About
Zichun Zhu is an AI engineer and a Computer Science PhD graduate from the School of Electrical Engineering and Computer Science (EECS) at The University of Queensland (UQ), with expertise in machine learning, spatiotemporal data, sequence modelling, and intelligent transportation systems.
Most recently, he led the AI transformation of an ERP software company. He redesigned its platform to be AI-ready, built an integrated AI agent and custom models, set up AI governance, and trained teams to work with AI coding tools.
His PhD research, part of an ARC Linkage Project with Transport and Main Roads and Brisbane City Council, developed deep learning, contrastive and semi-supervised methods for reconstructing vehicle trajectories from Bluetooth sensor data. He combines research-level machine learning with hands-on experience turning AI into real products.
Experience
AI R&D Contractor, Shanghai Little Antelope Software Co., Ltd., Changzhou, China (Mar 2026 - Oct 2026)
- Led the company's transition from a traditional ERP software provider to an AI-oriented, data analytics business.
- Redesigned and implemented the ERP framework to be AI-compatible and ready for future AI-driven features, including smart factory modules.
- Built an AI agent integrated into the ERP platform and trained custom models for specific tasks.
- Built an AI governance platform to manage AI usage across the company and its customers.
- Introduced an AI-assisted development workflow and AI coding agent tools, and trained teams to use the latest AI tools to improve productivity.
PhD Researcher, The University of Queensland, Brisbane, Australia (Apr 2021 - Jan 2026)
- ARC Linkage Project in collaboration with Transport and Main Roads (TMR) and Brisbane City Council (BCC).
- Developed machine learning methods to reconstruct vehicle trajectories from sparse Bluetooth sensor data on urban road networks.
- Designed and evaluated deep learning, reinforcement learning, contrastive learning and semi-supervised learning approaches for map-matching and trajectory inference.
- Built end-to-end pipelines for data preprocessing, model training and experimental evaluation on real-world transportation datasets.
- Published at IEEE MDM 2023 and ADC 2024, and presented at both conferences, see Section Publications.
Education
- Doctor of Philosophy - The University of Queensland (2021 - 2026)
- Master of Science (Computer Science) - University of Melbourne (2018 - 2020)
- Diploma in Informatics - University of Melbourne (2017 - 2018)
- Bachelor of Science in Electrical Systems - University of Melbourne (2015 - 2018)
Skills
- AI Engineering: LLM integration, AI agents, custom model training, AI governance, AI-assisted development, coding agents
- Machine Learning: PyTorch, TensorFlow, supervised learning, deep learning, sequence-to-sequence models, transformers, contrastive learning, semi-supervised learning, reinforcement learning
- Data & Analytics: Data preprocessing, feature engineering, statistical modelling, data visualisation, geospatial data analysis
- Programming: Python, Java, SQL, JavaScript, R, C++, MATLAB
- Tools & Platforms: Linux, VS Code, Git, MySQL, PostgreSQL, MongoDB, AWS (basic), QGIS, Unity3D, Arduino, ROS (Robot Operating System)
- Spoken Languages: English, Mandarin Chinese
Projects
Quant Trading Bot, Personal (2026 - Present)
- Built an autonomous research-to-trading pipeline that uses an LLM agent to generate and validate trading strategies before live deployment.
- Self-hosted a backtesting and execution engine with a web control panel to explore automated market analysis and algorithmic trading.
Infectious Disease (Malaria) Modelling, University of Melbourne (May 2019 - July 2020)
- Developed a population-level simulation model incorporating within-host parasite dynamics to investigate malaria transmission; Explored how within-host interactions influence broader epidemic patterns and transmission outcomes.
- Integrated stochastic simulation techniques and parameter calibration to ensure biological realism and model robustness; Contributed to understanding the lifecycle of malaria parasites and their implications for disease control strategies.
RoboMaster Simulation Team, University of Melbourne (Sep 2019 - May 2020)
- Developed simulation components in Unity3D and C# to support autonomous robot training and testing.
- Collaborated in a multidisciplinary team on robotics simulation and AI challenge development.
Autonomous Pacman, University of Melbourne (2019)
- Built an autonomous game-playing agent in Python using search and reinforcement learning methods, including Monte Carlo Tree Search and Q-learning.
Twitter Analysis, University of Melbourne (2019)
- Developed a scalable pipeline for collecting and analysing Twitter data on HPC infrastructure using CouchDB, MapReduce, and NLP methods.
FPGA Computer and Calculator, University of Melbourne (2019)
- Designed and implemented an embedded system on an FPGA board, including a custom CPU, ROM, and peripheral interfaces using Verilog.
- Developed a stack-based Reverse Polish Notation (RPN) calculator supporting signed 8-bit addition and multiplication, with overflow detection and LED status indicators.
- Integrated concepts from digital system design, microprocessor architecture, finite state machines, and hardware-software co-design in a complete hardware implementation.
Awards
- UQ Research Higher Degree Scholarship with Top-up - full doctoral stipend scholarship awarded by The University of Queensland (2021 - 2026)
Publications
- A Study on Map-Matching on Wireless Traffic Sensor Data, PhD Thesis
Zichun Zhu, Wen Hua & Jiwon Kim - CLMM: Uncertainty-aware Map-Matching for Bluetooth Data through Contrastive Learning, ADC 2024
Zichun Zhu, Fengmei Jin, Wen Hua & Jiwon Kim. 35th Australasian Database Conference (ADC), 2024 - Map-matching on Wireless Traffic Sensor Data with a Sequence-to-Sequence Model, MDM 2023
Zichun Zhu, Dan He, Wen Hua, Jiwon Kim & Hua Shi. 24th IEEE International Conference on Mobile Data Management (MDM), 2023
Presentations
- Conference Presenter, Australasian Database Conference (ADC), Gold Coast & Tokyo (Dec 2024)
- Conference Presenter, IEEE International Conference on Mobile Data Management (MDM), Singapore (Jul 2023)
Contact
Email:
LinkedIn: LinkedIn Profile