Dr Debora Correa University of Western Australia

Dr Debora Correa

Chief Investigator

Theme 2


Dr Débora Corrêa is an enthusiast of computer science and data science; she has worked in data science on time series analysis, machine learning, computer vision, neural networks and complex networks. Débora is motivated by application-driven problems where the interplay between theory and application enriches both and results in a proper data analysis that contributes to the real world. Débora has explored solutions in bioengineering and biomedical signals, music, audio signals and diverse engineering systems. In addition, her research allows her to work and build collaborations with many researchers across different fields, including the opportunity to supervise postdoctoral, undergraduate, master and PhD students.

Débora has worked as a Research Fellow at the ARC ITTC Transforming Maintenance Through Data Science (CTMTDS) from 2019-to 2021. In this position, she developed research in data science projects to solve industry problems. She cooperated with industry peers and stakeholders to improve the way maintenance is performed using data analytics. Débora has supervised PhD students in their industry placements, guiding them for deliverables that add value to the business and taking part in talking to the industry partners. Débora has developed and is now teaching machine learning units at the professional programs promoted by CORE Skills.  

In 2022, Débora commenced an appointment at the UWA Computer Science Department and has continued supervising and mentoring students of the ARC ITTC CTMTDS as a Chief Investigator.  
Debora is currently developing mathematical tools to analyse large-scale temporal data consisting of many variables, mixed data types and resolution. She wants Australian companies to benefit from recent advances and accessibility of sensor technologies and instrumentation by effectively and efficiently extracting meaningful information from this massive data to guide strategic business decisions. Debora will develop solutions for open problems in industrial asset condition-based monitoring.

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