Asst. Prof. Jingjie Yeo
Cornell University, USA

Prof. Jingjie Yeo is an assistant professor in Cornell University’s Sibley School of Mechanical and Aerospace Engineering and the Principal Investigator at the J² Lab for Engineering Living Materials. His research program aims to lead advances in computationally designing and characterizing environmentally sustainable materials, with a focus on bacteria-based engineered living materials and related extracellular biopolymers and biominerals. To achieve this goal, his group develops and applies computational techniques of molecular simulations, agent-based models, and machine-learning. Prof. Yeo has received the NSF's most prestigious award, the NSF CAREER award in 2024, and the highest teaching award in Cornell's College of Engineering, the Dennis G. Shepherd Excellence in Teaching Award in 2023. Prior, he was a research scientist in the Institute of High Performance Computing, Singapore, and a postdoc at Tufts University and Massachusetts Institute of Technology. He received his Ph.D. and his B.Eng. degrees from Nanyang Technological University Singapore.

 

 

Assoc. Prof. Keiichi Takahashi
Kindai University, Japan

Keiichi Takahashi received his master's degree and worked for 10 years in the computer department of a steel company, where he was responsible for the development of various automatic control systems, including an optimal scheduling system based on genetic algorithms. He studied in a doctoral program while working as a project leader and received his PhD from the Muroran Institute of Technology. He has been at Kindai University (Fukuoka Campus) since 2004, where he has been in charge of software engineering training courses such as project management and web application development. He is currently an associate professor in the Department of Information and Computer Science. He is interested in engineering education and learning support systems. His recent research theme is the construction of a support system for programming learners based on log analysis, which aims to obtain students' stumbling blocks from log information during programming using Ruby on Rails, one of the frameworks for web application development, and to solve the students’ stumbling blocks themselves.