About me

I am currently working as a Research Professor at Ghent University Global Campus, where my research spans several key areas at the intersection of artificial intelligence and medical applications. My current work focuses on medical and biomedical imaging, trustworthy and explainable AI, and self-supervised learning.

My interests lie in developing AI-driven solutions that enhance clinical decision-making, improve interpretability, and ensure reliability in real-world medical and biomedical settings. These days, I'm working on surgical video understanding with video object segmentation models such as SAM2, AI-assisted characterization of sub-visible particles in biopharmaceuticals (classification, clustering, and segmentation), and other topics involving spurious correlations, robust predictions, and interpretable AI. If you're working on cool projects within my areas of interest that I can contribute to, feel free to reach out!

Here’s a brief overview of my career, including education, work history, research focus (past and present), publications, and repositories.

Summary

Current Position: Research Professor at Ghent University Global Campus, South Korea

Highest Education: PhD in Computer Science, Ghent University, Belgium

Primary Research Expertise: AI, Machine Learning, Computer Vision, Medical and Biomedical Imaging

Research Output: 13 First-author and 5 Last-author Papers in Top-tier Venues

Repositories: 9,440 GitHub Stars and 1,684 Forks

Work Experience

2023 - Current: Research Professor, Ghent University Global Campus, South Korea

2022 - 2023: Postdoctoral Fellow, Ghent University Global Campus, South Korea

2017 - 2022: AI Researcher, Ghent University Global Campus, South Korea

2015 - 2016: SAP Business Intelligence Consultant, The Coca Cola Company, Turkey

2014 - 2015: SAP Business Intelligence Consultant, Turkish Airlines, Turkey

Education

2017 - 2022: PhD in Computer Science, Ghent University, Belgium

2016 - 2017: MSc in Data Science, University of Southampton, United Kingdom

2012 - 2014: BSc in Computer Engineering, Yasar University, Turkey

Research Expertise

(Med) Medical and biomedical imaging

(XAI) Trustworthy and explainable AI

(SecureAI) AI security and safety

(Bio) Bioinformatics and genomics

(SSL) Image-based self-supervised learning

Publications

Last / Corresponding Author Publications

(Med) SpurBreast: A Curated Dataset for Investigating Spurious Correlations in Real-world Breast MRI Classification
2025, MICCAI - Main Track - Early Accept

(Med) Color Flow Imaging Microscopy Improves Identification of Stress Sources of Protein Aggregates in Biopharmaceuticals
2024, MICCAI - MOVI Workshop

(Med) Exploring Patient Data Requirements in Training Effective AI Models for MRI-based Breast Cancer Classification
2024, MICCAI - Deep Breath Workshop

(Med-XAI-SSL) Evaluating Visual Explanations of Attention Maps for Transformer-Based Medical Imaging
2024, MICCAI - iMIMIC Workshop

(Med-XAI-SSL) Identifying Critical Tokens for Accurate Predictions in Transformer-based Medical Imaging Models
2024, MICCAI - MLMI Workshop

First Author Journal Publications

(Bio) Assessing the Reliability of Point Mutation as Data Augmentation for Deep Learning with Genomic Data
2024, BMC Bioinformatics, Q1 SCIE

(Bio-XAI) Mutate and Observe: Utilizing Deep Neural Networks to Investigate the Impact of Mutations on Translation Initiation
2023, Bioinformatics, Oxford Press, Q1 SCIE

(SSL) Know Your Self-supervised Learning: A Survey on Image-based Generative and Discriminative Training
2023, Transactions on Machine Learning Research, SCOPUS

(SecureAI) Perturbation Analysis of Gradient-based Adversarial Attacks
2020, Pattern Recognition Letters, Elsevier, Q1 SCIE

(SecureAI-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, Q1 SCIE

First Author Conference Proceedings

(SSL) Self-Supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?
2024, IEEE IJCNN, oral presentation

(Bio-XAI) Utilizing Mutations to Evaluate Interpretability of Neural Networks on Genomic Data
2023, NeurIPS - LMRL Workshop

(SecureAI-XAI) Evaluating Adversarial Attacks on ImageNet: A Reality Check on Misclassification Classes
2022, NeurIPS - Workshop on ImageNet: Past, Present, and Future

(SecureAI) Selection of Source Images Heavily Influences the Effectiveness of Adversarial Attacks
2021, BMVC, oral presentation

(SecureAI) Regional Image Perturbation Reduces Lp Norms of Adversarial Examples While Maintaining Model-to-model Transferability
2020, ICML - UDL Workshop

(SecureAI-Med) Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation
2019, MICCAI, poster presentation

(SecureAI) Not All Adversarial Examples Require a Complex Defense: Identifying Over-optimized Adversarial Examples with IQR-based Logit Thresholding
2019, IEEE IJCNN, oral presentation

(SecureAI) How the Softmax Output is Misleading for Evaluating the Strength of Adversarial Examples
2018, NeurIPS - SecML Workshop

First Author Preprints and Other Publications

(Med) One Patient's Annotation is Another One's Initialization: Towards Zero-Shot Surgical Video Segmentation with Cross-Patient Initialization
2025, Arxiv

(Med) Less is More? Revisiting the Importance of Frame Rate in Real-Time Zero-Shot Surgical Video Segmentation
2025, Arxiv

(Bio) BRCA Gene Mutations in dbSNP: A Visual Exploration of Genetic Variants
2023, Arxiv

(SecureAI-XAI) Exact Feature Collisions in Neural Networks
2022, Arxiv

(SecureAI) Prevalence of Adversarial Examples in Neural Networks: Attacks, Defenses, and Opportunities
2022, Ghent University, PhD thesis

Repositories

(XAI) pytorch-cnn-visualizations

(Other) pytorch-custom-dataset-examples

(SecureAI) pytorch-cnn-adversarial-attacks

(SecureAI) adaptive-segmentation-mask-attack

(Other) pytorch-simple-diffusion

(SecureAI) imagenet-adversarial-image-evaluation

(Bio) mutate-and-observe

(SecureAI) regional-adversarial-perturbation