Research Areas:

1. Parallel Computing;

2. Deep Learning;

3. Operations Research.

Publications

1. Luo, J., El Baz, D., Xue, R.*, & Hu, J. (2020). Solving the dynamic energy aware job shop scheduling problem with the heterogeneous parallel genetic algorithm. Future Generation Computer Systems, 108, 119-134.(JCR Q1, CCF C)

2. Luo, J.*, Fujimura, S., El Baz, D., & Plazolles, B. (2019). GPU based parallel genetic algorithm for solving an energy efficient dynamic flexible flow shop scheduling problem. Journal of Parallel and Distributed Computing, 133, 244-257. (JCR Q2, CCF B)

3. Luo, J.*, & El Baz, D. (2019). A Dual Heterogeneous Island Genetic Algorithm for Solving Large Size Flexible Flow Shop Scheduling Problems on Hybrid Multicore CPU and GPU Platforms. Mathematical Problems in Engineering, 2019. (JCR Q2)

4. Han, X., Ye J., Luo, J.*, & Zhou H. (2020). The effect of axis-wise triaxial acceleration data fusion in CNN-based human activity recognition. IEICE TRANSACTIONS on Information and Systems, 103(4), 813-824. (JCR Q3)

Personal Statement

Jia Luo received the bachelor’s degree from Shanghai University, China, in 2011 and the master’s degree from Waseda University, Japan, in 2015. Afterwards, she received her Ph. D, degree from Université de Toulouse, France, in 2019. Currently, she is a lecturer at College of Economics and Management, Beijing University of Technology, China. Her research interests include parallel evolutionary algorithms, deep learning and shop scheduling.