Houbing Herbert Song is a Tenured Associate Professor of Electrical Engineering and Computer Science (EECS) and the Director of the Security and Optimization for Networked Globe Laboratory (SONG Lab, www.SONGLab.us) at Embry-Riddle Aeronautical University (ERAU), Daytona Beach, FL. He received the Ph.D. degree in electrical engineering from the University of Virginia, Charlottesville, VA, in August 2012. He has served as an Associate Technical Editor for IEEE Communications Magazine (2017-present), an Associate Editor for IEEE Internet of Things Journal (2020-present), IEEE Transactions on Intelligent Transportation Systems (2021-present), and IEEE Journal on Miniaturization for Air and Space Systems (J-MASS) (2020-present). He is the editor of eight books, including Aviation Cybersecurity, Scitech Publishing, 2022, Smart Transportation: AI Enabled Mobility and Autonomous Driving, CRC Press, 2021, Big Data Analytics for Cyber-Physical Systems: Machine Learning for the Internet of Things, Elsevier, 2019, Smart Cities, Hoboken, NJ: Wiley, 2017, Security and Privacy in Cyber-Physical Systems, Chichester, UK: Wiley-IEEE Press, 2017, Cyber-Physical Systems, Boston, MA: Academic Press, 2016, and Industrial Internet of Things: Cybermanufacturing Systems, Cham, Switzerland: Springer, 2016. He is the author of more than 100 articles and the inventor of 2 patents (US & WO). His research interests include cyber-physical systems/internet of things, cybersecurity and privacy, AI/machine learning/big data analytics, edge computing, unmanned aircraft systems, connected vehicle, smart and connected health, and wireless communications and networking. His research has been sponsored by federal agencies (including US Department of Transportation, National Science Foundation, Federal Aviation Administration, US Department of Defense, and Air Force Research Laboratory) and industry. His research has been featured by popular news media outlets, including IEEE GlobalSpec's Engineering360, Association for Uncrewed Vehicle Systems International (AUVSI), Security Magazine, CXOTech Magazine, Fox News, U.S. News & World Report, The Washington Times, New Atlas, Battle Space, and Defense Daily.
Dr. Song is a senior member of ACM, and an ACM Distinguished Speaker, the faculty advisor of ERAU chapter of ACM’s Upsilon Pi Epsilon (UPE), and a senior member of IEEE. Dr. Song is a Highly Cited Researcher identified by Clarivate™ (2021) and a Top 1000 Computer Scientist identified by Research.com. Dr. Song was a recipient of the Best Paper Awards from the 12th IEEE International Conference on Cyber, Physical and Social Computing (CPSCom-2019), the 2nd IEEE International Conference on Industrial Internet (ICII 2019), the 19th Integrated Communication, Navigation and Surveillance technologies (ICNS 2019) Conference, the 6th IEEE International Conference on Cloud and Big Data Computing (CBDCom 2020), the 15th International Conference on Wireless Algorithms, Systems, and Applications (WASA 2020), the 40th Digital Avionics Systems Conference (DASC 2021), 2021 IEEE Global Communications Conference (GLOBECOM 2021) and 2022 IEEE International Conference on Computer Communications (IEEE INFOCOM 2022).
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AI/Machine Learning for Internet of Dependable and Controllable Things
The Internet of Things (IoT) has the potential to enable a variety of applications and services. However, it also presents grand challenges in security, safety, and privacy. Therefore, there is a...
Counter-Unmanned Aircraft System(s) (C-UAS): State of the Art, Challenges and Future Trends
There is an increasing need to fly Unmanned Aircraft Systems (UAS, commonly known as drones) in the airspace to perform missions of vital importance to national security and defense, emergency...
Data-Efficient Machine Learning
Most research on machine learning has focused on learning from massive amounts of data resulting in large advancements in machine learning capabilities and applications. However, many...
Real-Time Machine Learning for Quickest Detection
Quickest detection, which refers to real-time detection of abrupt changes in the behavior of an observed signal or time series as quickly as possible after they occur, is essential to enable...
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