I am a postdoctoral researcher at Australian Institute for Machine Learning, the University of Adelaide. I obtained my PhD degree at the Australian Institute for Machine Learning, The University of Adelaide, under the supervision of Prof. Javen Shi，A/Prof. Lingqiao Liu, and Dr Ehsan Abbasnejad. My research focuses on developing innovative AI education and quantitative trading solutions by leveraging Causal AI and NLP to uncover insights into complex decision-making processes.

Email: jinan.zou (at) adelaide.edu.au

I am actively seeking motivated undergraduate students and postgraduates interested in collaboration. I am also committed to mentorship and welcome inquiries from those new to the field of quant trading and AI Education. Please feel free to email me if you are interested.

News

• Feb 2025 I will join the AI in Education at Oxford University (AIEOU) interdisciplinary research hub as an academic collaborator.
• Nov 2024 I will serve as the panellist at AI in Education Conference.
• Aug 2024 One paper on CasualAI for stock trading is accepted by TMLR.
• Aug 2024 I will serve as the AI Scientist for STEM camp organized by CSIRO Education.
• Feb 2024 One paper on OoD detection for NLP is accepted by COLING 24.
• Dec 2023 One paper on Document-level event extraction is accepted by ICAIF 23.

Experience

• Postdoctoral Researcher, Australian Institute for Machine Learning, the University of Adelaide, 2023-current
• AI Education and Outreach Liaison, Responsible AI Research Centre (RAIR), 2025-current
• Teaching Assistant, the University of Adelaide, 2019-2023

Selected Publications

Astock: A New Dataset and Automated Stock Trading based on Stock-specific News Analyzing Model

Zou, J., Cao, H., Liu, L., Lin, Y., Abbasnejad, E. and Shi, J.Q., 2022, December. Astock: A New Dataset and Automated Stock Trading based on Stock-specific News Analyzing Model. In Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP) (pp. 178-186).

Stock Market Prediction via Deep Learning Techniques: A survey

Zou, J., Zhao, Q., Jiao, Y., Cao, H., Liu, Y., Yan, Q., Abbasnejad, E., Liu, L. and Shi, J.Q., 2022. Stock market prediction via deep learning techniques: A survey. arXiv preprint arXiv:2212.12717.

A Generative Approach for Comprehensive Financial Event Extraction at the Document Level

Zou, J., Liu, Y., Qi, Y., Cao, H., Liu, L. and Shi, J.Q., 2023, November. A Generative Approach for Comprehensive Financial Event Extraction at the Document Level. In Proceedings of the Fourth ACM International Conference on AI in Finance (pp. 323-330).

Semantic Role Labeling Guided Out-of-distribution Detection

Zou, J., Guo, M., Tian, Y., Lin, Y., Cao, H., Liu, L., Abbasnejad, E. and Shi, J.Q., 2024, May. Semantic Role Labeling Guided Out-of-distribution Detection. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (pp. 14641-14651)

InvariantStock: Learning Invariant Features for Mastering the Shifting Market

Cao, H., Zou, J., Liu, Y., Zhang, Z., Abbasnejad, E., Hengel, A.V.D. and Shi, J.Q., 2024. InvariantStock: Learning Invariant Features for Mastering the Shifting Market. arXiv preprint arXiv:2409.00671.

Projects Experience

• Feb 2023 - Current: GRDC-Funded project: Automated Snail Detection, Tracking, and Counting Using Computer Vision for more effective control of snails in Australian Grain Crops.
• Jan 2024 - Current: Enhancing AI Education for School Students with Large Language Models (LLM) for Personalized and Engaging Learning Experiences.
• Jan 2023 - Current: Leveraging Large Language Models (LLM) and Causal AI for Advanced Portfolio Management Solutions.
• July 2022 - July 2023: Utilizing Large Language Models (LLM) for Sentiment Analysis on Twitter to Enhance Insights for Roche Australia.

Services

• Reviewer for ICAIF 24, CoNLL 24