Zhuo Lu (吕卓), Ph.D., Associate Professor
Electrical Engineering / Florida Center for Cybersecurity, University of South Florida.
4202 E. Fowler Avenue, ENG030, Tampa, FL 33620.
Office: ENB 347. Email: firstname.lastname@example.org.
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Dr. Zhuo Lu is an Associate Professor in Department of Electrical Engineering, University of South Florida. He is also affiliated with the Florida Center for Cybersecurity (CyberFlorida) and by courtesy with Department of Computer Science and Engineering. Dr. Lu received his Ph.D. degree from North Carolina State University in 2013. He currently leads the Communications, Security, and Analytics (CSA) Lab at University of South Florida. His research has been supported by NSF, ARO, ONR, DOE and Florida Center for Cybersecurity. He received the NSF CISE CRII award in 2016, the Best Paper Award from IEEE GlobalSIP in 2019, and the NSF CAREER award in 2021. Dr. Lu's research has been mainly focused on both theoretical and system perspectives on communication, network, and security. His recent research is equally focused on machine learning and AI perspectives on networking and security. He is a senior member of IEEE and a member of ACM and USENIX.
|[Dec, 2023] Using distance to decision boundary statistics to detect adversarial spectrum attacks is accepted by IEEE INFOCOM 2024.|
|[Nov, 2023] Wireless resource allocation for federated learning under practical cost contraints has been accepted by IEEE Trans. Mobile Computing.|
|[Nov, 2023] Using the most limited knowledge to improve transferablity of black-box adversarial audio examples is accepted by NDSS 2024|
|[Aug, 2023] Understanding the limitation of CSI authentication leveraging machine learning is to appear in IEEE S&P 2024.|
|[Feb, 2023] The FedLGA paper on heterogeneity of federated learning is accepted by IEEE Trans. Cybernetics.|
|[Aug, 2022] WiFi location spoofing via geolocation API has been accepted by ACM CCS 2022.|
|[Aug, 2022] Multi-Server federated learning with overlaping areas is accepted by IEEE Trans. Mobile Computing.|
|[May, 2022] Generalized federated learning via sharpness aware minimization is to appear in ICML 2022.|