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Dr. Catherine Inibhunu

 

PhD (Comp Sc)

M.Sc. (Comp Sc) Data Mining

B.Sc.(H) (Comp Sc) Major Software Engineering, Minor Mathematics

MIEEE, MACM

 
Post-Doctoral Research Fellow
Health Informatics Research Lab
Adjunct Professor
Faculty of Business and Information Technology

 

 

Dr. Catherine Inibhunu

Dr Catherine Inibhunu is a software engineer, data scientist, machine learning research scientist and instructor with industry and academic carrier record producing research and analytics critical for making decision in various industries, establishing clear analytical road maps for operational efficiency, pinpointing operational gaps, utilizing best practices in stream and cloud computing and analytics. She has led multiple analytics teams to support vast projects on stream computing, management and analysis of complex data using methods in data mining, machine learning and artificial intelligence.

She’s currently a Post-Doctoral Research Fellow in Dr. Carolyn McGregor’s Health Informatics Research Lab at Ontario Tech University engaged in multi-disciplinary research projects geared at application of stream computing, edge and cloud computing, big data analytics, artificial intelligence, machine learning, internet of things, temporal pattern mining, data management and health informatics.

 

Research topics

Temporal Pattern Mining | Stream Computing | Health Informatics | Cloud and Edge Computing | Internet of Things | Intelligent Decision Support Systems | Smart Cities | Cyber Security and Data Privacy | Responsible AI | Machine Learning | Deep Learning | Women in Engineering/Computing/Machine Learning

 

 

Background

Dr. Inibhunu received her PhD in Computer Science from University of Ontario Institute of Technology (Ontario Tech University), Master’s degree in Computer science specializing in Data Mining and Honors B.Sc. Computer Science Specializing in Software Engineering and Minor in Mathematics from University of Windsor, Canada. She’s currently a Post-Doctoral Research Fellow in Dr. Carolyn McGregor’s Health Informatics Research Lab at Ontario Tech University engaged in multi-disciplinary research projects geared at application of stream computing, big data analytics, artificial intelligence, machine learning, internet of things, temporal pattern mining, data management and health informatics.

 

 

  • Published Research

    [1] Inibhunu, C. & McGregor, C. (2021) Privacy Preserving Framework for Big Data Management in Smart Buildings, accepted for presentation at IEEE PERCOM Workshop on Security, Privacy, and Trust in the Internet of Things (SPT-IoT) March 22 -26, 2021, Kassel, Germany

    [2] Inibhunu, C & McGregor, C. (2020), A Privacy Preserving Framework for Smart Cities utilising IoT, Smart Buildings and Big Data, accepted for presentation at IEEE International Conference on SmartCity Dec 12-14 2020.

    [3] Inibhunu, C & McGregor, C. (2020), Edge Computing with Big Data Cloud Architecture: A Case Study in Smart Building, accepted for presentation at the BigData Workshop on IoT based Big Data Architectures and Applications, Dec 10-13 2020.

    [4] Inibhunu, C (2020) A Method to Detect and Represent Temporal Patterns from Time Series Data and its Application for Analysis of Physiological Data Streams, PhD. Dissertation.

    [5] Inibhunu, C., & McGregor, C. (2020). Identification of Temporal Changes on Patients at Risk of LONS with TPRMine: A Case Study in NICU, 33rd IEEE International Symposium on Computer-Based Medical Systems. IEEE CBMS. Mayo Clinic, Rochester, Minnesota, USA.

    [6] Inibhunu, C., & McGregor, C. (2020). Application of TPRMine method for Identification of Temporal Changes on Patients with COPD: A Case Study in Telehealth, 33rd IEEE International Symposium on Computer-Based Medical Systems. IEEE CBMS. Mayo Clinic, Rochester, Minnesota, USA.

    [7] Inibhunu, C., Jalali, R., Doyle, I., Gates, A., Madill, J., & McGregor, C. (2019). Adaptive API for Real-Time Streaming Analytics as a Service. 41st EMB. Berlin: IEEE.

    [8] Inibhunu, C., & McGregor, C. (2018). Fusing Dimension Reduction and Classification for Mining Interesting Frequent Patterns in Patients Data. Machine Learning and Data Mining in Pattern Recognition. MLDM (pp. 1-15). New York: Lecture Notes in Computer Science, vol. 10935. Springer, Cham. {Premier Machine Learning Conference, 33% Acceptance Rate}

    [9] Inibhunu, C., & McGregor, C. (2018). State Based Hidden Markov Models for Temporal Pattern Discovery in Critical Care. Life Sciences Conference (LSC) (pp. 77-80). Montreal, Canada: IEEE.