Wasswa Shafik - Teaching Experience

Teaching Experience

My teaching portfolio encompasses undergraduate and postgraduate instruction in cybersecurity, artificial intelligence, intelligent systems, computer engineering, data science, and applied mathematics. Across diverse academic environments, I have designed and delivered research-informed courses that integrate theoretical rigor with practical problem-solving, fostering analytical thinking, innovation, and interdisciplinary competence. My teaching philosophy emphasizes experiential learning, critical inquiry, and the application of emerging digital technologies to address real-world scientific and societal challenges.

Universiti Brunei Darussalam

Introduction to Cybersecurity (Elective | 2025)

This course provided a comprehensive introduction to the principles and practices of modern cybersecurity, enabling students to understand the evolving cyber threat landscape, security governance, cryptographic foundations, network defence, and digital risk management. Through theoretical exploration and practical case studies, students developed the analytical skills required to evaluate security vulnerabilities and design resilient solutions for protecting critical information infrastructures.

Physical Computing for Intelligent Systems (Advanced | 2024)

This advanced course examined the convergence of embedded computing, intelligent sensing technologies, and cyber-physical systems to support next-generation smart environments. Students explored the design, implementation, and optimization of intelligent physical systems by integrating hardware, software, and Internet of Things (IoT) technologies, while addressing challenges related to automation, real-time decision-making, and system reliability.

Mathematics for Digital Science (Advanced | 2023)

This course established the mathematical foundations underpinning modern digital technologies, artificial intelligence, and computational science. Students developed advanced analytical competencies in linear algebra, probability theory, optimization, and mathematical modelling, enabling them to formulate, analyse, and solve complex computational problems encountered in data-intensive scientific and engineering applications.

Yazd University

Mathematics for Computer Engineering (Advanced | 2021)

This course strengthened the mathematical competencies essential for advanced computer engineering by integrating discrete mathematics, linear algebra, probability, and computational analysis. Emphasis was placed on developing rigorous analytical reasoning and mathematical abstraction to support algorithm design, software engineering, artificial intelligence, and complex computational systems.

Cyber-Physical System Security (Elective | 2020)

This course investigated the security challenges associated with cyber-physical systems, industrial control systems, and interconnected IoT infrastructures. Students critically examined emerging attack vectors, threat modelling methodologies, secure system architectures, and resilience strategies, gaining an in-depth understanding of protecting mission-critical infrastructures in increasingly connected digital ecosystems.

Security Topics in Soft Computing (Elective | 2019)

This interdisciplinary course explored the application of computational intelligence techniques—including neural networks, fuzzy logic, evolutionary computation, and hybrid intelligent systems—to contemporary cybersecurity problems. Students evaluated intelligent approaches for intrusion detection, anomaly detection, malware analysis, and adaptive security, highlighting the transformative role of artificial intelligence in cyber defence.

Internet Security (Advanced | 2019)

This course provided an advanced examination of Internet security technologies, secure communication protocols, cryptographic systems, authentication mechanisms, and network defence strategies. Students developed the capability to analyse sophisticated cyber attacks, assess system vulnerabilities, and design secure communication architectures aligned with modern cybersecurity standards and best practices.

Data Mining and Machine Learning (Elective | 2018)

This course introduced advanced techniques for discovering meaningful patterns and predictive insights from complex datasets through data mining and machine learning. Students investigated supervised and unsupervised learning methodologies, feature engineering, model evaluation, and intelligent decision-making, while applying computational algorithms to solve practical challenges across diverse scientific and industrial domains.

Research Methodology and Analysis (Advanced | 2018)

This course cultivated advanced research competencies by guiding students through the complete scientific research lifecycle, from problem identification and methodological design to statistical analysis, scholarly communication, and research dissemination. Particular emphasis was placed on research integrity, critical evaluation of scientific literature, reproducible research practices, and the development of high-quality publications capable of contributing to international scientific discourse.