Design & Analysis of Algorithms
Algorithm design strategies, asymptotic analysis, greedy methods, dynamic programming, graph algorithms, complexity and problem-solving.
TEACHING
Making Complex Computing Concepts Understandable
More than two decades of Computer Science teaching, mentorship and student-centred learning.
20+
Years of Teaching Experience
TEACHING PHILOSOPHY
Understanding Before Memorisation
My teaching philosophy is centred on making complex computing concepts understandable, practical and relevant.
I encourage students to move beyond memorisation by focusing on the reasoning behind algorithms, systems and computational methods.
I use a combination of conceptual explanation, worked examples, visual illustrations, algorithm tracing, coding exercises, project-based learning and research-oriented assignments.
My aim is to help students develop both theoretical understanding and practical problem-solving skills, while connecting classroom topics with real-world computing applications.
COURSES & TEACHING AREAS
Core and Emerging Areas of Computer Science
Data Structures
Arrays, linked structures, stacks, queues, trees, heaps, graphs and efficient data organisation.
Object-Oriented Programming Using Java
Classes, objects, inheritance, polymorphism, encapsulation, abstraction and practical object-oriented software development.
Operating Systems
Processes, threads, scheduling, memory management, file systems, concurrency, synchronisation and operating-system design concepts.
Boolean Algebra
Boolean expressions, logical operations, truth tables, simplification techniques and foundations of digital computation.
Compiler Design
Lexical analysis, parsing, syntax and semantic analysis, intermediate representation, optimisation and code generation.
Artificial Intelligence
Intelligent systems, search, reasoning, machine learning foundations and practical AI applications.
Cybersecurity
Security principles, threats, vulnerabilities, access control, secure systems and contemporary cybersecurity challenges.
Cryptography
Classical and modern cryptography, symmetric and asymmetric techniques, hashing, digital signatures and emerging cryptographic systems.
Privacy-Preserving Computing
Homomorphic Encryption, secure computation, federated approaches and privacy-preserving artificial intelligence.
TEACHING APPROACH & METHODOLOGY
Learning Through Explanation, Visualisation and Practice
Conceptual Clarity
Breaking difficult computing concepts into smaller, understandable steps before introducing complexity.
Algorithm Visualisation
Using diagrams, execution traces and visual explanations to demonstrate how algorithms work.
Hands-on Coding
Connecting concepts to implementation through practical coding exercises and laboratory work.
Problem Solving
Developing analytical thinking through worked examples, classroom exercises and structured problem-solving activities.
Project-Based Learning
Applying theoretical concepts to practical projects that encourage independent thinking and teamwork.
Research-Led Teaching
Introducing students to contemporary computing problems and connecting classroom learning with current research.
STUDENT MENTORSHIP
Supporting Independent Thinking and Research
Mentorship forms an important part of my academic role. I work with undergraduate students to help them develop confidence in problem solving, technical communication, research methodology and independent learning.
I particularly encourage students interested in Artificial Intelligence, Cybersecurity, Privacy-Preserving Computing, Homomorphic Encryption and Secure Systems to move from broad interests toward clearly defined research problems.
My mentoring approach emphasises gradual development: understanding the problem, reviewing related work, defining a research question, selecting an appropriate method, implementation, evaluation and clear communication of results.
PROJECT-BASED LEARNING
From Theory to Implementation
I use project-based learning to help students connect theoretical knowledge with implementation and experimentation. Projects are designed to encourage students to apply course concepts to practical scenarios rather than treating laboratory activities as isolated programming exercises.
Depending on the course, activities may involve algorithm implementation and performance analysis, software design, data-structure experimentation, operating-system concepts, security problems, AI applications and research-oriented prototypes.
01
Problem Solving
02
Technical Independence
03
Teamwork
04
Documentation
05
Analytical Thinking
LEARNING RESOURCES
Supporting Learning Beyond the Classroom
I develop and curate teaching materials designed to make difficult Computer Science topics easier to understand.
01
Lecture Notes
Structured notes and explanatory materials for core Computer Science topics.
02
Lab Exercises
Practical exercises supporting implementation, experimentation and skills development.
03
Algorithm Visualisations
Visual demonstrations and illustrations designed to make algorithmic processes easier to follow.
04
Project Guidelines
Structured guidance for course projects, research activities and project-based learning.
TEACHING & MENTORSHIP
Learn. Explore. Build. Research.
My goal is to help students understand computing deeply, apply it confidently and develop the independence required for professional and research-oriented problem solving.