Steven Ding

Steven Ding is an Adjunct Assistant Professor and Lab Director at the School of Computing, Queen's University, leading the L1NNA Artificial Intelligence and Security Lab. His research focuses on the intersection of machine learning, data mining, and cybersecurity with applications in malware analysis, reverse engineering, and authorship attribution.

• Education: PhD in Information Studies (McGill University, 2019), M.A.Sc. in Information Systems Security (Concordia University), B.S. in Information Systems (University of the Fraser Valley), B.S. in Computer Science (University of Shanghai for Science & Technology)

Research Interests: His work integrates artificial intelligence and security systems to develop solutions for malware phenotype decomposition, neural malware analysis, binary provenance, and explainable AI in cybersecurity. He explores data analytics for assembly code evolution and authorship analysis using machine learning and differential privacy.

Publication Trends: His peer-reviewed work (IEEE Transactions, ACM conferences) emphasizes binary code analysis, neural networks, and cybersecurity, with a focus on static malware characterization, authorship attribution, and cross-architecture code similarity.

Scientific Awards:

• FRQNT Doctoral Research Scholarship (2017-2018)
• Dean’s Graduate Award at McGill University (4 years, 2014-2018)
• NSERC Alliance Grant (2021-2024)
• Best Poster Award, SERENE-RISC Research Showcase (2016)
• Second Prize, Hex-Rays Software Plug-in Contest (2015)
• ACM SIGKDD Student Travel Award (2016)

Advising & Grants: Dr. Ding has supervised numerous Ph.D. and Master’s students (e.g., Li Tao Li, Mark M. Adams) and secured significant funding from BlackBerry Cylance, DRDC, and NSERC, including a 1.24M CAD NSERC Alliance Grant (50%) and 1.5M CAD DRDC Contract (50%).

Labs & Teams: The L1NNA lab collaborates with DRDC Canada, NVIDIA, and IEEE Security & Privacy, focusing on AI-driven security tools (e.g., Kam1n0 assembly clone search engine) and neural malware analysis. The lab is located in Goodwin Hall, Queen's University, and emphasizes practical software development and industrial partnerships.