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Apurva Narayan obtained his PhD from the Department of Systems Design Engineering at the University of Waterloo and his Bachelor’s degree in Electrical Engineering from Dayalbagh Educational Institute in 2015 and 2008 respectively. His PhD thesis focused on a holistic systems approach to modeling and designing engineering systems under uncertainty. Narayan was an NSERC post-doctoral fellow in the Real-Time Embedded Systems Group in the Department of Electrical and Computer Engineering at the University of Waterloo. His research investigates artificial intelligence and machine learning with an emphasis on explainable AI, quantum machine learning, data mining, data analytics, safety and security in cyber-physical systems, software engineering, and graph-theoretic analysis of complex systems. Narayan has authored or co-authored over 20 peer-reviewed publications in top-tier ACM and IEEE conferences and journals. Currently, his research is centered on developing models for understanding and ensuring the safety and security of complex cyber-physical systems, with a strong interest in interpretable and explainable machine learning models used for applications such as anomaly detection and cyber-physical system security.
University of British Columbia • Kelowna, Canada
Teaching and supervising graduate students in machine learning and data analytics.
Offers course-only and thesis routes. Focus areas include philosophy of science, mind, ethics, and Asian philosophy.