Yu Feng, Ph.D.
Associate Professor
Education
Ph.D., Mechanical Engineering
North Carolina State University, 2015
M.S., Mechanical Engineering
North Carolina State University, 2010
B.S., Engineering Mechanics
Zhejiang University, 2007
Professional Experience
Research Assistant Professor, Mechanical Engineering
North Carolina State University, 2015
Research Scientist
DoD Biotechnology HPC Software Applications Institute (BHSAI), 2015
Postdoctoral Researcher, Mechanical Engineering
North Carolina State University, 2013
Professional Honors and Affiliations
OSU President’s Fellows Faculty Research Award, 2025
Academic Co-chair, Pharma Strategy Task Force – Avicenna Alliance, 2024 – Present
NSF EPSCoR Research Fellow, 2024
Distinguished Early Career Faculty Award at OSU, 2024
NSF Division of Civil, Mechanical and Manufacturing Innovation (CMMI) Panel Fellow, 2024
Development Committee, American Association for Aerosol Research (AAAR), 2022 - Present
Journal of Aerosol Science Excellence in Research Award, 2022
Chair, Health Related Aerosol Working Group in American Association for Aerosol Research (AAAR), 2020 - 2021
Vice Chair, Health Related Aerosol Working Group in American Association for Aerosol Research (AAAR), 2019 - 2020
Major Areas of Interest
Dr. Yu Feng’s major areas of interest lie at the intersection of computational modeling, biomedical engineering, and pulmonary healthcare innovation. His research focuses on Computational Fluid-Particle Dynamics (CFPD) and lung aerosol dynamics, aiming to understand and optimize the transport and deposition of inhaled particles within the complex human respiratory system. He is particularly passionate about developing AI-empowered smart inhalers for precise and patient-specific drug targeting small airways, enhancing therapeutic outcomes for various lung diseases. Dr. Feng also works extensively on occupational exposure health risk assessments, leveraging advanced numerical tools to evaluate inhaled aerosol toxicity in industrial environments. His work bridges multiphysics simulations, machine learning algorithms, and digital twin technologies, creating noninvasive, cost-effective, and physiologically realistic solutions to address pressing challenges in pulmonary medicine and public health.
Recent Research Activities
- AI-enhanced indoor viral transmission risk modeling
- AI-CFD-DEM-based Reduced Order Models (ROMs) for inhaler comparability
- AI-assisted digital human testing platform for inhaler innovation
- AI-CFD digital twin for distillation efficiency
- Toxicology of e-cigarette and cannabis aerosols
- Smart inhaler for targeted small airway drug delivery