I am currently a Visiting Researcher and Head of X.Lab at XU Exponential University of Applied Sciences, Germany. My work focuses on applied artificial intelligence, large language models (LLMs), AI agents, robotics, and enterprise AI solutions, with an emphasis on bridging academic research and industrial innovation. If you are interested in academic collaboration, please feel free to contact me at, please feel free to contact me at zhanghaousm@gmail.com.

I received my Ph.D. in Artificial Intelligence from Universiti Sains Malaysia (USM). Prior to that, I obtained my M.S. and B.S. degrees in Computer Science from the International University of SUPINFO, Paris, France.

My research interests include natural language processing (NLP), large language models (LLMs), sentiment analysis, information extraction, explainable AI, and trustworthy AI. My research has focused on Aspect-Based Sentiment Analysis (ABSA), Aspect-Category-Opinion-Sentiment Quadruple Extraction (ACOSQE), Chain-of-Thought reasoning, and AI-driven information processing. I have published 10+ papers in SCI journals and top international AI conferences such as Computer Science Review, Artificial Intelligence Review, EMNLP, and ACL. More details can be found on my Google Scholar.

📖 Educations

  • Apr. 2021 - May. 2026, Ph.D. in School of Computer Science, Universiti Sains Malaysia.
  • Oct. 2013 - Sep. 2015, M.S. in Computer Science Engineering, École Supérieure d’Informatique (SUPINFO).
  • Sep. 2009 - Jun. 2013, B.S. in Computer Science Engineering, École Supérieure d’Informatique (SUPINFO).

💼 Experience

  • Oct. 2025 - present, Visiting Researcher, XU Exponential University of Applied Sciences GmbH, Potsdam, Germany.
  • Sep. 2018 - present, Lecturer, Cangzhou Normal University, Cangzhou, China.
  • Ja. 2016 - Apr. 2018, iOS Engineer & Projetct Manger, iHealth (Tianjin, China; Paris, France; California, USA)

🔥 News

  • May 2026. I completed my Ph.D. in the School of Computer Science at Universiti Sains Malaysia.
  • Apr 2026. Our paper, Tree-CoT-RT: An Explainable Multi-Path Tree-Guided Chain-of-Thought and Reinforcement Learning Framework for Aspect Sentiment Quad Prediction, was accepted by Findings of ACL 2026.
  • Jan 2026. Our survey paper, A Survey of Large Language Models for Legal Applications: Progress, Prospects and Challenges, was published in Computer Science Review.
  • Dec 2025. Our paper, SolEval: Benchmarking Large Language Models for Repository-level Solidity Smart Contract Generation, was accepted by Mains of EMNLP 2025.
  • Oct 2025. I started serving as a Visiting Researcher at XU Exponential University of Applied Sciences GmbH.

📝 Publications

CSR 2026
LLM4Law cover

A Survey of Large Language Models for Legal Applications: Progress, Prospects and Challenges

Congqing He, Haichuan Hu, Yanli Li, Hao Zhang, Quanjun Zhang

Computer Science Review (2026), JCR Q1, 中科院1区TOP, IF: 12.7

ESWA 2025
JuriSim cover

Simulating Judicial Trial Logic: Dual Residual Cross-Attention Learning for Predicting Legal Judgment in Long Documents

Congqing He, Tienping Tan, Sheng Xue, Yanyu Tan

Expert Systems with Applications (2025), JCR Q1, 中科院1区TOP, IF: 7.5

JESTECH 2025
SCNet cover

SCNet: Few-Shot Image Classification via Self-Correlational and Cross Spatial-Correlation Attention

Congqing He, Ding Xu, Ke Gong, Fusen Guo, Dapeng Wei

Engineering Science and Technology, an International Journal (2025), JCR Q1, 中科院2区, IF: 5.4

TIM 2025
TIM cover

Few-Shot Steel Strip Surface Defect Classification via Self-Correlation Enhancement and Feature Refinement

Ke Gong, Ding Xu, Fusen Guo, Zihan Wang, Fangrui Zhang, Congqing He#

# corresponding author

IEEE Transactions on Instrumentation and Measurement (2025), JCR Q1, 中科院2区, IF: 5.9

EMNLP 2024
ASQP cover

An Instruction Tuning-Based Contrastive Learning Framework for Aspect Sentiment Quad Prediction with Implicit Aspects and Opinions

Hao Zhang, Yu-N Cheah, Congqing He, Feifan Yi

Findings of the Association for Computational Linguistics: EMNLP 2024, CCF B

JKSUCI 2023
Explaining legal judgments cover

Explaining legal judgments: A multitask learning framework for enhancing factual consistency in rationale generation

Congqing He, Tienping Tan, Sheng Xue, Yanyu Tan

Journal of King Saud University - Computer and Information Sciences (2023), JCR Q1, 中科院2区, IF: 6.9

ECAI 2020
ECAI 2020 paper cover

Learning to Predict Charges for Legal Judgment via Self-Attentive Capsule Network

Yuquan Le*#, Congqing He*#, Meng Chen, Youzheng Wu, Bowen Zhou

* co-first author; # co-corresponding author

ECAI 2020, CCF B

🎖 Honors and Awards

  • 2024. X.Lab Year Star, XU & X.lab.

🤝 Academic Services

Serving as a reviewer for multiple SCI/EI journals and the TOP AI Conference, including Discover Artificial Intelligence, Cluster Computing, The Journal of Supercomputing, Frontiers in Communication and NeurIPS 2026.