Teaching

Fostering the next generation of computer scientists through innovative teaching methods, interactive learning tools, and personalized mentorship in AI and software engineering.

Teaching Philosophy

My teaching philosophy centers on the belief that every student has unique potential that can be unlocked through personalized learning approaches, hands-on experience, and continuous encouragement.

"Be yourself, respect your abilities, and work hard" - This message to my students reflects my commitment to fostering individual growth while maintaining high academic standards.

I integrate cutting-edge research into my teaching, ensuring students are exposed to the latest developments in AI, machine learning, and software engineering while building strong foundational knowledge.

Interactive automata theory teaching

Courses Taught

Comprehensive curriculum spanning theoretical computer science to practical software development

Automata and Formal Languages

Fundamental course covering finite automata, regular expressions, context-free grammars, and Turing machines. Emphasis on theoretical foundations and practical applications.

  • • Finite State Machines and Regular Languages
  • • Context-Free Grammars and Pushdown Automata
  • • Turing Machines and Computability
  • • Interactive automata simulators and visual tools

Java Programming

Comprehensive introduction to object-oriented programming using Java, covering basic concepts to advanced topics including data structures and algorithm design.

  • • Object-Oriented Programming Principles
  • • Data Structures and Collections Framework
  • • Exception Handling and File I/O
  • • GUI Development and Event Handling

Information Theory

Mathematical foundations of information theory, entropy, coding theory, and their applications in data compression and communication systems.

  • • Entropy and Information Measures
  • • Source Coding and Data Compression
  • • Channel Coding and Error Correction
  • • Interactive learning tools and simulations

iOS Programming

Mobile application development for iOS devices using Swift, covering UI design, data persistence, and integration with device hardware features.

  • • Swift Programming Language
  • • iOS UI Development with SwiftUI
  • • Core Data and Persistence
  • • Device Integration and ARKit

Machine Learning and Applications

Fundamental course covering core machine learning concepts, algorithms, and real-world applications. Emphasis on theoretical foundations, data-driven modeling, and practical implementation of learning systems.

  • • Supervised and Unsupervised Learning Methods
  • • Classification, Regression, and Clustering Algorithms
  • • Model Evaluation, Optimization, and Generalization
  • • Practical applications using real-world datasets and tools

Language Processing Systems

Fundamental course covering the principles and techniques of processing natural and formal languages. Emphasis on linguistic modeling, algorithmic analysis, and practical applications of language processing systems.

  • • Lexical Analysis and Text Processing
  • • Syntax, Parsing, and Grammar-Based Models
  • • Statistical and Machine Learning Approaches to Language Processing
  • • Practical language processing applications and tools

Computational Models

Advanced study of computational models including term rewriting systems, lambda calculus, and their applications in programming language semantics and software verification.

  • • Term Rewriting Systems
  • • Lambda Calculus and Functional Programming
  • • Program Verification and Model Checking
  • • Advanced Automata Theory

Formal Languages and Compilers

Theory and practice of compiler design, lexical analysis, parsing, semantic analysis, and code generation with emphasis on modern compiler construction techniques.

  • • Lexical Analysis and Parsing
  • • Semantic Analysis and Type Checking
  • • Intermediate Code Generation
  • • Optimization Techniques

Theory of Automata and Languages

Advanced theoretical treatment of automata theory, formal language theory, and computational complexity with connections to modern applications in AI and software engineering.

  • • Advanced Automata Theory
  • • Formal Language Theory
  • • Computational Complexity
  • • Applications in AI and Verification

Educational Innovation

Developing cutting-edge educational tools and methodologies to enhance computer science learning

Interactive Learning Tools

Developed virtual environments and simulators for teaching complex computational concepts including automata theory and information theory.

  • • Turing Machine Simulators
  • • Automata Visualization Tools
  • • Interactive Coding Environments
  • • Real-time Assessment Systems

Adaptive Learning Systems

Research in personalized learning approaches that adapt to individual student needs, learning styles, and progress patterns.

  • • Learning Style Assessment
  • • Personalized Content Delivery
  • • Intelligent Tutoring Systems
  • • Performance Analytics

Mobile Learning

Development of mobile applications and multimedia learning systems that enable ubiquitous access to educational content.

  • • iOS/Android Learning Apps
  • • Multimedia Content Creation
  • • Offline Learning Capabilities
  • • Cross-platform Integration

Student Outcomes

Measuring the success of teaching methods through student achievements and career progression

95%
Average Pass Rate
4.5/5
Average Student Rating
80%
Average Industry Placement