DrDavid Hason Rudd

Dr David Hason Rudd is currently a casual academic at the School of Computer Science in the Faculty of Engineering and Information Technology (FEIT), UTS Business School, and  Faculty of Arts and Social Sciences (FASS) at the University of Technology Sydney (UTS), He obtained his Master of Science (Research) in Computing Science (C03025) and PhD in Analytics (C02029) from UTS.

His research portfolio is broad and impactful, encompassing areas such as causal machine learning, Industry 4.0 technologies, 5G networks and smart sensing. A significant focus of his work is on the innovative application of signal processing and deep learning to recognise emotional and mental states from speech data, providing insights into human behaviour and interaction. Moreover, his research focuses on advancing financial analytics and has developed a method called "Financial X-Ray". This innovative approach combines semi-supervised and deep learning techniques to predict customer financial literacy.
Additionally, his expertise extends to conducting causal analysis of customer churn, which aids financial institutions in understanding the cause of attrition. He actively collaborates with the Data Science & Machine Intelligence Lab (DSMI.tech) on industrial research projects. His work includes a study on customer churn for the Australian Dental Association, identifying key factors in member retention. He also developed drone-assisted AI-IoT techniques to enhance 5G indoor coverage predictions in 3D models. His dedication to the advancement of data science is reflected in his prolific contributions to esteemed journals and major conferences, making him a prominent figure in his field. He has published several papers in prestigious venues, including IEEE-sponsored conferences such as PAKDD, DSInS, AJCAI, BESC, KES etc.

In the industrial section, he is a qualified professional engineer, holding credentials from Engineers Australia (MID #8385590) and Industrial Engineers Australia (MID #4473294), along with a license as a registered Design Building Practitioner (DBP) class 3 in electrical design (License #0-10-530-01580). He has over 17 years of multidisciplinary engineering experience, spanning four countries and fields including electrical, telecommunications, engineering management, and data analytics. This broad experience enables him to connect with students from diverse academic backgrounds effectively, providing tailored guidance. 

David teaches a range of large undergraduate and postgraduate subjects in predictive and prescriptive business analytics, information systems, application implementation, and machine learning for data mining.