Summary
Shamim Towhid is an AI researcher and engineer with nine years of experience bridging academia and industry, currently focused on applying reinforcement learning to automate cybersecurity tasks at the National Research Council Canada. Based in Regina, he combines a PhD trajectory in Computer Science with hands-on roles as a graduate teaching assistant and technical lead of the University of Regina Cybersecurity Club, mentoring students and maintaining secure club infrastructure. His background includes applied AI and computer vision work in industry, Mitacs-funded research on ML-driven 5G fault management, and a Udacity nanodegree in deep reinforcement learning, reflecting a strong applied research-to-production mindset. Shamim’s teaching and sessional instructor roles highlight clear communication skills—he prepares course materials, exams, and labs for topics from Python fundamentals to networks and data structures. Pragmatic and code-focused (he quotes Cory House on readable code), he excels at turning complex ML research into reproducible tools and student-ready curriculum. Notably, his career blends real-world cybersecurity projects with a proven ability to translate academic research into operational systems.
9 years of coding experience
4 years of employment as a software developer
Nanodegree Deep Reinforcement Learning , Nanodegree Deep Reinforcement Learning at Udacity
Bachelor of Science (BS) Computer Science and Engineering, Bachelor of Science (BS) Computer Science and Engineering at Ahsanullah University of Science and Technology
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at University of Regina
Higher Secondary School Certificate, Higher Secondary School Certificate at Govt. K.C college
Bengali, English, Japanese