معرفی
Giulia Fanti is an academic researcher affiliated with Carnegie Mellon University in the Computer Science Department. Her research focuses on privacy-preserving technologies, blockchain systems, and machine learning mechanisms, with significant contributions to federated learning, differential privacy, and cryptocurrency network design.
- Key Research Areas: Privacy in blockchain, Generative Adversarial Networks (GANs), Federated Learning, Game Theory applications to decentralized systems.
- Recent Publications: Her work explores liquidity provisioning in decentralized finance, truncated consistency models for image generation, and private data valuation frameworks. She has contributed to venues like NeurIPS, ICLR, and SIGMETRICS, often addressing privacy-utility tradeoffs.
۰مقاله منتشرشده
