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Bijan Raahemi

Raahemi, Bijan
Full Professor
Director of Digital Transformation and Innovation (DTI) Graduate Program
B.Sc. (Hon.) (Isfahan University of Technology (IUT)), M.Sc. (Sharif University of Technology), Ph.D. (Waterloo)
DMS 6143
613-562-5800 x 4859
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Professor Raahemi received his Ph.D. in Electrical and Computer Engineering from the University of Waterloo, Canada, in 1997. Prior to joining the Telfer School of Management as a Professor in Management Information Systems and Technologies, Dr. Raahemi held several senior research positions in the telecommunications industry, including Nortel and Alcatel, focusing on computer networks architectures and services, dynamics of internet traffic, systems modelling and performance analysis.

Professor Raahemi's work has appeared in more than 85 peer-reviewed journals and conferences papers. He also holds eight patents in data communications. He is co-editor of the book “Handbook of Research on Data Science for Effective Healthcare Practice and Administration”. Dr. Raahemi received the Research Excellence Award, Established Researcher, Telfer School of Management in 2018. Professor Raahemi’s research are funded by grants from NSERC, MITACS, OCI, NRC, and CFI. His current research interests include artificial Intelligence, machine learning, data mining, big data analytics, their applications in business, and engineering, Information Systems and Technologies, and Data Communications Networks and Services. Dr. Raahemi established the Knowledge Discovery and Data mining (KDD) Research Laboratory compromising graduate students and researchers from multidisciplinary areas of Digital Transformation and Innovation, Systems Science, Computer Science and Engineering. The research projects in the KDD lab focus on two main streams: (a) novel techniques of data mining and machine learning with their emerging applications in business, healthcare, and engineering; and (b) Information systems and technologies, data communications networks and services.

Professor Raahemi is a registered member of Professional Engineers of Ontario (PEO), a senior member of the Institute of Electrical and Electronics Engineering (IEEE), and a member of the Association for Computing Machinery (ACM). He is also a member of Ottawa-Carleton Institute for Electrical and Computer Engineering (OCIECE), and Ottawa-Carleton Institute for Computer Science (OCICS).

Publications during the last 7 years

Papers in Refereed Journals

  • Motie, S. and Raahemi, B. 2024. Financial fraud detection using graph neural networks: A systematic review. Expert Systems with Applications, 240(April 2024): 122156.
  • Ashtiani, M.N. and Raahemi, B. 2023. News-Based Intelligent Prediction of Financial Markets Using Text Mining and Machine Learning: A Systematic Literature Review. Expert Systems with Applications, 217.
  • Pham, T. and Raahemi, B. 2023. Bio-Inspired Feature Selection Algorithms With Their Applications: A Systematic Literature Review. IEEE Access, 11: 43733-43758.
  • Raahemi, B. and Obidallah, W.J. 2023. Special Issue in Blockchain Applications in Data Management and Governance. Data.
  • Obidallah, W.J., Raahemi, B. and Rashideh, W. 2022. Multi-Layer Web Services Discovery Using Word Embedding and Clustering Techniques. Data, 7(5): 57.
  • Riahi-Madvar, M., Akbari-Azirani, A., Nasersharif, B. and Raahemi, B. 2022. Subspace-based outlier detection using linear programming and heuristic techniques. Expert Systems with Applications, 207.
  • Pishgoo, B., Akbari-Azirani, A. and Raahemi, B. 2022. A Dynamic Feature Selection and Intelligent Model Serving for Hybrid Batch-Stream Processing. Knowledge-Based Systems Journal, 256.
  • Riahi, M., Akbari, A., Nasersharif, B. and Raahemi, B. 2021. A new density-based subspace selection method using mutual information for high dimensional outlier detection. Knowledge-Based Systems Journal, 216, (In Press).
  • Ashtiani, M.N. and Raahemi, B. 2021. Intelligent Fraud Detection in Financial Statements using Machine Learning and Data Mining: A Systematic Literature Review. IEEE Access.
  • Obidallah, W.J., Ruhi, U. and Raahemi, B. 2020. Clustering and Association Rules for Web Service Discovery and Recommendation: A Systematic Literature Review. SN Computer Science, 1(27): 1-33.
  • Pishgoo, B., Akbari, A. and Raahemi, B. 2020. A Hybrid Distributed Batch-Stream Processing Approach for Anomaly Detection. Information Sciences, 543: 309-327.
  • Bigdeli, E., Mohammadi, M., Raahemi, B. and Matwin, S. 2018. Incremental anomaly detection using two-layer cluster-based structure. Information Sciences, 429(2018): 315–331.
  • Cheraghchi, F., Abualhaol, I., Falcon, R., Abielmona, R. and Raahemi, B. 2018. Modeling the Speed-based Vessel Schedule Recovery Problem using Evolutionary Multiobjective Optimization. Information Sciences.
  • Muhammed, M.T. , Obidallah, W.J. and Raahemi, B. 2018. Applying Deep Learning Techniques for Big Data Analytics: A Systematic Literature Review. Archives of Information Science and Technology, 1(1): 20-41.
  • Dutta, I., Dutta, S. and Raahemi, B. 2017. Detecting Financial Restatements using Data Mining Techniques. Expert Systems with Applications, 90(C): 374-393.
  • Obidallah, W.J. and Raahemi, B. 2017. Managing Changes in Service Oriented Virtual Organizations: A Structural and Procedural Framework to Facilitate the Process of Change. Journal of Electronic Commerce in Organizations, 15(1): 59-83.


  • Akhond Zadeh Noughabi, E., Raahemi, B., Albadwi, M. and Far, B. Handbook of Research on Data Science for Effective Healthcare Practice and Administration. Pennsylvania, USA: IGI Global Publishers, 2017.

Chapters in Books

  • Kamali, A., Richards, G., Raahemi, B. and Danesh, M. A Framework and Architecture for Performance Management in Virtual Organizations. In Shah J. M. and William Y.. Applying Business Intelligence Initiatives in Healthcare and Organizational Settings. USA: IGI Global, 2018.
  • Tekieh, M., Raahemi, B. and Benchimol, E. Leveraging Applications of Data Mining in Healthcare using Big Data Analytics: An Overview. In Akhond Zadeh Noughabi, E.. Handbook of Research on Data Science for Effective Healthcare Practice and Administration. Pennsylvania, USA: IGI Global Publishers, 2017.
  • Nasiri, S. and Raahemi, B. Non-Parametric Statistical Analysis of Rare Events in Healthcare: Case of Histological Outcome of Kidney Transplantation. In Akhond Zadeh Noughabi, E.. Handbook of Research on Data Science for Effective Healthcare Practice and Administration. Pennsylvania, USA: IGI Global Publishers, 2017.

Funded Research during the last 7 years

Funded Research during the last 7 years
From-To Source Title * ** Role Amount
2023-2028 NSERC - Discovery Grants Program Reliable AI for Outlier Detection in Unstructured Data with Applications in Engineering and Business R C PI $ 250,000
2023-2026 SSHRC Partnership Development Grant Enabling Systems Transitions Towards Inclusive Innovation R C Co-I $ 198,793
2020-2022 OCE and Ciena Smart Factory R O PI $ 150,000
2018-2019 MITACS & Bell Canada Network Traffic Classification for Cyber Threat and Malware Detection U C PI $ 50,000
2017-2023 NSERC - Discovery Grants Program Outlier Detection in High-Dimensional Big Data using Bio-Inspired Methods for Emerging Applications in Engineering, Healthcare, and Business R C PI $ 120,000
2017-2018 OCE, NSERC, NRC, SMAT Technologies Estimating Bus Passengers' Origin Destination Travel Route using Data Analytics on Wi-Fi and Bluetooth Signals R U PI $ 65,000
2017-2018 MITACS & Bell Canada Network Traffic Classification for Cyber Threat and Malware Detection R C PI $ 50,000
2016-2019 MITACS and ICES Mining of population-based routinely collected health data to determine risk factors associated with pediatric morbidity in Ontario. R O PI $ 90,000
2012-2017 NSERC - Discovery Grants Program Feature Engineering using Bio-Inspired Methods for the Internet Data Analytics R C PI $ 105,000


C: Contract (R and D) | E: Equipment Grant | R: Research Grant | S: Support Award | P: Pedagogical Grant | O: Other, U: Unknown

C: Granting Councils | G: Government | F: Foundations | I: UO Internal Funding | O: Other | U: Unknown

PI = Principal Investigator | Co-I = Co-Investigator | Co-PI = Co-Principal Investigator

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