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Sadegh Riazi is a Research Scientist at Google DeepMind, specializing in the intersection of cryptography, machine learning, and security. He earned his PhD in Computer Science and Engineering from the University of California, San Diego (UCSD), where his research focused on secure multi-party computation (MPC) and its applications to machine learning, particularly for privacy-preserving neural networks. His work aims to develop practical and provably secure systems, contributing significantly to the field of privacy-enhancing technologies. He is known for his contributions to homomorphic encryption applications and secure collaborative learning.
Sadegh Riazi's work history includes a series of influential roles in various companies. Here is a detailed list of his professional journey:
Obtained a doctorate from the University of California, San Diego, with a dissertation focused on efficient and secure techniques for privacy-preserving machine learning.
Conducting cutting-edge research on AI safety, privacy, and security at one of the world's leading artificial intelligence research laboratories.
Key contributor to the development of HE-Transformer, a significant work enabling privacy-preserving neural network inference using homomorphic encryption, published at top-tier conferences.
Authored and co-authored numerous influential research papers in leading conferences and journals in the fields of cryptography (e.g., CCS, Eurocrypt), machine learning (e.g., NeurIPS, ICML), and security.
Developed novel protocols and systems for secure multi-party computation, enhancing the efficiency and practicality of secure collaborative data analysis and model training.
University of California San Diego - Year 2016
University of California, San Diego - Rady School of Management - Year 2017
Rice University - Year 2014
Sharif University of Technology - Year 2010
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