Sim Kuan GOH, PhD

Assistant Professor

Xiamen University Malaysia

About Me
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About Me

Sim Kuan Goh holds a B.Eng. and Ph.D. from the National University of Singapore. He is currently an Assistant Professor at Xiamen University Malaysia. Before joining Xiamen University, he served as a Research Fellow at the Air Traffic Management Research Institute at Nanyang Technological University. His research interests include computational intelligence, multi-modal AI, and brain-computer interfaces. He is an IEEE Senior Member and has shared his work in journals and conferences (e.g., NeurIPS, ACL, and others).

Find out more:

Translational Epilepsy Summer School 2024

IEEE STEER 2024 Conference

ACM AAMAS 2024 Workshop

UTM's ICBMEHS 2024 Conference

DSAIE: Speaker Series

XMUM AI Club

Research Projects

AI & NeuroTech

In this interdisciplinary research, we bring together insights from artificial intelligence and brain-computer interface to deepen our understanding of intelligence, cognition, and their associations with psychiatric disorders. We aspire to uncover valuable insights that could contribute to personalized treatment of neuro-diseases (e.g., epilepsy), and advance the development of more neuro-inspired artificial intelligence systems (e.g., multi-modal AI).



Decision Support Systems

This research focuses on the integration of advanced predictive and optimization models to address complex, multidisciplinary challenges. Core areas include the application of deep reinforcement learning to formulate climate policies that optimize environmental, economic, and social trade-offs, fostering evidence-based strategies for carbon emission reduction. Furthermore, the study develops efficient time-series forecasting models for solar energy prediction, enhances seizure prediction and epileptogenic zone localization to support surgical planning, and refines diagnostic methodologies for sleep disorders, such as sleep apnea, through comprehensive physiological data analysis.

Contact Me

For research and industrial collaboration, etc.

simkuangoh@gmail.com

FYP Projects

Please contact me if you are interested in the following projects.

3D Human Pose and Motion Analysis Using Computer Vision For Sport and Rehabilitation


keywords: 3D Human Pose Estimation, Motion Analysis, 3D Computer Vision, Rehab, Deep Learning, Multi-camera System, Virtual/Mixed Reality

Research collaboration between XMUM and Brandeis University, USA.


Decoding Brain Signals using Machine Learning for Clinical Diagnosis and Treatment of Epilepsy


keywords: Brain-Computer Interface, Machine/Deep Learning, Explainable AI (XAI), Epilepsy

Research collaboration between XMUM, Universiti Malaya (UM) and UM Medical Center.


Optimizing Deep Learning Training and Architecture Using Neuroevolution


keywords: Neuroevolution, Neural Architecture Search

Research collaboration between XMUM and Harbin Institute of Technology, China.


Optimal Visualization and Classification of Navigation Strategies using Machine Learning Method

ABSTRACT: As sentient beings, we are equipped with many innate abilities that cannot be replaced by AI thus far. One of such abilities is our ability to navigate our everyday environments. Whenever we carry out daily navigational activities, we rely on a host of decisions and strategies that we may or may not be fully conscious of. In this project, the focus is on measuring the strategies that pedestrians use to get to places that they are familiar with. These strategies fall within three core components of (i) spatial updating (ii) allocentric-survey, and (iii) procedural route (Zhong, 2013; Zhong & Kozhevnikov, 2016). The current study aims are to perform a Machine Learning-guided reanalysis of Zhong's (2013) Interfaculty Spatial Navigation Project Dataset containing survey responses from 500 participants (National University of Singapore, 2012-2013) using state-of-the-art Machine Learning-based Principal Component Analysis (ML PCA). The purpose of doing so follows a three-pronged approach: (i) To address inadequacies in data visualization, classification, and factor score computation in conventional, non-ML-based PCA, (ii) To create comprehensible data visualization, strategy classification, and strategy preference indices using ML PCA, and (iii) To enable speedy and accurate sorting of new self-reported navigation strategy ratings using the ML-PCA-trained algorithm without the need to collect more data from a large sample of participants. If you have skills and interests in Machine Learning, Python programming, psychology, neuroscience, geography, you are most welcomed to apply for this project.

Research collaboration between XMUM and Nanyang Technological University.