Berkant Turan

I am working on improving the interpretability of AI, focusing on the Merlin-Arthur Classifier. This intriguing model uses two feature selectors to generate meaningful saliency maps. My objectives are to fine-tune its architecture, explore its adversarial components, and evaluate its effectiveness with complex datasets.

📬 Contact

office
Room 3024 at ZIB
e-mail
homepage
b-turan.github.io
languages
German, English, and Turkish

🎓 Curriculum vitae

since 2023
Member of BMS
since 2022
Researcher at ZIB
2022 to 2022
Research Assistant at ZIB
May 2022
M.Sc. in Scientific Computing at TUB
Oct 2018
B.Sc. in Engineering Science at TUB

đź“ť Publications and preprints

Preprints

  1. GĹ‚uch, G., Turan, B., Nagarajan, S. G., and Pokutta, S. (2024). The Good, the Bad and the Ugly: Watermarks, Transferable Attacks and Adversarial Defenses. [arXiv]
    [BibTeX]
    @misc{2024_GrzegorzTuranNagarajanPokutta_Watermarksadversarialdefenses,
      archiveprefix = {arXiv},
      eprint = {2410.08864},
      primaryclass = {cs.LG},
      year = {2024},
      author = {GĹ‚uch, Grzegorz and Turan, Berkant and Nagarajan, Sai Ganesh and Pokutta, Sebastian},
      title = {The Good, the Bad and the Ugly: Watermarks, Transferable Attacks and Adversarial Defenses}
    }

Conference proceedings

  1. Wäldchen, S., Sharma, K., Turan, B., Zimmer, M., and Pokutta, S. (2024). Interpretability Guarantees with Merlin-Arthur Classifiers. Proceedings of the International Conference on Artificial Intelligence and Statistics. [arXiv]
    [BibTeX]
    @inproceedings{2022_WaeldchenEtAl_Interpretabilityguarantees,
      year = {2024},
      booktitle = {Proceedings of the International Conference on Artificial Intelligence and Statistics},
      archiveprefix = {arXiv},
      eprint = {2206.00759},
      primaryclass = {cs.LG},
      author = {Wäldchen, Stephan and Sharma, Kartikey and Turan, Berkant and Zimmer, Max and Pokutta, Sebastian},
      title = {Interpretability Guarantees with Merlin-Arthur Classifiers}
    }

🔬 Projects

AI-Based High-Resolution Forest Monitoring

Preserving global vegetation is crucial for addressing and mitigating climate change. Accurate, up-to-date forest health data is essential. AI4Forest aims to develop advanced AI methods to monitor forests using satellite imagery, including radar and optical data. The project will create scalable techniques for detailed, high-resolution maps of the globe, e.g., to monitor canopy height, biomass, and to track forest disturbances.

AI4Forest
Jun 2023 to May 2027
3
3

Expanding Merlin-Arthur Classifiers: Interpretable Neural Networks Through Interactive Proof Systems

Existing approaches for interpreting Neural Network classifiers that highlight features relevant for a decision are based solely on heuristics. We introduce a theory that allows us to bound the quality of the features without assumptions on the classifier model by relating classification to Interactive Proof Systems.

MATH+ EF1-24
Apr 2023 to Mar 2026
4
3

đź’¬ Talks and posters

Poster presentations

Jul 2024
Unified Taxonomy in AI Safety: Watermarks, Adversarial Defenses, and Transferable Attacks
Workshop on Theoretical Foundations of Foundation Models (TF2M) @ ICML 2024, Vienna
May 2024
Interpretability Guarantees with Merlin-Arthur Classifiers
27th AISTATS Conference, València
Jul 2023
Extending Merlin-Arthur Classifiers for Improved Interpretability
The 1st World Conference on eXplainable Artificial Intelligence

đź“… Event Attendance

May 2025
7th DOxML Conference, Kyoto

👨‍🏫 Teaching

winter 2019
Tutor for Statics and Elementary Strength of Materials at TUB
summer 2018
Tutor for Energy-Based Methods in Mechanics at TUB
summer 2018
Tutor for Continuum Mechanics at TUB
winter 2018
Tutor for Kinematics and Dynamics at TUB
summer 2017
Tutor for Statics and Elementary Strength of Materials at TUB
winter 2017
Tutor for Energy-Based Methods in Mechanics at TUB
winter 2017
Tutor for Continuum Mechanics at TUB
summer 2016
Tutor for Kinematics and Dynamics at TUB
summer 2015
Tutor for Statics and Elementary Strength of Materials at TUB