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’s a brief overview of my career, as well as current and past research focus:

Current research focus:

  • Trustworthy and explainable AI (XAI)
  • Image-based self-supervised learning (SSL)
  • Biomedical imaging (B.img)

Past research expertise:

  • Bioinformatics (B.inf)
  • Adversarial examples: attacks, defenses, and properties (Adversarial)

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


  • (SSL) Self-Supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?
    2024, IEEE IJCNN
  • (B.inf) Assessing the Reliability of Point Mutation as Data Augmentation for Deep Learning with Genomic Data
    2024, BMC Bioinformatics

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


  • (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


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


  • (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, IEEE IJCNN, oral presentation
  • (Adversarial) How the Softmax Output is Misleading for Evaluating the Strength of Adversarial Examples
    2018, NeurIPS - SecML Workshop


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