Resume

Education

• PhD student of Engineering Mathematics (2023-Now)
• Master of Data Science (2021-2022)
  • Academic performance: First Class; Distinction
  • Core courses: Large-Scale Data Engineering (66%); Statistical Computing and Empirical Methods (76%); Introduction to Artificial Intelligence (68%); Advanced Data Analytics (81%).
• Bachelor of Computer Science and Technology (2016-2020)
  • Academic performance: GPA 3.58/4.0
  • Core courses: Computer Graphs (95%); Linux (95%); Microcomputer Technology (89%); Network Security and Cryptography (96%); Mathematical Modeling (95%); Linear Algebra (87%); Advanced Mathematics (91%); Probability and Mathematical Statistics (87%); Principles of Compiler Design (92%)

Teaching

Teaching Assistance

• 09/23 EMATM0051 Large Scale Data Engineering
• 01/24 EMATM0044 Introduction to AI
• 02/24 ECONM0017 Economics with Data Science
• 08/24 ISC Data Science & Financial Technology
• 09/24 EMATM0051 Large Scale Data Engineering
• 01/25 EMATM0050 Data Science Mini Project

Marking

• 08/24 EMATM0047 Data Science Project (Dissertation)
• 12/24 EMATM0049 Technology, Innovation, Business, and Society

Publications

LLMs | Fairness/Alignment | Explainability/Interpretability

• Hu, J., Yang, M., Du, M. ,& Liu, W., (2025). Fine-Grained Interpretation of Political Opinions in Large Language Models. arXiv preprint. Preprint |

• Shu D, Zhao H, Hu J., Liu W., Cheng L. & Du, M. (2025) Large Vision-Language Model Alignment and Misalignment: A Survey Through the Lens of Explainability[J]. arXiv preprint. Preprint |

• Hu, J., Bo, H., Hong, J., Liu, X., & Liu, W. (2025). Mitigating Degree Bias Adaptively with Hard-to-Learn Nodes in Graph Contrastive Learning. Under-review.
• Hu, J., Liu, W., & Du, M. (2024). Strategic Demonstration Selection for Improved Fairness in LLM In-Context Learning. EMNLP 2024. Paper | Code | Demo |
• Yang, R., Hu, J., Li, Z., Mu, J., Yu, T., Xia, J., … & Xiong, H. (2024). Interpretable machine learning for weather and climate prediction: A review. Atmospheric Environment, 120797. Paper |
• Hu, J., Hong, J., Du, M., & Liu, W. (2024). ProxiMix: Enhancing Fairness with Proximity Samples in Subgroups. AEQUITAS ECAI 2024. Paper |
• Hu, J., Liang, Y., Zhao, W., McAreavey, K., and Liu, W. (2023) An Interactive XAI Interface with Application in Healthcare for Non-experts. xAI 2023. Paper | Demo |
• Li, M., Hu, J., and Ho Ryu K. An Efficient Tool for Semantic Biomedical Document Analysis SIST 2021. Demo
• Hu J., Li M,, Zhang Z., et al. An Efficient Semantic Document Similarity Calculation Method Based on Double-Relations in Gene Ontology. FITAT.

Projects

You may find the details of these projects here.

• Neural Networks with Bayesian Inference in ICU Data Slides
• Single-cell Differential Analysis with Explainable Machine Learning Models Slides
• Real-time Shanghai Yangshan Port’s AIS analyze .Net system Code
• National Natural Science Foundation of China Project (No. 61702324) Large-scale text mining, ontology-based graph.
• Self-testing and analyzing software based on Windows and Android platforms

Skills

• Language: (Native) Chinese; (Familiar, PTE:72) English; (Familiar, JLPT: N2) Japanese
• Programming/Scripting: Python, Java, C#, SQL, C++, Matlab; Pytorch, PEFT
• Tech: AWS, VPS, SpringBoot, HTML, Elastic Search, Android Studio, Git, Neo4j, Mysql, Flask
• Reviewer: ACL, ECAI, AISTATS, AAAI ReLM, Journal of Pattern Recognition, ACM Computing Surveys