Hadis Karimipour, PhD, PEng, SMIEEE
Schulich School of Engineering, Department of Electrical and Software Engineering
Schulish Chair in Secure and Reliable Networked Engineering Systems
University of Calgary
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PhD, Universality of Alberta, 2016
Dr. Hadis Karimipour is the Director of the Smart Cyber-Physical (SCPS) Lab, an Associate Professor and Chair in Secure and Reliable Networked Engineering Systems in the Department of Electrical and Software Engineering at the University of Calgary. Her research mainly focuses on the application of Artificial Intelligent (AI) and machine learning on the Internet of Things (IoT) and critical infrastructure security.
Her research interest includes:
AI-enabled Monitoring Solution for Smart Cyber-Physical Grids
AI-enabled IoT/OT Security
Anomaly Detection in Critical Cyber-physical System
Machine Learning/Deep Learning
Areas of Research
Machine Learning (ML) is a subset of Artificial Intelligence (AI) that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. The basic premise of ML is to build algorithms that can receive input data and use statistical analysis to predict an output while updating outputs as new data becomes available. Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage are the main motivations toward machine learning developments. ML can be used for anomaly detection, system monitoring, security analysis, load prediction, load forecasting, etc. in cyber-physical systems.
The deployment of smart technologies in the communication layer brings new challenges for online monitoring and control of the Cyber-Physical Systems (CPS). In addition to the failure of physical infrastructure, CPSs are also sensitive to different anomalies on their communication layer. Examples of CPS include smart grid, autonomous transportation systems, medical monitoring, and autonomous vehicles. AI is a popular technology that has the potential to be leveraged in different aspects of CPS monitoring including anomaly/failure detection. AI/ML can extract patterns of suspicious or anomalous behaviour in the system to predict failure in advance.
Over the last decade, IoT platforms have been developed into a global giant that grabs every aspect of our daily lives. Because of easy accessibility and fast-growing demand for smart devices and networks, IoT is now facing more security challenges than ever before. There are lots of discussions about the role of AI/ML in security-aware design and analysis of IoT devices. AI-based methods can be used to identify various attacks at an early stage as well as providing defensive strategies. Moreover, AI seems to be promising in detecting new attacks using learning skills and handle them intelligently.
The traditional grid is not scalable enough to provide the world’s future energy requirements. Looking at the big picture, a nationwide effort to completely automate the grid is underway. A smart grid integrates a variety of distributed and renewable energy resources which are tightly coupled with IoT technology. These components provide a vast amount of data to support various applications in the smart grid, such as distributed energy management, generation forecasting, grid monitoring, fault detection, home energy management, etc. Considering the huge amount of data and complexity of the grid, AI techniques can be applied to automate and further improve the performance of the smart grid.
Participation in university strategic initiatives
- APEGA Early Accomplishment Award, Association of Professional Engineers and Geoscientists of Alberta (APEGA). 2022
- Canada Research Chair (Tier II), Government of Canada. 2021
- Canada's Top 20 Women in Cyber Security, IT World Canada. 2021
- MITACS Elevate Postdoctoral Fellowship, MITACS. 2016
- Queen Elizabeth II Graduate Scholarship, University of Alberta. 2015
- Queen Elizabeth II Graduate Scholarship, University of Alberta. 2014
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