Research Groups

The Establishment of the Cybersecurity and Digital Resilience (CDR) Lab

The Jodrey School of Computer Science is pleased to announce the formation of the Cybersecurity and Digital Resilience Lab (CDR Lab), an emerging research entity within the Acadia AIoT Research Hub (AARH). The CDR Lab will be led principally by Dr. Amir Eaman, with support from Dr. Esteve Hassan, and will focus on advancing research and solutions in cybersecurity and digital resilience, addressing critical challenges in today’s digitally connected world.

This new initiative represents a significant step in expanding the School’s research capacity and promoting novel development in secure and resilient computing systems, where both graduate and undergraduate students will be actively engaged in research and training opportunities.

 

Lifelong Machine Learning and Reasoning

The Machine Learning Research Group (MLRG) undertakes research into novel machine learning and data mining algorithms and approaches, and the application of these approaches to synthetic and real-world problems. The lab’s researchers and students specialize in developing machine learning algorithms and methods particularly those in the area of Lifelong Machine Learning, Transfer Learning, Knowledge Consolidation, and Learning to Reason. The group has particular expertise in artificial neural networks and deep learning used for supervised, unsupervised and time series problems. We also apply standard machine learning methods as well as more advanced LMLR approaches to problems in the areas of data mining, adaptive systems, intelligent agents and robotics. For more information please see the Software, Data and Publication pages.

 

CILS Computational Intelligence and Learning Systems

CILS research group is dedicated to advancing cutting-edge research in artificial intelligence, with a focus on developing adaptive systems that enhance decision-making across complex environments. At CILS, we specialize in integrating machine learning, deep learning, and neuro-symbolic approaches to tackle challenges including early disease detection and privacy-preserving AI. Our team is committed to creating equitable, robust, and interpretable AI systems, pushing the boundaries of computational intelligence to meet real-world needs and benefit society.