Jinan Zou

Dr Jinan Zou

Postdoctoral Research Fellow B

Australian Institute for Machine Learning - Projects

Faculty of Sciences, Engineering and Technology

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

I spearhead a team dedicated to creating a versatile, scalable, and self-sufficient trading platform. We utilize cutting-edge machine learning methodologies to refine and automate distinct facets of quantitative trading strategies. Our primary focus lies in market-making and exploiting arbitrage windows across both equity and digital currency markets.

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 and attach your CV if you are interested.

For more information, please visit my homepage.

 

  • Appointments
    Date Position Institution name
    2023 - ongoing Postdoctoral Research Fellow Australian Institute for Machine Learning

  • Education
    Date Institution name Country Title
    2019 - 2023 University of Adelaide Australia PHD

  • Conference Papers
    Year Citation
    2024 Lin, Y., Xu, H., Liu, L., Zou, J., & Shi, J. (2024). Revisiting Image Reconstruction for Semi-supervised Semantic Segmentation. In 2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023 (pp. 32-40). Online: IEEE.
    DOI Scopus1
    2024 Zou, J., Guo, M., Tian, Y., Lin, Y., Cao, H., Liu, L., . . . Shi, J. Q. (2024). Semantic Role Labeling Guided Out-of-distribution Detection. In 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings (pp. 14641-14651). Online: European Language Resources Association (ELRA).
    Scopus1
    2023 Zou, J., Liu, Y., Qi, Y., Cao, H., Liu, L., & Shi, J. Q. (2023). A Generative Approach for Comprehensive Financial Event Extraction at the Document Level. In ICAIF 2023 - 4th ACM International Conference on AI in Finance (pp. 323-330). Online: Association for Computing Machinery, Inc.
    DOI Scopus2
    2022 Qin, Z., Zou, J., Luo, Q., Cao, H., & Jiao, Y. (2022). aiML at the FinNLP-2022 ERAI Task: Combining Classification and Regression Tasks for Financial Opinion Mining. In FinNLP 2022 - 4th Workshop on Financial Technology and Natural Language Processing, Proceedings of the Workshop (pp. 127-131). Vienna, Austria: Association for Computational Linguistics.
    Scopus1
    2022 Zou, J., Cao, H., Liu, Y., Liu, L., Abbasnejad, E., & Shi, J. Q. (2022). UOA at the FinNLP-2022 ERAI Task: Leveraging the Class Label Description for Financial Opinion Mining. In FinNLP 2022 - 4th Workshop on Financial Technology and Natural Language Processing, Proceedings of the Workshop (pp. 122-126). Online: Association for Computational Linguistics (ACL).
    Scopus1
    2022 Zou, J., Cao, H., Liu, L., Lin, Y., Abbasnejad, E., & Shi, J. Q. (2022). Astock: A New Dataset and Automated Stock Trading based on Stock-specific News Analyzing Model. In FinNLP 2022 - 4th Workshop on Financial Technology and Natural Language Processing, Proceedings of the Workshop (pp. 178-186). Online: Association for Computational Linguistics (ACL).
    Scopus3

Introduction to Statistical Machine Learning, Concepts in Artificial Intelligence and Machine Learning, Deep Learning Fundamentals, CS Research Projects (Honour and Master), AI Technologies

  • Position: Postdoctoral Research Fellow B
  • Phone: 83131583
  • Email: jinan.zou@adelaide.edu.au
  • Campus: North Terrace
  • Building: Australian Institute for Machine Learning, floor G
  • Org Unit: Australian Institute for Machine Learning
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