Sina Sajadmanesh
Profile
Sina earned his Ph.D. from EPFL, focusing on privacy-preserving machine learning on graphs. During his Ph.D., Sina interned at Brave Software and visited the Safe and Ethical AI program at The Alan Turing Institute. His research interests include trustworthy and secure machine learning, federated learning, reinforcement learning, and learning on graphs.
Publications
Argus: A Compact and Versatile Foundation Model for Vision
CVPR, 2025 | Weiming Zhuang, Chen Chen, Zhizhong Li, Sina Sajadmanesh, Jingtao Li, Jiabo Huang, Vikash Sehwag, Vivek Sharma, Hirotaka Shinozaki, Felan Carlo Garcia, Yihao Zhan, Naohiro Adachi, Ryoji Eki, Michael Spranger, Peter Stone, Lingjuan Lyu
While existing vision and multi-modal foundation models can handle multiple computer vision tasks, they often suffer from significant limitations, including huge demand for data and computational resources during training and inconsistent performance across vision tasks at d...
Masked Differential Privacy
ECCV, 2024 | Sina Sajadmanesh, Vikash Sehwag, Lingjuan Lyu, Vivek Sharma, David Schneider, Saquib Sarfraz, Rainer Stiefelhagen
Privacy-preserving computer vision is an important emerg- ing problem in machine learning and artificial intelligence. The prevalent methods tackling this problem use differential privacy or anonymization and obfuscation techniques to protect the privacy of individuals. In b...