Summary
Yifan Zhang is a data scientist with 11 years of experience applying machine learning and deep learning to high-impact problems across finance, insurance fraud detection, and digital agriculture. With a PhD in Computer Science and postdoctoral work at CSIRO and The University of Queensland, he has built large-scale satellite imagery pipelines, LSTM/GAN-based anomaly detectors, and production-grade fraud models using Spark that improved feature engineering throughput 20x and model accuracy by about 40% versus baseline policies. He blends research and product delivery—publishing over 10 ML papers, winning the 2020 Kaggle Open Data Research Grant, and translating models into calibrated, QA-tested systems for customers. Comfortable across time series, tabular, and image domains, he leverages Transformers, GANs, YOLO-family detectors and classical methods to solve messy real-world data problems. Based in Queensland, Australia, Yifan is equally at home prototyping novel architectures and operationalizing them at scale for business impact.
11 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Computer Science, Doctor of Philosophy (Ph.D.) Computer Science at QUT (Queensland University of Technology)
Master's degree Control Science and Engineering, Master's degree Control Science and Engineering at Beihang University
English, Chinese