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. My research delves into devising groundbreaking approaches for agricultural dilemmas and Quant Trading, utilizing Causal AI and NLP to unravel the nuances of decision-making paradigms.

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. Please feel free to email me if you are interested.

News

• 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
• 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)

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