Meikang Qiu received the BE and ME degrees from Shanghai Jiao Tong University and received Ph.D. degree of Computer Science from University of Texas at Dallas. Currently, He is the Department Head and tenured full professor of Texas A&M University Commerce. He is an ACM Distinguished Member and IEEE Senior member. He is the Chair of IEEE Smart Computing Technical Committee. His research interests include Cyber Security, Artificial Intelligence, Big Data, Cloud Computing, Smart Computing, Intelligent Data, Embedded systems, etc. A lot of novel results have been produced and most of them have already been reported to research community through high-quality journal and conference papers. He was selected as Highly Cited Researcher 2020 by Web of Science. His H-index is 58 with citation around 11000.
Dr. Qiu has published 20+ books, 600+ peer-reviewed journal and conference papers (including 300+ journal articles, 300+ conference papers, 100+ IEEE/ACM Transactions papers). His paper on Tele-health system has won IEEE System Journal 2018 Best Paper Award. His paper about data allocation for hybrid memory has been published in IEEE Transactions on Computers has been selected as IEEE TCSC 2016 Best Journal Paper and hot paper (1 in 1000 papers by Web of Science) in 2017. His paper published in IEEE Transactions on Computers about privacy protection for smart phones has been selected as a Highly Cited Paper in 2017-2020. He also won ACM Transactions on Design Automation of Electrical Systems (TODAES) 2011 Best Paper Award. He has won another 10+ Conference Best Paper Awards in recent years.
Currently Dr. Qiu is an associate editor of 10+ international journals, including IEEE Transactions on Computers, IEEE Transactions on Big Data, IEEE Transactions on SMC, and IEEE Transactions on Cloud Computing. He has served as leading guest editor for IEEE Transactions on Dependable and Secure Computing (TDSC), special issue on Social Network Security. He is the General Chair/Program Chair of a dozen of IEEE/ACM international conferences, such as IEEE TrustCom, IEEE BigDataSecurity, IEEE CSCloud, IEEE ISPA, and IEEE HPCC. He has won Navy Summer Faculty Award in 2012 and Air Force Summer Faculty Award in 2009. His research is supported by US government, such as NSF, NSA, Air Force, Navy and companies such as GE, Nokia, TCL, and Cavium.
Texas A&M University-Commerce
Email: qiumeikang@yahoo.com
DVP term expires December 2023
Presentations
AI Enhanced Cyber Security
This talk will first illustrate how to use AI techniques to enhance cyber security of various systems. There are several ways to apply AI to cyber security area. This talk will use prediction-based AI technics to enhance the total security of the V2X (Vehicle-to-Everything) communication system. The talk takes serious considerations of latency while implementation the data encryption for V2X communication systems. Furthermore, the talk will discuss about deep reinforcement learning to protect the security of V2X system without scarifying safety of the vehicles. Examples and experimental results will be given to show the detailed techniques on applying AI techniques to enhance cyber security of vehicles, with the potential of implementing them to various cyber-physical systems.
Resource Allocation and Security for Heterogeneous Cloud Systems
Recent booming growth of cloud computing and Big Data &AI have brought numerous challenges to resource scheduling and security from both insider and outsider threats. The encrypted data are relatively considered a safe storage status. However, the process of encrypting data is still facing adversarial actions and data process generally is inapplicable over cipher-texts. As a type of the encryption approach allowing computations over cipher-texts, a Fully Homomorphic Encryption (FHE) can concurrently deal with the adversarial hazards and support computations on cipher-texts. This research focuses on the issue of blend arithmetic operations over real numbers and proposes a novel tensor-based FHE solution with limited resources available. The proposed approach is called a Fully Homomorphic Encryption for Blend Operations (FHE-BO) model that uses tensor laws to carry the computations of blend arithmetic operations over real numbers. The experimental results have depicted that our approach is superior in security protection and resource usage to previous methods.
