Jun Zou is an accomplished Staff Research Scientist at Google, specializing in the field of Trustworthy AI. His research and development efforts are concentrated on enhancing the safety, robustness, efficiency, and alignment of Large Language Models (LLMs) and other sophisticated machine learning systems. He is deeply involved in pioneering techniques to make AI more reliable, fair, and interpretable, contributing significantly to Google's initiatives in responsible AI development. His work often addresses critical challenges such as adversarial attacks, data privacy in machine learning, and the overall dependability of AI applications in real-world scenarios.
Jun Zou's work history includes a series of influential roles in various companies. Here is a detailed list of his professional journey:
Leads and contributes to cutting-edge research projects in Trustworthy AI, focusing on developing robust, secure, and ethical artificial intelligence technologies, particularly for Large Language Models.
Authored and co-authored numerous influential papers published in top-tier AI conferences and journals (e.g., NeurIPS, ICML, ICLR), advancing the scientific understanding of machine learning reliability and safety.
Completed doctoral research with a strong focus on machine learning, AI security, and privacy, laying the groundwork for his impactful contributions to the field of Trustworthy AI.
Made significant contributions to understanding and mitigating vulnerabilities in machine learning models, particularly against adversarial attacks, and developing frameworks for safer AI systems.
The University of Texas at Dallas - School of Management - Year 1997
Shanghai International Studies University - Year 1989
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