Nurbek Tastan

PhD Candidate in Machine Learning, MBZUAI.

I am Nurbek Tastan, a PhD candidate in Machine Learning at MBZUAI, affiliated with the SPriNT-AI lab. I conduct my research under the guidance of Dr. Karthik Nandakumar and Dr. Samuel Horvath.

My research focuses on trustworthy and efficient machine learning, with particular emphasis on fairness, privacy, robustness, and efficiency in collaborative settings. I also develop methods to make large-scale models, including large language models, more efficiently fine-tunable while also preserving utility and user confidentiality.

Passionate about building machine learning systems that are not only performant, but also equitable, privacy-aware, and computationally viable for real-world deployment.

selected publications

  1. A Framework for Double-Blind Federated Adaptation of Foundation Models
    Nurbek Tastan, and Karthik Nandakumar
    In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), ICLR 2025 Workshop on Modularity for Collaborative, Decentralized, and Continual Deep Learning (MCDC) , 2025
  2. CYCle: Choosing Your Collaborators Wisely to Enhance Collaborative Fairness in Decentralized Learning
    Nurbek Tastan, Samuel Horváth, and Karthik Nandakumar
    Transactions on Machine Learning Research, 2025
  3. Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks
    Nurbek Tastan, Samuel Horváth, and Karthik Nandakumar
    In Proceedings of the Forty-second International Conference on Machine Learning (ICML) , 2025