Biography

I am currently a postdoctoral researcher at the Penn State Neuromechanics Laboratory, The Pennsylvania State University, at University Park, working with the laboratory’s principal investigator, Prof. Xiaogang Hu. My postdoctoral work lies at the intersection of neuromechanics, neural interfaces, machine learning, and rehabilitation robotics.

I received my Ph.D. in Pattern Recognition and Intelligent Systems from the Institute of Automation, Chinese Academy of Sciences in June 2026, under the supervision of Prof. Zengguang Hou and Prof. Xiaohu Zhou. My doctoral research connects multimodal human manipulation modeling with brain-inspired robot learning. I study how high-level brain planning and low-level muscular execution interact, how manipulation can be represented across behavioral and neural scales, and how these representations can support efficient human–robot skill transfer. During my doctoral studies, I received the 2026 Chinese Academy of Sciences President’s Special Award, the Academy’s highest honor for graduate students.

In November 2025, I was a visiting student at Victoria University of Wellington under the supervision of Prof. Bing Xue. Before my Ph.D., I received my B.S. in Automation from Tongji University (2017–2021), where I was named an Academic Star of Tongji University, the University’s highest honor for undergraduate students. I worked with Prof. Zhuping Wang and Lingzhe Zhao on monocular depth estimation and visual odometry. I have also collaborated on bioinformatics research for cancer classification and prognosis prediction.

As of July 2026, I have published or received acceptance for 19 first/co-first-author papers, including six IEEE Transactions papers. I have served as lead drafter on five invention-patent applications, three of which have been granted.

I welcome interdisciplinary research collaborations in neuromechanics, rehabilitation robotics, human–robot interaction, brain–computer interfaces, and brain-inspired robot learning.

Doctoral Dissertation

Multimodal Representation of Human Manipulation Skills and Brain-Inspired Human–Robot Skill Transfer
人类操作技能多模态表征与类脑人机技能迁移

The dissertation develops an end-to-end framework spanning cross-modal brain–muscle representation, multi-scale manipulation modeling, online EEG decoding, and bidirectional human–robot skill transfer under few- and sparse-demonstration settings.

Grants

• IEEE CIS Graduate Student Research Grant: Brain-inspired Computational Intelligence Algorithm for Human Learning Modeling — USD 3,996 (2025)
• IEEE CIS Travel Grant: Cross-modal Motor Representation Learning — USD 600 (2024)
• Direct Ph.D. Pilot Program: Brain-inspired Robot Skill Learning — RMB 50,000 per year (2023–2026)

Awards

• Chinese Academy of Sciences President’s Special Award — 57 recipients among more than 15,000 graduates (2026)
• IEEE CIS Graduate Student Research Grant (2025)
• Top Merit Student, University of Chinese Academy of Sciences — 11 recipients institute–wide (2025)
• Best Conference Paper Finalist, IEEE ICIA (2025)
• Runner–Up Prize, IEEE CIS FLAME Technical Challenge (2024)
• First Prize, 6th Shenhao Cup China Postgraduate Robot Innovation and Design Competition — top 10 of 1,200 (2024)
• First Prize, Ruwei Dai Scholarship — one recipient institute–wide (2024)
• Direct Ph.D. Pilot Program, Institute of Automation, Chinese Academy of Sciences — top 10 (2023)
• Best Paper Award, IEEE International Conference on Real–time Computing and Robotics (2022)
• China National Graduate Scholarship (2022)
• Shanghai Outstanding Graduate (2021)
• Academic Star of Tongji University — top 10 university–wide (2020)

Teaching Experience

Teaching Assistant, Medical and Rehabilitation Robotics, University of Chinese Academy of Sciences, Spring 2026. Assisted with course instruction, class activities, and student learning.

Research Mentor, University of Chinese Academy of Sciences, 2023–2026. Assisted faculty in supervising research projects for 15 undergraduate and master’s students; advised on project scoping, methodology, data analysis, and presentation.