معرفی
Zhuangdi Zhu is an Assistant Professor at the Department of Cyber Security Engineering, George Mason University (GMU). She holds a PhD in Computer Science from Michigan State University (2022) and a BSc from Nanjing University of Science and Technology (2015). Prior to academia, she worked as a Senior Data & Applied Scientist at Microsoft (2022–2023) and interned at Meta, Google, IBM, and others. Her research focuses on accountable, scalable, and trustworthy AI, particularly in federated learning, reinforcement learning, robustness, fairness, privacy, and edge computing. She has organized workshops such as FedKDD and FL4Data-Mining, and her work has been recognized through grants like the CCI Grant (2024) and NAIRR Pilot Program Grant (2024).
Key research areas include federated learning with system heterogeneity, sample-efficient reinforcement learning, and de-biased representation learning. She teaches courses on federated learning and cybersecurity engineering at GMU. Her lab hosts PhD students Zhengbang Yang and Eason Zhong. She has published extensively in top venues including TPAMI, ICML, KDD, and AAAI.
Professional activities include serving as Program Chair for FedKDD and session chair at KDD. Her recent work explores AI applications in healthcare (e.g., cognitive support for seniors) and sports analytics. She also maintains a research lab focused on secure and privacy-conscious threat detection in federated systems.
