Xiaoying Wang
Scientific
Services offered
PhD-qualified Computer Vision and AI specialist with over 8 years of combined academic and industry
experience, including 4 years of postdoctoral research in collaboration with the medical device manufacturing industry, applying computer vision and machine learning to defect detection and quality inspection. Proven expertise in designing and deploying end-to-end machine learning solutions, with a strong foundation in image processing, computer vision, machine learning, model development, and performance optimisation. Known for my adaptability, integrity, and commitment to continuous learning. Seeking to contribute to impactful real-world innovations by applying advanced computer vision techniques, machine learning, large-scale data analysis, and artificial intelligence.
Experience
Digital Research Analyst at Intersect Australia | Deakin University from Jan. 2023 – current
Delivered tailored digital research solutions to over 100 projects, emphasis on medical and healthcare
researchers to achieve impactful outcomes through effective data management, analysis, and tool & platforms utilisation and integration.
Acted as a strategic connector between the Research Office, Graduate Researcher Academy, IT, and Library, facilitating the development and implementation of cohesive digital research services.
Coordinated and delivered training programs on advanced digital technologies, including Machine Learning, Python, R, HPC (Slurm), Unix Shell, REDCap, NVivo, Excel, Qualtrics, MATLAB, and LabArchives (designed the training), with a focus on data collection, management, visualisation, and analysis.
Provided expert guidance in both quantitative and qualitative data analysis using tools such as MATLAB, Python, Excel, and NVivo, uncovering patterns and insights from complex datasets.
Postdoctoral Researcher (in collaboration with Cook Medical, Australia) at RMIT University from Jun. 2018 – Dec. 2022
Designed and developed an AI vision system encapsulating machine learning models to automatically detect multiple defect types with over 96% classification rate in the advanced medical devices manufacturing.
Automated and optimised synthetic image generation (in batches) process on HPC based on created digital designs (3D CAD models on SolidWorks).
Defined quantitative quality standards of the studied medical devices through data modeling (using both statistical regression and machine learning models on MATLAB and Python, outperformed state-of-the-art models.
Education
PhD of Electrical and Computer Engineering
Applied advanced computer vision techniques and multiple target tracking algorithms to the automatic
detection/segmentation and tracking of dynamic behaviours of multiple small organisms.
Developed MSBOTS system(by C++ and MATLAB), which was the only system world-wild at that time
with the ability to process the realistic and complex microscopic videos without any imaging constraints, outperforming five leading academic and commercial systems and published on Scientific Reports journal by Nature publication group.
Created an annotated dataset for zebrafish larvae tracking using a crowdsourced approach (which was made publicly available for open science and repeatable research), saved 80% resources.
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