Houbing Song is an IEEE Fellow and tenured associate professor leading the SONG Lab, where he drives research at the intersection of AI, big data analytics, cybersecurity, and cyber-physical systems. He serves in senior editorial and leadership roles across IEEE and ACM—co-editing IEEE Transactions on Industrial Informatics and chairing ACM EIGTRUST—shaping research agendas for trustworthy AI and IoT. His work on integrating AI with IoT and aviation big data has been recognized both academically and in mainstream media, and his lab alumni populate universities and firms like Auburn, IBM, and Gulfstream. Houbing has founded multiple centers and working groups (including TRUST-IoT and an NSF IUCRC planning center) that translate algorithmic advances into resilient, real-world systems. A multidisciplinary engineer with dual Ph.D. training in communications and intelligent transportation, he is notable for blending signal-processing rigor with practical cybersecurity and explainable AI for large-scale IoT deployments. He is actively recruiting Ph.D. and postdoctoral researchers and maintains strong international research collaborations.
8 years of coding experience
19 years of employment as a software developer
Master of Science, Civil Engineering (Transportation), Master of Science, Civil Engineering (Transportation) at The University of Texas at El Paso
Ph.D., Electrical and Computer Engineering, Ph.D., Electrical and Computer Engineering at University of Virginia
UAC is a Live Response collection script for Incident Response that makes use of native binaries and tools to automate the collection of AIX, Android, ESXi, FreeBSD, Linux, macOS, NetBSD, NetScaler, OpenBSD and Solaris systems artifacts.
Contributions:2 PRs, 24 pushes, 2 branches in 10 months
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