Dr. Chen ZhiYuan currently is an Associate Professor with the University of Nottingham, School of Computer Science in Malaysia. She is a Senior Fellow of the Higher Education Academy, UK since July 2022 and is a British Computer Society (BCS) Professional member. Dr Chen worked as Principal Consultant for many government agencies, research organizations and industries, such as with MIMOS at the Accelerative Technology Lab, TM R&D, Crops for the Future and Tentacle technologies. She received the MPhil and a PhD in Computer Science from the University of Nottingham in 2007 and 2011 respectively. Before joining UNM, she has been a research associate in the UK Horizon Digital Economy Research Institute. Her research interests are in the area of computer science, machine learning, data mining, user modelling and artificial intelligence. She has published many research papers in the above research area and served as keynote Speaker and Session Chair for many international conferences in computer science and engineering.
Dr. Chen ZhiYuan has expertise in machine learning, data mining, user modelling, simulation and artificial intelligence. She has a particular interest in kernel methods, especially using support vector machines to solve real world problems, such as in anti-money laundering and medical diagnosis. Her research focuses on basic and applied problems in machine learning, understanding how machine learning can help with automated learning, reasoning and decision-making for diverse applications ranging from the knowledge management, economic modelling, agricultural maturity analysis, to pipeline flaw detection,
G54SIM - Simulation for Computer Scientists G52HCI - Human Computer Interaction G52GRP - Software Engineering Group Project G52GUI - Graphical User Interfaces G53ELC - Enterprise Level Computing G53MLE - Machine Learning G54IHC - Introduction to Human-Computer Interaction G54MET - Methods for Understanding Users in Computer Science
Her major research interest is in how artificial intelligence techniques can facilitate the knowledge discovery and decision making process that is complete and efficient retrieval of relevant… read more
SIZHOU WEI, ZHIYUAN CHEN, SENTHIL KUMAR ARUMUGASAMY and IRENE MEI LENG CHEW, 2022. Data augmentation and machine learning techniques for control strategy development in bio-polymerization process Environmental Science and Ecotechnology. 11, CHUNZHI YI, SEUNGMIN RHO, BAICHUN WEI, CHIFU YANG, ZHEN DING, ZHIYUAN CHEN and FENG JIANG, 2022. Detecting and Correcting IMU Movements During Joint Angle Estimation IEEE Transactions on Instrumentation and Measurement.
CHUNZHI YI, BAICHUN WEI, ZHEN DING, CHIFU YANG and ZHIYUAN CHEN, 2022. A Self-Aligned Method of IMU-based 3-DoF Lower-Limb Joint Angle Estimation IEEE Transactions on Instrumentation and Measurement. 3194935
Her major research interest is in how artificial intelligence techniques can facilitate the knowledge discovery and decision making process that is complete and efficient retrieval of relevant information from rapidly growing volume of data. Her work addresses the challenge of combining machine learning, data mining and user modelling for different domains. She has a particular interest in kernel methods, especially using support vector machines to solve real world problems, such as in anti-money laundering and medical diagnosis. Her research focuses on basic and applied problems in machine learning, understanding how machine learning can help with automated learning, reasoning and decision-making for diverse applications ranging from the knowledge management, economic modelling, AI in chemical engineering, agricultural maturity analysis to pipeline flaw detection.
• 2022-2025 PI for "Enhancing Asset Allocation with deep learning/machine learning models for Robo-Advisor development" project from HY ALPHA SDN. BHD, Industry Fund.
• 2021-2022 PI for "Unsupervised Learning Techniques for Anti-Money Laundering" project from BAE System Applied Intelligence, Industry Fund.
• 2019-2022 PI for "Determining optimal lag time selection function with novel machine learning strategies for better agricultural commodity prices forecasting in Malaysia" project from MOHE FRGS Fund: RM 121,800.
• 2017-2019 PI for "Pipe Crack Detection System by Using the Non-destructive Testing based Mindstorm Robot with Multiple NXT Sensors" project from MOSTI Science Fund: RM 181,000.
• 2012-2016 PI for "Economic Value Prediction System for Under-Utilised Crops" project from CFFRC: RM 292,400.
• 2015-2016 PI for "The study of peer assessment for group learning activities" project from T&L Grant: RM 13,100.
• 2015-2016 PI for "Novel Machine Learning Strategies for GST Impact on Agricultural Commodity Prices by Monitoring the Short-Term Fluctuations in Malaysia" project from Faculty of Science Pump Priming Grant: RM 10,000.
• 2014-2015 PI for "Unsupervised Real Time Prediction of Faults Using the Support Vector Machine" project from Faculty of Science Conference Fund: RM 4,000.
• 2013-2014 PI for "The Unsupervised learning model of the support vector machine for money laundering detection" project from 3rd Called Collaboration with Public Universities and Agencies: RM 9,500.
• 2013-2015 Co-I for "Validating the Alzheimer's Disease Evoked Potential Test (ADEPT) task as a potential diagnostic tool for Alzheimer's Disease" project from MOSTI Science Fund: RM 312,748.
• 2012-2014 Co-I for "Unsupervised real time prediction of faults using an efficient strategy for QP in the support vector machine to implement incremental training for efficient classification and regression" project from MOSTI Science Fund: RM 175,000.
She would welcome any discussions on the general areas that have outlined in the "Current Research" interests.