Yiyao Zhang

Yiyao Zhang

Master of Data Science, University of Adelaide  ·  Research Intern, Australian Institute of Machine Learning (AIML)
📍 Adelaide, South Australia, Australia
✉ yiyao663@gmail.com ✉ yiyao.zhang@student.adelaide.edu.au 📞 +61 0489 045 586

Research Statement

My research focuses on engineering intelligent systems capable of autonomous reasoning and strategic support in high-stakes environments by integrating mathematical foundations with causal inference and reinforcement learning. A central theme is the design of Multi-Agent Reinforcement Learning (MARL) architectures that are physics-informed, meta-learned, and hybrid — enabling robust coordination in complex, adversarial, and partially observable settings. Complementing this, I develop Expert Systems and explainable AI frameworks that translate learned policies into interpretable, actionable intelligence for domains including cyber defence, financial markets, and adaptive education.
Multi-Agent Reinforcement Learning (MARL) Expert Systems Explainable AI (XAI) Reinforcement Learning Causal Inference Causal Deep Learning Autonomous Cyber Defence Adversarial MARL Algorithmic Trading Portfolio Optimisation Meta-Cognitive Agents Intelligent Tutoring Systems

Education

PhD Candidate — Statistics (Qualifying Exam Passed)
—
University of South Carolina, Columbia, USA
2017 – 2020
Discontinued due to COVID-19 pandemic
Master of Data Science
GPA 6.3 / 7.0
University of Adelaide, Adelaide, Australia
2024 – 2026
Global Citizenship Scholarship recipient
Master of Data Science
GPA 3.6 / 4.0
University of South Carolina, Columbia, USA
2017 – 2019
Bachelor — UCLA–CSST Summer Research Program
GPA 3.8 / 4.0
University of California, Los Angeles, USA
2014
CSST Scholarship & Aoqing Tang International Exchange Scholarship
Bachelor Exchange — Applied Mathematics
GPA 3.7 / 4.0
Georgia Institute of Technology, Atlanta, USA
2014
Bachelor of Science — Computational Mathematics
GPA 93 / 100
Jilin University (985 / 211, Top 2%), Changchun, China
2011 – 2015
Aoqing Tang Honor Class · National Scholarship (Top Tier)

Publications & Manuscripts

Conference Papers
Synergistic MARL: Unifying Physics-Informed, Meta Learning, and Hybrid Learning Published CORE B
Y. Zhang, Y. Dong, K. Yu, W. Liu, J. Zhou, B. Liang, F. Chen
Australasian Joint Conference on Artificial Intelligence (AJCAI) — 2025
AI-Edu-App: An Integrated AI Platform for K-12 Learning Published
Y. Zhang, J. Huang, et al.
ACM International Conference on Education Technology and Artificial Intelligence (ICETAI) — 2025
Journal Articles
RegimeFolio: A Regime-Aware ML System for Sectoral Portfolio Optimisation in Dynamic Markets Published Q1 Journal
Y. Zhang, D. Goel, H. Ahmad, C. Szabo
IEEE Access — 2025
Research and Design of a Comprehensive University Public Mathematics Question Bank
Q. Cao, Y. Zhang, et al.
Network Security Technology and Application, (9), pp. 193–195 — 2014
Multivariate Statistical Analysis and Prediction of Factors Affecting PM2.5
Y. Zhang, G. Miao, et al.
Journal of Resource Conservation and Environmental Protection — 2013
Submitted Manuscripts
Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning Under Review
Y. Zhang, D. Goel, H. Ahmad
Submitted to Expert Systems with Applications
AgentVM: Agentic Multi-Agent Software Vulnerability Management Under Review
Y. Zhang, H. Ahmad, et al.
Submitted to IEEE Software
AdapTutor: An Evolving Dual-Agent Framework for Synchronous Learner Support in Online Learning Environments Under Review
Y. Zhang, X. Fan, L. Yue
Submitted to IEEE Transactions on Learning Technologies
Work in Progress
Meta-Cognitive Agents: Adaptive Reasoning with Hierarchical Task Decomposition WIP
Y. Zhang, et al.
Preparing for NeurIPS
CausalVision: Interpretable Causal Structures in Multimodal Generative Models WIP
Y. Zhang, et al.
Preparing for NeurIPS
Deep Causal Inference: Neural Kernel Methods for Optimal Experimental Design WIP
Y. Zhang, et al.
Preparing for ICML
Causal-Guided Hierarchical RL with Adaptive Human Feedback WIP
Y. Zhang, et al.
Preparing for ICML
Curvature-Aware Neural Networks for Robust Graph Representation Learning WIP
Y. Zhang, et al.
Preparing for ICLR
Towards AI-Driven Cyber Deception Mechanism for Enterprise Networks: A Literature Review WIP
Y. Zhang, D. Goel, H. Ahmad, et al.
Preparing for ACM Computing Surveys
Adaptive Graph-Enhanced Sparse Hedging (AGESH): An Efficient Differentiable Framework for High-Dimensional Portfolios WIP
Y. Zhang, Y. Dong, et al.
Preparing for Quantitative Finance

Research & Industry Experience

Research
Research Intern — AI for Financial Markets
2024 – 2025
Australian Institute of Machine Learning (AIML), Adelaide, Australia
  • Investigated and implemented state-of-the-art deep learning techniques for algorithmic trading.
  • Designed and rigorously backtested strategies using PyTorch, Scikit-learn, and Pandas.
  • Collaborated with PhD researchers to adapt novel AI models for market pattern identification.
Teaching Assistant — Statistics
2017 – 2020
University of South Carolina, Columbia, USA
  • Taught statistical concepts and data analysis techniques using R and Python.
  • Conducted tutorials on statistical modelling and machine learning fundamentals for undergraduate students.
Research Project Team Member
2012 – 2015
Jilin University, Changchun, China
  • Developed generalised linear models and predictive frameworks for large environmental datasets.
  • Conducted statistical analysis using R and SPSS to identify patterns in complex environmental data.
Industry
AI & Software Developer
2024 – 2025
Upwork – Syncove, Adelaide, Australia
  • Designed quantitative analysis tools for financial markets and event forecasting using Pandas and NumPy.
  • Developed Advanced Performance Analyzer Pro, a Python-based desktop application for probabilistic recommendations.
  • Implemented risk-optimised allocation algorithms based on Modern Portfolio Theory.
Financial Product Investment Analyst
2025
Trademax Global Markets (TMGM), Adelaide, Australia
  • Conducted market research and performance evaluation of CFD and forex products using Python.
  • Sourced and managed relationships with introducing brokers (IBs) and affiliates.
  • Developed sales strategies and provided tailored investment recommendations while managing compliance.
Algorithm Engineer
2020 – 2023
Haier Electronics, Qingdao, China
  • Developed ML algorithms for sales volume forecasting, improving accuracy by 15%.
  • Applied NLP techniques to customer feedback to extract actionable product improvement insights.
  • Designed and maintained efficient database systems for large-scale sales analytics.

Technical Skills & Languages

Programming

Python R SQL MATLAB C++ Java

ML / AI Libraries

PyTorch TensorFlow Scikit-learn Pandas NumPy LangChain OpenCV

Tools & Platforms

Git Docker AWS LaTeX

Languages & Test Scores

English — Fluent Mandarin — Native TOEFL 106/120 PTE 75/90 GRE 326/340 GRE Math 900/910

Awards & Honours

2024–2026 Global Citizenship Scholarship — University of Adelaide
2025 5th Place — AUCPL Coding Competition, University of Adelaide
2017–2020 University Teaching Assistantship — University of South Carolina
2014 UCLA CSST Scholarship & Aoqing Tang International Exchange Scholarship
2011–2015 National Scholarship (Top Tier) & University Scholarships — Jilin University
2010 Second Class Prize — National Olympiad in Informatics in Provinces (NOIP)

Referees

Prof. Ran Zhang
Vice President, Jilin University; Director, Tianyuan Mathematical Center in Northeast China; Dean, School of Mathematics, Jilin University
zhangran@jlu.edu.cn
Prof. Lin Liu
Director, 4LLab Data Analytics Group; Professor, STEM Unit, University of Adelaide
Lin.Liu@adelaide.edu.au
Prof. Wotao Yin
Director, Decision Intelligence Lab, DAMO Academy, Alibaba Group US; Professor, UCLA
wotao.yin@alibaba-inc.com
Prof. Joshua Tebbs
Director & Professor, Department of Statistics, University of South Carolina
tebbs@stat.sc.edu
Prof. Yingfei Yi
Killam Memorial Scholar; Professor, Dept. of Mathematical & Statistical Sciences, University of Alberta; Professor, Georgia Tech
yingfei@ualberta.ca