Yiran Zhang
Interests
💡 Health Informatics
💡 Data Analytics
💡 Big Data Development
💡 Software Engineering
Skills
⚙️ Machine Learning
⚙️ Deep Learning
⚙️ Python, R, SQL, Java, etc.
Languages
🇨🇳 Chinese
🇬🇧 English
You can also find me on:
PhD Student - University of Manchester
Hello there! I'm a second-year PhD student at the University of Manchester. I am doing multi-outcome clinical prediction modelling for my PhD project, supervised by Victoria Palin, Glen Martin, and Tjeerd Van Staa.
My research interests include healthcare statistics, data analytics, and machine/deep learning. I have experience in cancer risk analysis, synthetic electronic health records generation, and image processing. I am passionate about leveraging data science techniques to address real-world challenges. Whether it’s developing innovative algorithms or collaborating on interdisciplinary projects, I thrive on pushing the boundaries of knowledge and making a positive impact.
EDUCATION
2023 - Now
University of Manchester
PhD Health Informatics
2021 - 2022
University of Leeds
MSc Advanced Computer Science (Data Analytics)
PROFESSIONAL EXPERIENCE
2023 - Now
Multiple outcome prediction modelling for adverse pregnancy outcomes
With a focus on addressing the complexities of pregnancy complications, this project aims to specialise in developing and validating multiple-outcome clinical prediction models (CPMs) tailored to estimate complicated adverse pregnancy outcomes and comparing the efficacy of multi-outcome models against traditional single-outcome approaches.
2022 - 2023
Breast cancer prediction model and risk factor analysis based on imbalanced data
This project uses routinely collected data from a wide range of populations, adjusts for unbalanced data, and develops an XGBoost predictive model that validates the performance of the SMOTE algorithm in dealing with unbalanced data. The project also used Bayesian networks to analyse risk factors for breast cancer.
2022 - Now
Synthetic Electronic Health Records Generation
To protect patient privacy and provide additional training data for medical AI models, generated synthetic healthcare records from the MIMIC-III database based on Bayesian networks and GAN models.
2017 - 2020
Humanoid Robot Football (RoboCup)
The project uses NAO robots, mainly to complete 5v5 football matches. I worked as part of the lab team focusing on robot status decisions, path searching and motion adjustment. The robot's path planning was optimised by introducing the A* algorithm, and speed and stability adjustments were made to the kicking motion. We participate in the RoboCup football tournament every year and have won the second and third prizes at the national level.