吉君恺，博士，助理教授，特聘副研究员

深圳大学，大数据系统计算机技术国家工程实验室

 

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于2013年，合肥工业大学，化学工程与工艺专业，获得工学学士学位；

于2016年，日本富山大学，智能信息工程专业，获得工学硕士学位；

于2018年，日本富山大学，数理与人类系统科学专业，获得工学博士学位。

 

研究兴趣包括：计算机辅助药物设计，脑启发式神经网络；

累计发表期刊及会议论文50余篇，申请发明专利4项，

主持国家自然基金青年项目、广东省联合基金青年基金项目、深圳市自然基金面上项目。

 

研究成果包括：

[1] Junkai Ji, Jin Zhou, Zhangfan Yang, Qiuzhen Lin, and Carlos A. Coello Coello. "AutoDock Koto: A Gradient Boosting Differential Evolution for Molecular Docking." IEEE Transactions on Evolutionary Computation (2022).

[2] Junkai Ji, Yajiao Tang, Lijia Ma, Jianqiang Li, Qiuzhen Lin, Zheng Tang, and Yuki Todo. "Accuracy Versus Simplification in an Approximate Logic Neural Model." IEEE Transactions on Neural Networks and Learning Systems (2020). 

[3] Junkai Ji, Jiajun Zhao, Qiuzhen Lin, and Kay Chen Tan. "Competitive Decomposition-Based Multiobjective Architecture Search for the Dendritic Neural Model." IEEE Transactions on Cybernetics (2022).

[4] Junkai Ji, Minhui Dong, Qiuzhen Lin, and Kay Chen Tan. "Noninvasive Cuffless Blood Pressure Estimation With Dendritic Neural Regression." IEEE Transactions on Cybernetics (2022).

[5] Junkai Ji, Minhui Dong, Qiuzhen Lin, and Kay Chen Tan. "Forecasting Wind Speed Time Series Via Dendritic Neural Regression." IEEE Computational Intelligence Magazine 16, no. 3 (2021): 50-66.

[6] Junkai Ji, Shuangbao Song, Cheng Tang, Shangce Gao, Zheng Tang and Yuki Todo*, "An artificial bee colony algorithm search guided by scale-free networks." Information Sciences, 473 (2019): 142-165.

[7] Junkai Ji, Shuangbao Song, Yajiao Tang, Shangce Gao, Zheng Tang and Yuki Todo, "Approximate logic neuron model trained by states of matter search algorithm." Knowledge-Based Systems, 163 (2019): 120-130.

[8] Junkai Ji, Shangce Gao, Jiujun Cheng, Zheng Tang, and Yuki Todo, "An approximate logic neuron model with a dendritic structure." Neurocomputing 173 (2016): 1775-1783.

[9] Junkai Ji, Cheng Tang, Jiajun Zhao, Zheng Tang, and Yuki Todo. "A Survey on Dendritic Neuron Model: Mechanisms, Algorithms and Practical Applications." Neurocomputing (2022).

 

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