Junping Li

I’m Junping Li, MS in Marine Science at Shanghai Jiao Tong University and BE in Automation (EECS) at Ocean University of China. I studied mapping nonlinearity of language and modal space with large model at AI Institute, Shanghai Jiao Tong University, and cybernetics & learning of cross environment vehicle (CEV) or hybrid aerial underwater vehicle (HAUV), such as factor, condition, control strategy and deep reinforcement learning, at Shanghai Jiao Tong University, advised by Prof. Zheng Zeng. During my university years, I was awarded Outstanding Student, Outstanding Graduate, Academic Excellence Scholarship and Practice Scholarship.

My background covers cybernetics, system and control theory, machine learning, transformer and large models, vehicle decision planning, and robotics, I learn and like to learn knowledge, causal inference, cognitive science, game theory and others. Due to cybernetics and AI, I develop interested in such as mind, cognition, language, behavior, society, and sometimes I might think a few questions: human non Bayesian or non scientific cognition but enough natural/effective, rule learning that we in most cases are based on rules and fuzzy logic, with explainable expression, abstract physiology and network compatibility, and cognition embedding/empowerment for something. I hope to combine cybernetics, AI, psychology, linguistics and multiple disciplines to propose new ideas, theories and works, and they can be applied to the development of human and society, and make contributions to the world we live in.

Research Experiences & Publications

Mapping Nonlinearity of Language and Modal Space with Large Model

AI Institute, Shanghai Jiao Tong University, and Institute of Automation, Chinese Academy of Sciences, 2025

Designed a modal space network, built, trained, and tuned the model based on LLaMA and transformers by RMS norm, KV cache and grouped query attention; Modal space alignment training with nonlinear mapping and fine tuning based on Qwen and SigLIP; Comparison of training effects about multiple mappings.

Nonlinear Control and Deep Reinforcement Learning of CEV/HAUV

Junping Li, H Zhou, D Lu, et al. Nonlinear and reinforcement learning control for motion of hybrid aerial underwater vehicle. Neural Computing and Applications, 2024/2025.

Proposed a 3-D space cross model; Key issues: uncertainty, cross environment, constraint of environment difference; Nonlinear control laws with robustness, adaptation and fuzzy logic; Deep reinforcement learning of CEV by deterministic policy, neural networks and temporal difference learning; Various methods in the tracking cases of issues.

Cross Domain Strategy, Factors and Conditions

Junping Li, Y Jin, R Hu, et al. Trajectory tracking control of hybrid aerial underwater vehicle subject to wind and wave disturbances. Journal of Intelligent & Robotic Systems, 2024.

Proposed a strategy to address the control convergence problem caused by the large change of environment transition; Key factors and conditions of the cross environment in the various scenarios with multiple variables; Critical relations and feasible domains of the factors that control conditions need to meet.

Phenomena and Mechanisms of CE with Experiments and Learning

T Wei, Junping Li, Z Zeng, et al. Trans-media resistance investigation of hybrid aerial underwater vehicle base on hydrodynamic experiments and machine learning. Ocean Engineering, 2022.

Built the experiment platform, invention patent CN202110217870.4; Operated the cross environment experiments of CEV with various states; Obtained the key mechanisms and coefficients by multivariate analysis and neural networks.

Tools

C/C++, Matlab & Simulink, Python, PyTorch, LaTeX, TensorBoard, OpenBayes

Academic Services

Neural Computing and Applications, ICRA, IROS

Contact

Email: ljp.id [at] sjtu.edu.cn
Web: junpingli.com
Address: Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240