Javad Velni

Javad Velni is a Professor in the Department of Mechanical Engineering at Clemson University's College of Engineering, Computing and Applied Sciences. He directs the Velni Lab, conducting interdisciplinary research at the intersection of control theory, machine learning, and applied mathematics with applications in precision agriculture, autonomous vehicles, and industrial processes.

Education:

• Ph.D., Mechanical Engineering, University of Houston, 2008
• M.S., Electrical Engineering, University of Tehran, 2002
• B.S., Electrical Engineering, Sharif University of Technology, 1999

Research focuses on developing analytical tools for complex systems through:

• Learning-based control and reinforcement learning for industrial applications
• Distributed model predictive control of multi-agent systems
• AI-driven solutions for precision agriculture including greenhouse automation and crop monitoring
• Cyber-physical security and robust control of connected vehicles
• Data-driven modeling of nonlinear systems using Bayesian neural networks

Research publications demonstrate strong emphasis on model predictive control variants (distributed, learning-based, safe) with applications spanning agricultural technology, automotive systems, plasma processing, and multi-agent coordination. Recent work increasingly integrates Bayesian optimization, reinforcement learning, and physics-informed neural networks.

Significant research grants include:

• Collaborative Research: CPS: Medium: Multi-Scale Sensing and Control for Optimizing Crop Production and Energy Efficiency (2024)
• GOALI/Collaborative Research: Control-Oriented Modeling of Natural Gas Engines (2022)
• CPS: DFG Joint: Perceptive Stochastic Coordination in Automated Vehicle Platoons (2020)
• Distributed Predictive Control of Microplasma Jet Arrays (2019)

The Velni Lab develops distributed control algorithms and coverage strategies for heterogeneous multi-agent systems, with experimental applications in agricultural monitoring, vehicle platooning, and plasma-based biomaterial processing.