Hewenxuan Li Headshot

Hewenxuan Li

Assistant Professor, Mechanical Engineering

Department of Mechanical Engineering
Kate Gleason College of Engineering

Hewenxuan Li

Assistant Professor, Mechanical Engineering

Department of Mechanical Engineering
Kate Gleason College of Engineering

Bio

Dr. Li's research interests concentrate on the intelligent integration of mobile sensing, perception, monitoring, and intervention for aerospace and mechanical systems. He has been developing data-driven system identification, state estimation, and control for complex systems. He is also dedicated to building cyber-physical testbeds to investigate neuromorphic perception and control for bio-inspired flapping flights. He received his Ph.D. in Mechanical Engineering and Applied Mechanics from the University of Rhode Island. Before joining RIT, he was a postdoctoral associate in the mechanical and aerospace engineering department at Cornell University. His research interests include:

Research Interests

Dr. Li's research interests mainly include:

1. Dynamics and control of biomimetic robots

2. Intelligent structural health monitoring

3. Damage estimation and prognostics

4. Deep learning and foundation models for engineering resilience

Openings

I am currently accepting highly-motivated and resilient PhD students with a background in mechanical engineering, applied mechanics, applied mathematics and physics. Students who are interested in conduting pioneering research in developing cyber-physical biomimetic robot development environment and/or building intelligent/autonomous health monitoring systems for mechanical systems are welcomed to apply.

Currently Teaching

MECE-320
3 Credits
This required course introduces the student to lumped parameter system modeling, analysis and design. The determination and solution of differential equations that model system behavior is a vital aspect of the course. System response phenomena are characterized in both time and frequency domains and evaluated based on performance criteria. Laboratory exercises enhance student proficiency with model simulation, basic instrumentation, data acquisition, data analysis, and model validation.