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Sia Jye Ying began her academic career at ºìÐÓÊÓƵ University in 2013, where she currently serves as a Senior Lecturer and the Head of the Department of Applied Statistics. In addition to her teaching and leadership responsibilities, she has been actively involved in enriching the student learning experience by organizing industry-relevant workshops, talks, and seminars. These initiatives have helped bridge academic learning with real-world application and foster strong industry-university connections. She holds a Bachelor’s degree in Applied Mathematics with Computing from Universiti Tunku Abdul Rahman (UTAR) and a Master of Applied Statistics from Universiti Putra Malaysia (UPM). To further support her development as an educator, she completed the Postgraduate Certificate in Academic Practice (International) from Lancaster University, UK, in 2017, reflecting her commitment to effective and research-informed teaching. Jye Ying is currently pursuing her PhD at UTAR, where her research focuses on two core areas, i.e The development and application of Physics-Informed Neural Networks (PINNs) for solving differential equations, particularly in inverse problems and sparse optimization, and The advancement of the Multiple Unreplicated Linear Functional Relationship (MpULFR) Model in the presence of multicollinearity, aiming to improve model interpretation and parameter estimation in complex statistical systems. Her work integrates modern machine learning methods with rigorous statistical modeling, positioning her research at the intersection of applied mathematics, data science, and computational intelligence. As an educator, she emphasizes active and critical learning, encouraging students to engage with concepts through inquiry, reflection, and hands-on application. Her teaching philosophy centers on developing both analytical skills and adaptive thinking—qualities essential for future data professionals. Jye Ying welcomes collaboration in interdisciplinary research related to functional data analysis, statistical computing, AI-enhanced modeling, and pedagogical innovation. Her combined expertise in applied statistics, machine learning, and academic practice makes her a valuable partner for joint research projects, curriculum development, and knowledge exchange initiatives.