湖北孝感人，本硕就读于中南财经政法大学，计算机科学与技术专业。博士就读于中国科学院半导体研究所，电路与系统专业。主要从事基于深度学习的符号回归算法、认知追踪、时间序列等研究。
发表论文：
Liu J, Wang G, Li W, et al. Transcendental equation solver: A novel neural network for solving transcendental equation[J]. Applied Soft Computing, 2022, 117: 108425. (SCI, Q1,中科院二区)
Liu J, Li W, Yu L, et al. SNR: Symbolic network-based rectifiable learning framework for symbolic regression[J]. Neural networks, 2023, 165: 1021-1034. (SCI, Q1,中科院一区，发表时)
Liu J, Wu M, Yu L, et al. CaMo: Capturing the modularity by end-to-end models for Symbolic Regression[J]. Knowledge-Based Systems, 2025, 309: 112747. (SCI, Q1,中科院一区)
Li Y, Liu J, Wu M, et al. MMSR: symbolic regression is a multi-modal information fusion task[J]. Information Fusion, 2025, 114: 102681. (SCI, Q1,中科院一区，共同一作)
Liu J, Li W, Yu L, et al. Mathematical expression exploration with graph representation and generative graph neural network[J]. Neural Networks, 2025, 187: 107405.(SCI, Q1,中科院二区)
Liu J, Li S. A dependency-based hybrid deep learning framework for target-dependent sentiment classification[J]. Pattern recognition letters, 2023, 176: 160-166. (SCI, Q2,中科院三区)
Liu J, Yu L, Sun L, et al. Fitting objects with implicit polynomials by deep neural network[J]. Optoelectronics Letters, 2023, 19(1): 60-64. (EI期刊)
承担项目：
基于预训练的增量符号回归模型，2025年度国家资助博士后研究人员计划（C档），主持，24万，CZC20251069.
知识融合的神经网络分治约简符号回归方法，国家自然科学基金重大研究计划，参与，90万，2024，No. 92370117.