H. Shi
Contact
Department of Mechanical & Materials Engineering
Spencer Engineering Building,
Room SEB 3089
Western University
Tel: 519-661-2111 ext. 84771
Fax: 519-661-3020
hshi265@uwo.ca
H. Shi
Assistant Professor, Ph.D., P.Eng.
Ph.D., University of Toronto, 2020
M.A.Sc., University of Toronto, 2016
B.A.Sc., University of Toronto, 2014
My Research Group
Dr. HaoTian Harvey Shi is an Assistant Professor of advanced manufacturing in the Department of Mechanical and Materials Engineering at Western. Prior to joining Western, Dr. Shi was a post-doctoral research associate at the University of Cambridge, U.K., developing multi-lengths-scale hierarchical fiber-based printing technologies for fog harvesting and breath sensing applications. Dr. Shi received his Bachelor of Applied Science in Engineering Science from the University of Toronto in 2014. He then completed his M.A.Sc. (2016) and Ph.D. (2020) in Mechanical Engineering, as part of the Toronto Institute for Advanced Manufacturing (TIAM) at the University of Toronto, with a specialization in designing hierarchical functionalized nanostructures for energy storage. Dr. Shi was a recipient of the NSERC PGS-D doctoral award, the Ontario Graduate Scholarship (OGS), and the NSERC PDF Fellowship.
Research Interests
Dr. Shi’s research group (Data-Driven Advanced Manufacturing (D2M) Group) is working on designing the next generation of sustainable functional materials for additive hierarchical manufacturing across multiple length scales. By combining materials science fundamentals and mechanical design principles, improved materials properties along with optimal hierarchical manufacturing parameters can be revealed for enhanced device performance in the following application areas:
- Energy Storage (Supercapacitors and Zn-ion Batteries)
- Biomedical Sensors (Tactile, Strain, Temperature, Humidity, etc.)
The D2M group situates at the intersection between advanced manufacturing, nanoengineering, and material informatics. Hidden relations between the device performance and manufacturing parameters can potentially be revealed and further optimization on functional material quality and performance can be made.