Yi Sun is a Senior Lecturer at the School of Engineering and Computer Science. She joined the university in 2003 after she obtained her Ph.D. in the Department of Information Engineering at Aston University. Her Ph.D. research work was funded by Pfizer Central Research. She has worked extensively in applied machine learning and data visualisation. Yi Sun is strongly interested in working in Computational Intelligence for Bioinformatics and Cheminformatics. She has especially worked on developing computational models for predicting skin permeability coefficients and identifying transdermal enhancers. In recent years, she has also spent more time in image pattern recognition, natural language processing, and time series analysis.

Probabilistic Data Modelling; Data Visualisation; Bioinformatics; Cheminformatics; Natural Language Processing; Time Series Analysis.

• Current research student supervision in the areas of
  
  • Counting clustered bacterial colonies using deep neural networks.
  • Telecom customer churn prediction using machine learning techniques.
  • Instance weighting for clustering.
  • Deep learning for conditions detection in chest radiographs.
  • Text classification using deep 2D convolutional neural network.
  • Automatically verifying periodicity presented in time-series astronomical data.
  • Interaction patterns using machine learning to facilitate automation, personalisation and control for the UX  design of the consumer IoT.
  • Exploring the use of online reviews in the development of video game. 
• Supervised eight successful Ph.D. projects
  • Reducing errors in optical data transmission using trainable machine learning methods (Dr. Weam M. Binjumah, 2020).
  • The application of data mining techniques to learning analytics and its implications for interventions with small class sizes (2020).
  • Assessing variability of EEG and ECG/HRV time series signals using a variety of non-linear methods (Dr. Ronakben P. Bhavsar, 2019).
  • Automatic object detection and categorization in deep astronomical imaging surveys using unsupervised machine learning (2018).
  • Predicting the absorption rate of chemicals through mammalian skin using machine learning algorithms (2016).
  • Software defect prediction using static code metrics: formulating a methodology (2012).
  • Improving computational predictions of cis-regulatory binding sites in genomic data (2012).
  • A multi-level machine learning system for attention-based object recognition. (2010).

Foundations of Data Science; Neural Networks and Machine Learning; Data-Driven System.