Rate My Professor Hongxin Hu

Hongxin Hu is a Professor and Associate Department Chair in the Department of Computer Science and Engineering, School of Engineering and Applied Sciences, at the University at Buffalo, The State University of New York. He earned his PhD in Computer Science and Engineering from Arizona State University in 2012, where he was recognized as an Outstanding Ph.D. Student Finalist in Computer Science. Previously associated with Clemson University, where he received the Dean's Faculty Fellows Award from the College of Engineering, Computing and Applied Sciences in 2017. His research specializations encompass emerging network technologies and security, including 5G/NextG, NFV, SDN, and edge computing; machine learning applications for security, privacy, and networking; AI for social good, addressing online abuse, unsafe children games, and cyberbullying; security and privacy in IoT and cyber-physical systems; security and privacy in social networks; and usable privacy and security. He teaches graduate and undergraduate courses such as CSE 565 Computer Security, CSE 589 Modern Network Concepts, and special topics in computer science.

Hongxin Hu has received numerous awards and honors, including the NSF CAREER Award in 2019, Amazon Research Award in 2022, IEEE Big Data Security Senior Research Award in 2025, ACM SACMAT Test-of-Time Award in 2024, Distinguished Paper Award at ACSAC in 2020, Best Paper Awards at ACM ASIACCS in 2022, IEEE ICC in 2020, ACM SIGCSE in 2018, ACM CODASPY in 2014, and First Place in the ACM SIGCOMM 2018 Student Research Competition. His research is supported by funding from NSF programs (SaTC, CNS, IIS, OAC, SOC), USDOT, VMware, Amazon, Google, and Dell. Hu has published extensively in premier venues such as USENIX Security, NDSS, ACM CCS, IEEE S&P, SIGCOMM, NeurIPS, ICML, EMNLP, and CHI. Key publications include "Semantics Over Syntax: Uncovering Pre-Authentication 5G Baseband Vulnerabilities" (USENIX Security 2026), "APPATCH: Automated Adaptive Prompting Large Language Models for Real-World Software Vulnerability Patching" (USENIX Security 2025), "Jbshield: Defending large language models from jailbreak attacks through activated concept analysis and manipulation" (USENIX Security 2025), "I know what you MEME! Understanding and Detecting Harmful Memes with Multimodal Large Language Models" (NDSS 2025), and "Moderating Illicit Online Image Promotion for Unsafe User Generated Content Games Using Large Vision Language Models" (USENIX Security 2024). His work has been featured in ACM TechNews, InformationWeek, Slashdot, and NetworkWorld.