Funded Research Projects in AI/ML (Artificial Intelligence/Machine Learning) Applications

• Co-Principal Investigator, NOIC: Process AI Phase 1 AI/ML Process Control, supported by the Ohio Department of Development via Northwest Ohio Innovation Consortium, 2025-2026
• Co-Principal Investigator, Clinical Adaptive Performance Enhancement Through Human-AI Teaming (CAPE-HAT), supported by the National Science Foundation, 2024-2028
• Principal Investigator, AI and Robotics for Drug Discovery with Zebrafish: Embryo Sorting System, supported by the University of Toledo Foundation, 2023-2028
• Co-Principal Investigator, Integrated LIBS-RAMAN-AI System for Real-Time, In-Situ Chemical Analysis of MSW, supported by the U.S. Department of Energy (DOE) via Lehigh University, 2021-2025
• Principal Investigator, Constructing Meta-Models Using Machine Learning, supported by Ingersoll-Rand plc, 2019
• Principal Investigator, Machine Learning with Edge and Cloud Computing for Crowdsensing of Pavement Conditions, supported by Pennsylvania Department of Community and Economic Development via Pennsylvania Infrastructure Technology Alliance, 2019-2020

Teaching and Invited Talks

• EECS4980/5980: Generative AI and Large Language Models (with Ali Malekpour, 3 credit hours, Fall 2024)
• Teaching modules on AI and Semiconductor, Spring 2024, for Ohio TechNet Northeast Ohio (OTN-NEO) Semiconductor Workforce Consortium, supported by Intel via LCCC, 2024-2025
• Invited talk on Embedded Crowdsensing for Pavement Monitoring and its Incentive Mechanisms, International Summer School on Resource-aware Machine Learning (REAML) 2022, TU Dortmund, Germany, September 14, 2022

Select / Peer-reviewed Research Publications in Deep/Reinforcement Learning/ML Applications

• Md Sakib Galib Sourav, Ahmad Javaid, and Liang Cheng, A review of AI in human-machine cooperation: Machine perspective, accepted by ACM Transactions on Autonomous and Adaptive Systems, October 2025.
• Jincheng Liu, Oluwabunmi Iwakin, Liang Cheng, et al., Machine learning in the context of laser-induced breakdown spectroscopy, in: Singh, V.K. (eds) Laser Induced Breakdown Spectroscopy (LIBS), Springer, pp. 223–237, October 2025.
• Katherine Berry and Liang Cheng, A Survey of Graph Neural Networks for Drug Discovery: Recent Developments and Challenges, arXiv:2509.07887, September 2025.
• Jincheng Liu, Oluwabunmi Iwakin, Carlos E. Romero, Liang Cheng et al. Rapid characterization of MSW and RDF feedstocks for waste-to-energy process using LIBS and ML techniques, Waste Management, Volume 206, September 2025.
• Boyang Zhou and Liang Cheng, Deep Reinforcement Learning-based Scheduling for Same Day Delivery with a Dynamic Number of Drones. The 21st International Conference on Service-Oriented Computing (ICSOC’23), Rome, Italy, Nov. 28 – Dec. 1, 2023.
• Prashish Paudel, Scott Pappada, and Liang Cheng, Automated Multimodal Performance Evaluation in Simulation-based Medical Education using Natural Language Processing. The 14th ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS 2023) Demo/Poster/WIP, San Antonio, Texas, May 2023.
• Boyang Zhou and Liang Cheng. Mitigation of Scheduling Violations in Time-Sensitive Networking using Deep Deterministic Policy Gradient. In Workshop on Flexible Networks Artificial Intelligence Supported Network Flexibility and Agility (SIGCOMM ’21), August 27, 2021.
• Huan Yang, Liang Cheng, and Mooi Choo Chuah, Deep-learning-based network intrusion detection for SCADA systems, 2019 IEEE International Workshop on Cyber-Physical Systems Security (CPS-Sec’19) at IEEE Conference on Communications and Network Security (CNS’19), Washington, D.C., June 10-12, 2019.