Dr. Tan Le received the following fundings:

1. Co-PI, “Intelligent Automation for Cyber Ready Industrial Control Systems Training Program for Safe and Secure Shipbuilding Operations,” Commonwealth Cyber Initiative (CCI) Experiential Learning Program (2024-2025).

2. PI, “Hampton Roads GenCyber Host: Enhancing AI Security and Privacy Skills for High School Students and Teachers,” South Big Data Innovation Hub Partnership Nucleation program (2024 - 2025). 

3. PI, “Joint Semi-supervised Conditional Random Field and Graph Attention Network for Powergrid Collateral Damage Analysis and Fault Diagnosis,” Visiting Faculty Research Program (VFRP), US Air Force Research Laboratory (AFRL), Rome, NY, Summer 2023.

4. Co-PI, “Collaborative Research: CISE-MSI: DP: CNS: Efficient Data Communication and Processing for Intelligent Medical Systems with Edge-Cloud Interplay,” NSF (2022 - 2025), Link. 

5. Co-PI, “Learning based Dynamic Spectrum Sharing for NextG Networks,” ERDC WERX 5G Applications for Army Installations, The U.S. Army Engineer Research and Development Center (ERDC), DEPARTMENT OF THE ARMY (2022 - 2023).

6. Co-PI, “Secure and privacy preserving 5G network for connected vehicles,” Commonwealth Cyber Initiative (CCI) Cybersecurity Research Collaboration Funding (2021-2022).

7. Co-PI, “Top-down 5G Networks Security Design and Implementation in Zero-Trust Network Architecture,” Deloitte, USA (2021 - 2022).

8. Co-PI, “Distributed Secure Live, Virtual, and Constructive (LVC) Air Combat System Training Environment (SLATE),” Office of the Under Secretary of Defense, DOD, 2020 (Old Dominion University).

Edge AI Capabilities o.

Papers

[1] Tan Le, Martin Reisslein, and Sachin Shetty, “Multi-Timescale Actor-Critic Learning for Computing Resource Management with Semi-Markov Renewal Process Mobility,” IEEE Transactions on Intelligent Transportation Systems, doi: 10.1109/TITS.2023.3303953, 2023. 

[2] Q. Wang, L. T. Tan, R. Hu and Y. Qian, “Hierarchical Energy Efficient Mobile Edge Computing in IoT Networks,” IEEE Internet of Things Journal, vol. 7, no. 12, pp. 11626-11639, Dec. 2020.

[3] L. T. Tan, R. Hu and L. Hanzo, “Heterogeneous Networks Relying on Full-Duplex Relays and Mobility-Aware Probabilistic Caching,” IEEE Trans. Commun., vol. 67, no. 7, pp. 5037-5052, July 2019.

[4] L. T. Tan, R. Hu and L. Hanzo, “Twin-Timescale Artificial Intelligence Aided Mobility-Aware Edge Caching and Computing in Vehicular Networks,” IEEE Trans, Veh. Tech., vol. 68, no.4, pp. 3086-3099, April 2019.

[5] L. T. Tan and R. Hu, “Mobility-Aware Edge Caching and Computing Framework in Vehicle Networks: A Deep Reinforcement Learning,” IEEE Trans. Veh. Tech., vol. 67, no. 11, pp. 10190-10203, Nov. 2018.

[6] L. T. Tan, R. Hu and Y. Qian, “D2D Communications in Heterogeneous Networks with Full-Duplex Relays and Edge Caching,” IEEE Trans. Ind. Informat., vol. 14, no. 10, pp. 4557-4567, Oct. 2018.

[7] F. Pervej, L. T. Tan and R. Hu, “Artificial Intelligence Assisted Collaborative Edge Caching in Modern Small Cell Networks,” 2020 IEEE Global Communications Conference (IEEE GLOBECOM 2020).

[8] Tan Le and Sachin Shetty, "Artificial intelligence-aided privacy preserving trustworthy computation and communication in 5G-based IoT networks", Ad Hoc Netw. 126, C , Mar 2022. 

[9] Abrar Zahin, Le Thanh Tan and Rose Qingyang Hu, “A Machine Learning Based Framework for the Smart Healthcare Monitoring,” 2020 Intermountain Engineering, Technology and Computing (IETC), Orem, Utah, USA, Sept. 2020.

[10] Abrar Zahin, Le Thanh Tan and Rose Qingyang Hu, “Sensor-based Human Activity Recognition for Smart Healthcare: A Semi-supervised Machine Learning,” International Conference on Artificial Intelligence for Communications and Networks, Harbin, China, May 25-27, 2019.

[11] Ferdous Pervej, Le Thanh Tan and Rose Qingyang Hu, “User Preference Learning-Aided Collaborative Edge Caching for Small Cell Networks,” 2020 IEEE Global Communications Conference (IEEE GLOBECOM 2020), accepted 2020.

[12] Qun Wang, Le Thanh Tan, Rose Qingyang Hu and Geng Wu, “Hierarchical Collaborative Cloud and Fog Computing in IoT Networks,” 2018 10th International Conference on Wireless Communications and Signal Processing (WCSP), Hangzhou, China, October 2018.

[13] T. Le and V. Le, “Dynamic Power Flow Analysis and Fault Characteristics: A Graph Attention Neural Network,” The 2025 International Conference on the AI Revolution: Research, Ethics, and Society (AIR-RES 2025), April 2025, accepted for publication (Accepted rate: 16%)

[14] T. Le and V. Le, “Dynamic Interference Suppression for Radar and Communication Cohabitation,” 11th Annual Conference on Computational Science & Computational Intelligence (CSCI’24), Dec. 2024, accepted for publication (Accepted rate: 17%)

[15] T. Le and V. Le, “Leveraging AI for Privacy Preservation and Trust Management in NextG-based IoT Networks,” 2024 Academic Data Science Alliance Annual Meeting (2024 ADSA Annual Meeting), Oct. 29-31, 2024 - Ann Arbor, Michigan.

[16] V. Le, “Human Motion Recognition for Next-Generation Healthcare Systems: AI-Driven Behavioral Analysis Framework,” 2024 Academic Data Science Alliance Annual Meeting (2024 ADSA Annual Meeting), Oct. 29-31, 2024 - Ann Arbor, Michigan.

[17] T. Le and V. Le, “A Privacy-aware framework of Robust Malware Detection on Indoor Robots: Hybrid Quantum Computing and Deep Neural Networks,” submitted.

[18] T. Le and V. Le, “Toward Deep Quantum Neural Network Framework for Adversarial Spoofing-Aware UAV Tracking Resilience,” submitted.

Cooperative Spectrum Sensing

[1] Trung Q. Duong, T.-T. Le, and H.-J. Zepernick, “Performance of Cognitive Radio Networks with Maximal Ratio Combining over Correlated Rayleigh Fading,” in Proc. of International Conference on Communications and Electronics (ICCE’10), Nha Trang, Vietnam, Aug. 2010.

[2] Le Thanh Tan, and Hyung Yun Kong, “Primary User Detection using a Generalized Selection Combining over Rayleigh Fading Channel,” IWIT conference, Korea, 2009.

[3] Le Thanh Tan, Jin Hee Lee and Hyung Yun Kong, “Primary User Detection using a Generalized Selection Combining over Rayleigh Fading Channel”, International Journal of Internet, Broadcasting and Communication, vol. 2, no. 2, 2010. 

Distributed Cooperative Spectrum Sensing

[1] L. T. Tan, and L. B. Le, “Joint Cooperative Spectrum Sensing and MAC Protocol Design for Multi-channel Cognitive Radio Networks,” EURASIP Journal on Wireless Communications and Networking, 2014 (101), June 2014.

[2] L. T. Tan, and L. B. Le, “Channel Assignment with Access Contention Resolution for Cognitive Radio Networks,” IEEE Transactions on Vehicular Technology, vol. 61, no. 6, pp. 2808 – 2823, 2012.

[3] L. T. Tan, and L. B. Le, “Distributed MAC Protocol for Cognitive Radio Networks: Design, Analysis, and Optimization,” IEEE Transactions on Vehicular Technology, vol. 60, no. 8, pp. 3990-4003, 2011.

[4] Le Thanh Tan, Long Bao Le, “General Analytical Framework for Cooperative Sensing and Access Trade-off Optimization,” 2013 IEEE Wireless Communication and Networking Conference (IEEE WCNC 2013), Shanghai, China, April 2013.

Compressed Sensing

[1] T. Le Thanh, K. Hyung Yun and B. Vo Nguyen Quoc, “Projected Barzilai-Borwein Methods Applied to Distributed Compressive Spectrum Sensing,” in IEEE Symposium on New Frontiers in Dynamic Spectrum (DySPAN), Singapore, pp. 1-7, April 2010.

[2] L. T. Tan and H. Y. Kong, “A Novel and Efficient Mixed-Signal Compressed Sensing for Wide-Band Cognitive Radio,” in Proc. of the 2010 International Forum on Strategic Technologies (IFOST 2010), Ulsan, Korea, Oct. 2010.

[3] Le Thanh Tan, Hyung Yun Kong, “Group Smoothed l0 Algorithm for OFDM Channel Estimation,” KICS conference, Korea, 2010.

[4] Le Thanh Tan, Hyung-Yun Kong, “Performance of Spiked Population Models for Spectrum Sensing,” Journal of The Korean Institute of Electromagnetic Engineering and Science, vol. 12, no. 3, pp. 203–209, Sept. 2012.

[5] Le Thanh Tan, Hyung-Yun Kong, “Using Subspace Pursuit Algorithm To Improve Performance of The Distributed Compressive Wide-band Spectrum Sensing,” Journal of The Korea Electromagnetic Engineering Society, vol. 11, no. 4, pp. 250–256, Dec. 2011.

Full-Duplex Communication

[1] L. T. Tan, and L. B. Le, “Design and Optimal Configuration of Full–Duplex MAC Protocol for Cognitive Radio Networks Considering Self-Interference,” IEEE Access, vol. 3, pp. 2715–2729, Dec. 2015.

[2] Le Thanh Tan and Long Bao Le, “Multi–Channel MAC Protocol for Full–Duplex Cognitive Radio Networks with Optimized Access Control and Load Balancing,” 2016 IEEE International Conference on Communications (IEEE ICC 2016), Kuala Lumpur, Malaysia, May 2016.

[3] Le Thanh Tan and Long Bao Le, “Distributed MAC Protocol Design for Full-Duplex Cognitive Radio Networks,” 2015 IEEE Global Communications Conference (IEEE GLOBECOM 2015), San Diego, CA, USA, December 2015. 

[4] L. T. Tan, L. Ying and D. W. Bliss, ``Power Control and Relay Selection in Full-Duplex Cognitive Relay Networks: Coherent versus Non-coherent Scenarios,'' in The 51st Annual Conference on Information Systems and Sciences 2017 (IEEE CISS 2017), Baltimore, Maryland, USA, March 22-24, 2017. 

Biography  

Tan Le was a Research Assistant Professor with the Virginia Modeling, Analysis and Simulation Center (VMASC), Old Dominion University (ODU), Suffolk, VA, USA, and is now an Assistant Professor with Hampton University (HU), Hampton, VA, USA. From 2002 to 2010, he was a Lecturer with Ho Chi Minh University of Technology and Education. He was a Postdoctoral Research Associate at Ecole Polytechnique de Montreal from 2015 to 2016, Arizona State University from 2016 to 2017 and Utah State University from 2017 to 2020. He received his B.Eng. and M.Eng. degrees from Ho Chi Minh University of Technology in 2002 and 2004, respectively and his Ph.D. degree from University of Quebec in 2015.

His research focuses on artificial intelligence, machine learning, cybersecurity, internet of things, vehicular networks, blockchain, 5G and beyond network, smart healthcare, edge/fog/cloud computing and software defined networking. He has accumulated solid working experiences of both industry and academic institutions relevant to cybersecurity. He oversees many essential research directions to contribute to the future development of cybersecurity. He recently worked on the team to develop an integrated secure and privacy preserving 5G-empowered hyperconnected-vehicle platform that addresses secure mobility management, intrusion detection on the controller area network, privacy preserving data sharing and trust management. He also is working with ODU researchers to propose 5G and internet of things technology tailored to realize massive, low-latency and resilient infrastructure that will benefit and secure large numbers of Department of Defense mission-supporting devices.

Our Blockchain Capabilities  

o.

Papers

[1] Eranga Herath, Deepak Tosh, Pete Foytik, Sachin Shetty, Nalin Ranasinghe, Kasun De Zoysa, "Tikiri:Towards a Lightweight Blockchain for IoT," Future Generation Computer Systems 2021.

[2] Eranga Herath, Xueping Liang, Sachin Shetty,Pete Foytik, "Rahasak - Scalable Blockchain Architecture for Enterprise Applications  ," Journal of Systems Architecture (Accepted) 2021.

[3] S. P. Gochhayat, Sachin Shetty, R. Mukkamala, P. Foytik, G. A. Kamhoua and L. Njilla, "Measuring Decentrality in Blockchain Based Systems," in IEEE Access, vol. 8, pp. 178372-178390, 2020,

[4] Muhammad Saad, Jeffrey Spaulding, Laurent Njilla, Charles Kamhoua, Sachin Shetty, DaeHun Nyang, and David Mohaisen, "Exploring the Attack Surface of Blockchain: A Comprehensive Survey", IEEE Communications Surveys & Tutorials, 2020

[5] Sachin Shetty, Charles Kamhoua, Laurent Njilla, "Blockchain for Distributed Systems Security", Wiley-IEEE Computer Society Press, 1 edition, March 19, 2019.

[6] Sachin Shetty, Xueping Liang, Daniel Bowden, Juan Zhao, Lingchen Zhang, "Blockchain-Based Decentralized Accountability and Self-Sovereignty in Healthcare Systems", Business Transformation through Blockchain, Palgrave Macmillan, 2019

[7] Deepak Tosh, Sachin Shetty, Peter Foytik, Laurent Njilla, Charles Kamhoua, "Blockchain Empowered Secure Internet-of-Battlefield Things (IoBT) Architecture”, Milcom, Los Angeles, October 2018.

Our Machine Learning Capabilities

[1] Tyler Cody, Erin Lanus, Daniel D. Doyle, and Laura Freeman, "Systematic Training and Testing for Machine Learning Using Combinatorial Interaction Testing," arXiv preprint arXiv:2201.12428 (2022).

[2] Erik Higgins, Daniel Sobien, Laura Freeman and Jonathan S. Pitt, “Ship Wake Detection Using Data Fusion in Multi-sensor Remote Sensing Applications,” AIAA SCITECH Forum, 2022.

[3] Sayyed Farid Ahamed, Priyanka Aggarwal, Sachin Shetty, Erin Lanus, Laura J. Freeman, "ATTL: An Automated Targeted Transfer Learning with Deep Neural Networks," 2021 IEEE Global Communications Conference (GLOBECOM), 2021, pp. 1-7, doi:10.1109/GLOBECOM46510.2021.9685826.

[4] Laura Freeman, Abdul Rahman, and Feras A. Batarseh, "Enabling Artificial Intelligence Adoption through Assurance." Social Sciences 10, no. 9 (2021): 322.

[5] Yili Hong, Jiayi Lian, Li Xu, Jie Min, Yueyao Wang, Laura J. Freeman, and Xinwei Deng, "Statistical Perspectives on Reliability of Artificial Intelligence Systems," arXiv preprint arXiv:2111.05391 (2021).

[6] Jiayi Lian, Laura Freeman, Yili Hong, and Xinwei Deng, "Robustness with respect to class imbalance in artificial intelligence classification algorithms," Journal of Quality Technology 53, no. 5 (2021): 505-525.

[7] Erin Lanus, Ivan Hernandez, Adam Dachowicz, Laura J. Freeman, Melanie Grande, Andrew Lang, Jitesh H. Panchal, Anthony Patrick, and Scott Welch, "Test and Evaluation Framework for Multi-Agent Systems of Autonomous Intelligent Agents," In 2021 16th International Conference of System of Systems Engineering (SoSE), pp. 203-209. IEEE, 2021.

[8] Erin Lanus, Laura J. Freeman, D. Richard Kuhn, Raghu N. Kacker, “Combinatorial Testing Metrics for Machine Learning,” IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW), 2021.

[9] Jiayi Lian, Laura Freeman, Yili Hong, Xinwei Deng, “Investigating the Robustness of Artificial Intelligent Algorithms with Mixture Experiments,” arXiv preprint arXiv:2010.15551 (2020).

[10] Jianyu Su, Jing Huang, Stephen Adams, Qing Chang, and Peter A. Beling. "Deep multi-agent reinforcement learning for multi-level preventive maintenance in manufacturing systems," Expert Systems with Applications 192 (2022): 116323.

[11] Stephen Adams, Tyler Cody, and Peter A. Beling, “A survey of inverse reinforcement learning,” Artificial Intelligence Review, 2022.

[12] Stephen Adams, Tyler Cody, Peter A. Beling, “Pareto-Optimal Active Learning with Cost,” IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2021.

[13] Tyler Cody, Peter A. Beling, “A systems theory of transfer learning,” arXiv preprint arXiv:2107.01196 (2021).

[14] Tyler Cody, and Peter A. Beling, "Heterogeneous Transfer in Deep Learning for Spectrogram Classification in Cognitive Communications," In 2021 IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW), pp. 1-5. IEEE, 2021.

[15] Alex Langevin, Tyler Cody, Stephen Adams, and Peter Beling, "Generative adversarial networks for data augmentation and transfer in credit card fraud detection," Journal of the Operational Research Society (2021): 1-28.

[16] Cody Fleming, Carl Elks, Georgios Bakirtzis, Stephen C. Adams, Bryan Carter, Peter A. Beling, and Barry Horowitz, "Cyberphysical Security Through Resiliency: A Systems-Centric Approach," arXiv preprint arXiv:2011.14469 (2020).

[17] Alan Wang, Jianyu Su, Arsalan Heydarian, Bradford Campbell, and Peter Beling, "Is my sensor sleeping, hibernating, or broken? A data-driven monitoring system for indoor energy harvesting sensors," In Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, pp. 210-219. 2020.