company
- Turkish Airlines
- Bosch Siemens Hausgerate
- Flormar Cosmetics
2012 - 2014: BSc. in Computer Engineering, Yasar University, Turkey