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Benjamin Tan

Ph.D., BE(Hons), P.Eng.
Pronouns: He/Him


Assistant Professor

Schulich School of Engineering, Department of Electrical and Software Engineering

Contact information


Office: ICT446

Preferred method of communication

Please feel free to reach me via email


Educational Background

Ph.D. Computer Systems Engineering, University of Auckland,


Dr. Benjamin Tan (he/him/his) is an Assistant Professor at the University of Calgary, Alberta, Canada. He leads the Calgary Intelligent Secure Hardware Research Group (CalgaryISH). Before joining the University of Calgary, Dr. Tan was a Research Assistant Professor at New York University with the Center for Cybersecurity.  His recent research efforts include projects in collaboration with Intel, and his work has been funded by the Natural Sciences and Engineering Research Council of Canada (NSERC), the NSF (USA), DARPA (USA), Alberta Innovates, and the Semiconductor Research Corporation (SRC). He earned his Ph.D. at the University of Auckland, New Zealand. At the University of Auckland, he worked as a Professional Teaching Fellow and received a Student’s Choice Top Teacher Award (Top 15 in the Faculty). Dr. Tan was awarded the Schulich School of Engineering Research Excellence and Undergraduate Training awards in 2023.

His current research focuses on improving the security of computer systems at the hardware level up. This also includes understanding the implications of emerging deep learning techniques, including Large Language Models (LLMs) on the digital system development flow and supply chain.

He has served as a coordinator and adviser for competitions at CSAW (the most comprehensive student-run cybersecurity event in the world). His work has received a distinguished paper award (IEEE S&P ‘22) and an outstanding paper award (IEEE TALE ‘18). Dr. Tan is a Professional Member of the Association of Professional Engineers and Geoscientists of Alberta (APEGA). He is a member of IEEE and ACM. 


Areas of Research

Improving SoC Security

The general trend for computing systems these days is for increased integration: add more cores and more software/firmware into a system-on-chip (SoC)!

While the SoC approach provides new ways for achieving application-specific requirements through customization, the use of 3rd party IPs and increasing overall complexity can lead to potential security threats. In this line of work, I am broadly interested in coming up with new design flows and architectures that improve security. Naturally, nothing is free -- so working out how to specify security objectives and achieve them while also satisfying other requirements is the name of the game.

Hardware Security + Machine Learning

Hardware lies at the foundation of all computing systems -- processors, accelerators, memories -- securing hardware from attackers is paramount.

There are several problems in hardware security, including detecting hardware Trojans, Intellectual Property (IP) protection (e.g., reverse engineering), and side-channel attacks. How will the increasing capabilities of AI/ML affect hardware security? Increasing predictive capability can help with challenges like Trojan detection or malware classification. However, there is an opportunity for AI/ML to devise new strategies for attack and defense. In this line of work, I'm interested in seeing how we can formulate hardware security problems so that AI agents can start exploring the design space. 

Large Language Models + Security

LLMs are poised to change how we work and play... however, are they robust and safe? 

In my research in this area, I am interested in examining how this emerging technology can enhance cybersecurity and understanding what potential security risks are involved in using such a technology. 


Course number Course title Semester
ENSF 460 Embedded Software and Hardware Systems Fall 2023
ENCM 511 Embedded System Interfacing Fall 2022, Fall 2023
ENCM 515 Application Specific Processors and Accelerators Winter 2024
ENCM 515 Digital Signal Processors Winter 2022, 2023