Talks

• “Riemannian gradient descent improves parameter-efficient fine-tuning,” EUROPT, Southampton, UK, 06/2025
• “LoRA surgery,” Efficient Machine Learning Reading Group (online), 06/2025
• “Fine-tuning LLMs cost-efficiently,” Peking University, 04/2025
• “LoRA surgery,” EPFL LIONS Lab, 04/2025
• “Architecture-aware optimization for deep learning,” ELLIS UnConference, Bad Teinach, Germany, 12/2024

Reviewer activities

• NeurIPS 2021 - 2025
• ICML 2021 - 2025
• ICLR 2022 - 2025
• AISTATS 2021 - 2023, 2025
• AAAI 2021, 2025
• IEEE GLOBECOM 2019, 2020
• IEEE Signal Processing Magazine
• IEEE Transactions on Pattern Analysis and Machine Intelligence
• IEEE Transactions on Neural Networks and Learning Systems
• IEEE Transactions on Automatic Control
• IEEE Transactions on Signal Processing
• IEEE Journal on Selected Topics in Signal Processing
• IEEE Journal on Selected Areas in Communications
• IEEE Transaction on Communications
• IEEE Transactions on Cloud Computing
• IEEE Transactions on Mobile Computing
• IEEE Transactions on Vehicular Technology
• IEEE Signal Processing Letters
• Journal of Machine Learning Research (JMLR)
• Transactions of Machine Learning Research (TMLR)
• Data-centric Machine Learning Research (DMLR)
• Machine Learning
• Signal Processing
• Journal of Optimization Theory and Applications
• Optimization Methods and Software
• Computers & Security

Student projects supervision

We do have projects for ETH master and bachelor students. The lastest ones can be found in ODI student projects. If you are interested in optimization and the theoretical foundations of deep learning, feel free to get in touch. Below are past projects I (co-)supervised that are published or under submission at conferences and workshops:

Bachelor projects

• Memory efficient LLM fine-tuning with [ConMeZO]

Master’s projects

• In preparation for some conferences. Details to be released soon.

PhD projects

• Improved optimization for LLM fine-tuning with [RefLoRA]
• LLM fine-tuning with [PoLAR]

Teaching experiences

• TA for FoRL (Foundations of Reinforcement Learning), ETHZ, Spring 2024
• TA for EE 3025 Statistical Methods in ECE, UMN, Spring 2021