罗佳，女，重庆人，工学博士。2019年1月毕业于法国图卢兹第三大学，获博士学位。现为北京工业大学经济与管理学院管理科学与工程系讲师，日本学术振兴会（JSPS）外籍特别研究员。
主要研究方向：
深度学习；仿生算法；并行计算
近期发表学术论文：
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.
2. Luo, J., Xue, R. *, & Hu, J. (2020). COVID-19 Infodemic on Chinese Social Media: A 4P Framework, Selective Review and Research Directions. Measurement and Control. (Accepted)
3. 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.
4. 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.
5. 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.
6. Deng, H., Wang, Y., Luo, J., & Hu, J. (2020, July). Similitude Attentive Relation Network for Click-Through Rate Prediction. In 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.
7. Luo, J., El Baz, D., & Hu, J. (2018, June). Acceleration of a CUDA-Based Hybrid Genetic Algorithm and its Application to a Flexible Flow Shop Scheduling Problem. In 2018 19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) (pp. 117-122). IEEE.
8. Luo, J., & El Baz, D. (2018, May). A Survey on Parallel Genetic Algorithms for Shop Scheduling Problems. In 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) (pp. 629-636). IEEE.
联系方式：
Email:jia.luo1125@qq.com
地址：北京市朝阳区平乐园100号北京工业大学经济与管理学院
邮编：100124
