
About
Giulia Fanti is the Angel Jordan Associate Professor of Electrical and Computer Engineering at Carnegie Mellon University. She holds courtesy appointments in the Computer Science Department and Machine Learning Department, and is affiliated with CyLab Security and Privacy Institute. Her research focuses on security and privacy in distributed systems, blockchain technology, and privacy-enhancing machine learning.
- Ph.D., Electrical Engineering & Computer Science, University of California, Berkeley (2015)
- M.S., Electrical Engineering & Computer Science, University of California, Berkeley (2012)
- B.S., Electrical & Computer Engineering, Olin College of Engineering (2010)
Her research spans three major areas: privacy-preserving data sharing using generative models like GANs, privacy in decentralized networks under adversarial conditions, and blockchain scalability with rigorous mathematical modeling. She actively explores privacy-utility tradeoffs in synthetic data generation and secure federated learning frameworks.
Recent publications highlight her expertise in blockchain economics, privacy-preserving machine learning, and secure consensus algorithms. Articles span topics like game-theoretic liquidity provisioning, federated learning with synthetic data, and differential privacy in distributed systems. These works demonstrate her focus on bridging theoretical guarantees with practical implementations.
Scientific recognition includes:
- Best Paper Award Finalist @ ACM IMC
- Best Paper Award Finalist @ SIGMETRICS 2025
- ICML 2025 Oral Presentation
- ICLR 2025 Honorable Mention
- Atlantic Council Senior Fellowship
- World Economic Forum Global Future Council Fellowship
She advises numerous doctoral students and postdocs, including current advisees Shuaiqi Wang, Sangyun Lee, and former students now at institutions like HKUST, NTU, and University of Ottawa. Her group collaborates extensively through initiatives like the Upanzi Network and Secure Blockchain Initiative.
Research teams include CyLab-Africa, which she co-directs, and CMU's Secure Blockchain Initiative. Her work has been funded by NSF awards and industry grants.
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