AI/ML Resident at Apple · Machine Learning Researcher
Cupertino, California, United States
I am Nurbek Tastan, an AI/ML Resident at Apple, where I work as a machine learning researcher. I joined Apple in September 2026 after completing my PhD in Machine Learning at MBZUAI. I conducted my doctoral research with the SPriNT-AI lab, advised by Dr. Karthik Nandakumar and Dr. Samuel Horvath.
My research focuses on trustworthy and efficient machine learning, especially privacy, robustness, incentivization, and efficiency in collaborative settings. More recently, I have been exploring agentic AI, large language models (LLMs), and vision-language models (VLMs), with an emphasis on building systems that are practical, reliable, and privacy-aware in real-world deployment.
@inproceedings{tastan2026mose,title={MoSE: Mixture of Slimmable Experts for Efficient and Adaptive Language Models},author={Tastan, Nurbek and Laskaridis, Stefanos and Nandakumar, Karthik and Horvath, Samuel},booktitle={Proceedings of the Forty-third International Conference on Machine Learning (ICML)},year={2026},url={https://openreview.net/forum?id=18C6xMcD96},}
@inproceedings{tastan2026stochastic,title={Stochastic Self-Organization in Multi-Agent Systems},author={Tastan, Nurbek and Horv{\'a}th, Samuel and Nandakumar, Karthik},booktitle={The Fourteenth International Conference on Learning Representations},year={2026},url={https://openreview.net/forum?id=rS3Jb9AAej},}
@inproceedings{tastan2026loft,title={{Lo{FT}: Low-Rank Adaptation That Behaves Like Full Fine-Tuning}},author={Tastan, Nurbek and Laskaridis, Stefanos and Tak{\'a}{\v{c}}, Martin and Nandakumar, Karthik and Horv{\'a}th, Samuel},booktitle={The Fourteenth International Conference on Learning Representations},year={2026},url={https://openreview.net/forum?id=86P3sb1dpr},}
@inproceedings{fares2026molm,title={{MOLM}: Mixture of Lo{RA} Markers},author={Fares, Samar and Tastan, Nurbek and Hussein, Noor Hazim and Nandakumar, Karthik},booktitle={The Fourteenth International Conference on Learning Representations},year={2026},url={https://openreview.net/forum?id=1fYQOZovHR},}
@article{tastan2025cycle,title={{{CYC}le: Choosing Your Collaborators Wisely to Enhance Collaborative Fairness in Decentralized Learning}},author={Tastan, Nurbek and Horv{\'a}th, Samuel and Nandakumar, Karthik},journal={Transactions on Machine Learning Research},issn={2835-8856},year={2025},url={https://openreview.net/forum?id=ygqNiLQqfH},note={},}
@inproceedings{tastan2025aequa,title={{Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks}},author={Tastan, Nurbek and Horv\'{a}th, Samuel and Nandakumar, Karthik},year={2025},url={https://openreview.net/forum?id=Tw81RElDpe},booktitle={Proceedings of the Forty-second International Conference on Machine Learning (ICML)},series={Proceedings of Machine Learning Research},}
to attend ICML 2026! Let’s catch up if you are there. 
