CV

Education

• Ph.D. in Computer Science
  Warsaw University of Technology • Oct 2021 – Present

• M.Sc. in Computer Science
  Warsaw University of Technology • Feb 2018 – Jun 2021
      Specialization: Information and Decision Systems
      Erasmus Exchange – University of Vienna, Mar 2020 – Jun 2020

• M.Sc. in Power Engineering
  Warsaw University of Technology • Feb 2018 – Sep 2020
      Erasmus Exchange – Polytechnic University of Catalonia, Sep 2018 – Jan 2019

• B.Sc. in Quantitative Methods in Economics and Information Systems
  Warsaw School of Economics • Oct 2016 – Feb 2020

• B.Sc. in Power Engineering
  Warsaw University of Technology • Oct 2014 – Feb 2018

Work experience

• Senior Specialist, NASK National Research Institute • May 2025 – Present
  Department of Security and Transparency of Artificial Intelligence

• Visiting Researcher, CISPA – Helmholtz Center for Information Security • Mar 2024 – May 2024
  Identifying use of copyrighted data in Diffusion Models

• PhD Student Researcher, IDEAS NCBR • Oct 2023 – May 2025
  Research in Systems Security and Data Privacy

• Collaborator, CERN • Feb 2019 – Present
  Fast simulations for LHC calorimeter using GANs and VAEs

• Junior Machine Learning Engineer, Samsung R&D Institute Poland • Jul 2019 – Oct 2021
  Development of ML systems for automatic code repair and audio quality assessment

• IAESTE Internship, National Technical University in Bahia Blanca, Argentina • Aug 2017 – Sep 2017
  Development of grid power failure simulations

Skills

• Strong background in deep learning, with a focus on generative models and AI safety
• Practical research experience across academia and international institutions
• Advanced programming proficiency in Python 3
• In-depth knowledge of:
  • Machine Learning: PyTorch, TensorFlow, Keras
  • Data Science: Pandas, NumPy
  • Data Visualization: Matplotlib, Seaborn
• Practical knowledge of:
  • Version control: Git
  • Operating systems: Linux
  • Containerization: Docker

Selected Publications

• Privacy Attacks on Image Autoregressive Models
  A. Kowalczuk*, J. Dubiński*, F. Boenisch, A. Dziedzic
  *International Conference on Machine Learning (ICML), 2025, CORE A***

• CDI: Copyrighted Data Identification in Diffusion Models
  J. Dubiński*, A. Kowalczuk*, F. Boenisch, A. Dziedzic
  *Conference on Computer Vision and Pattern Recognition (CVPR), 2025, CORE A***

• Learning Graph Representation of Agent Diffuser
  Y. Djenouri, N. Belmecheri, T. Michalak, J. Dubiński, A. N. Belbachir, A. Yazidi
  *International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2025, CORE A***

• Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders
  J. Dubiński*, S. Pawlak*, F. Boenisch*, T. Trzcinski, A. Dziedzic
  *Neural Information Processing Systems (NeurIPS), 2023, CORE A***

• Towards More Realistic Membership Inference Attacks on Large Diffusion Models
  J. Dubiński, A. Kowalczuk, S. Pawlak, P. Rokita, T. Trzciński, P. Morawiecki
  Winter Conference on Computer Vision (WACV), 2024, CORE A

• Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks
  M. Podhajski, J. Dubiński, F. Boenisch, A. Dziedzic, A. Pregowska, T. Michalak
  European Conference on Artificial Intelligence (ECAI), 2024, CORE A

• Selectively Increasing the Diversity of GAN-generated Samples
  J. Dubiński, K. Deja, S. Wenzel, P. Rokita, T. Trzciński
  International Conference on Neural Information Processing (ICONIP), 2022, CORE A

• Progressive Latent Replay for Efficient Generative Rehearsal
  S. Pawlak, F. Szatkowski, M. Bortkiewicz, J. Dubiński, T. Trzciński
  International Conference on Neural Information Processing (ICONIP), 2022, CORE A

• Maybe I Should Not Answer That, but… Do LLMs Understand The Safety of Their Inputs?
  M. Chrabąszcz, F. Szatkowski, B. Wójcik, J. Dubiński, T. Trzciński
  ICLR Workshop on Building Trust in Language Models and Applications, 2025