Muhammad Waseem Lecturer Computer Science
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I am Dr. Muhammad Waseem, an experienced Computer Science Lecturer, AI/ML Researcher, and PhD Scholar with more than 12+ years of teaching, research, and industry experience. I have consistently delivered 100% student results since 2012, reflecting my strong command over concepts and student-centric teaching approach.

I completed my BS (3.70 CGPA) and MS in Computer Science (3.57 CGPA) and am currently pursuing a PhD in Computer Science with a focus on Generative AI, Large Language Models (LLMs), Diffusion Models, and Multimodal AI at UET Lahore.

Alongside academics, I have hands-on industry experience at NADRA as a System Engineer, and extensive research exposure at KICS-UET in advanced AI systems. I am also a certified AI expert, continuously learning new programming languages and emerging technologies.

Teaching Philosophy

I strongly believe that:

Concept clarity is more important than rote learning

A teacher succeeds only when students truly understand

Sudden syllabus changes are challenges, not obstacles

Motivation and confidence can transform average students into top performers

I actively encourage students to think ahead, ask questions, and apply concepts practically.

Subjects I Teach
Programming & Core CS

Python

C, C++, Java, C#, VB

JavaScript, PHP

HTML, CSS

Object Oriented Programming (OOP)

Computer Science Fundamentals

Data Structures & Algorithms

Database Systems

Operating Systems

Digital Logic Design

Information Technology

Advanced & Emerging Areas

Artificial Intelligence

Machine Learning

Deep Learning

Data Mining

Research Methodology

Subjects

  • Python Beginner-Expert

  • Machine Learning IGCSE-Doctorate/PhD

  • Assembly Language Beginner-Expert

  • C++ Expert

  • Deep Learning IGCSE-Doctorate/PhD

  • Data Structures and Algorithms in C Grade 12-Doctorate/PhD

  • PHP & MySQL Beginner-Expert

  • Artificial Intelligence Grade 11-Doctorate/PhD

  • Object-oriented programming Beginner-Expert

  • Database Management System Grade 11-Doctorate/PhD

  • Operating Systems Grade 11-Doctorate/PhD

  • Natural Language Processing Grade 5-Doctorate/PhD

  • Algorithm design and analysis Beginner-Expert

  • Object Oriented Programming and Design Beginner-Expert

  • Research and Publication Expert

  • AI tools Grade 1-Doctorate/PhD

  • GenAI Expert

Experience

  • Senior Reseacher (Oct, 2021–Present) at National Criminology Artificial intelligence Lab
    Reseach in AI in health, agriculture, Generative AI,
  • Lecturer (Jul, 2012–Sep, 2021) at Government of Punjab Pakistan
    Lecture Deliver
    Student assessment
    Project supervision
    In-charge online system
  • System Engineer (Jan, 2011–Jul, 2012) at NADRA,Khushab
    Public Dealing
    Inquiry expert
    Database Administrator
    Head quarter Coordination
    CNIC PRocessing
    Incharge Center
  • Research Scholar (Sep, 2009–Jan, 2011) at University of Sargodha, Sargodha
    I am working as a Visiting lecturer for the IT Department, Strategic Consulting, including business plan, IT Policies procedures and Network Infrastructure Design and Complete Systems Infrastructure administration and performing the following main tasks including the working projects during this time:
    • Work as Project coordinator for the students.

    Record level securities at multiple ERP users, Managing the SQL server 2008 multi Database environment taking the backups and Database logs shipping to the backup server

    • Working as a course coordinator for students Project

    Design and implement complete Database in SQL Server 2008.

    • Back up and recovery.

    • Work on Project Management system

    • Managing the connections with ISP

    •
    • Administration Oracle

    • Work on web development and designing
    • Database development monitoring

    • Configuration of servers
  • Lecturer (Feb, 2009–Present) at Punjab Higher Education Department
    Deliver engaging lectures and tutorials on topics such as programming, algorithms, machine learning, artificial intelligence, data science, and computer networks.
    Develop, review, and update course content, lesson plans, and study materials to ensure they meet academic standards and align with the latest advancements in Computer Science.
    Utilize innovative teaching methods, including project-based learning, to improve student understanding and engagement.
    Conduct high-quality research in Computer Science or a related field, publishing findings in peer-reviewed journals and conferences.
    Collaborate on research projects with colleagues or industry partners.
    Apply for research grants and funding opportunities to support academic work.

Education

  • PhD Computer Science (Aug, 2021–Dec, 2025) from university of Engineering and Technology, Lahore
  • MSCS (Nov, 2017–Mar, 2021) from Virtual University of Pakistan Lahore–scored 3.57/4.00
  • BSCS (Jul, 2005–Jun, 2009) from University of Sargodha, Sargodha–scored 3.70/4.00

Fee details

    Rs1,000–5,000/month (US$3.59–17.95/month)

    through bank account that will be provided on demand

Courses offered

  • GenAI

    • US$20
    • Duration: 4 Weeks
    • Delivery mode: Online
    • Group size: Individual
    • Instruction language: English
    • Certificate provided: No
    This course provides a deep dive into the evolving domain of Generative Artificial Intelligence (Generative AI), focusing on its foundational theories, cutting-edge techniques, and transformative applications. Generative AI enables machines to produce novel, human-like outputs across text, images, audio, and video, reshaping industries and research paradigms.

    The course covers state-of-the-art models and frameworks such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Transformers, Diffusion Models, Retrieval-Augmented Generation (RAG), and Multi-Agent RAG systems. It also addresses the integration of generative techniques with real-world data and their practical applications in solving complex, domain-specific problems.

    Through lectures, hands-on labs, and real-world projects, students will learn to design, implement, and evaluate generative AI systems while critically analyzing their ethical implications, limitations, and societal impact.

    Learning Objectives:
    By the end of this course, students will be able to:

    Grasp the theoretical foundations of generative AI and retrieval-augmented techniques.
    Develop and fine-tune models for various data modalities, including text, images, audio, and video.
    Implement Retrieval-Augmented Generation (RAG) pipelines to enhance model performance with external knowledge.
    Explore Multi-Agent RAG architectures to facilitate agent collaboration in decision-making and content generation tasks.
    Evaluate generative models based on metrics like quality, coherence, diversity, and real-world applicability.
    Address ethical challenges, including bias, accountability, and misuse in generative AI systems.
    Topics Covered:
    1. Introduction to Generative AI
    Overview and key concepts of generative AI
    Evolution of generative models and current trends
    Generative AI vs traditional AI approaches
    2. Core Generative Techniques
    Generative Adversarial Networks (GANs): Architecture, challenges, and advancements (e.g., StyleGAN, CycleGAN)
    Variational Autoencoders (VAEs): Latent representations and their applications
    Transformers: From BERT to GPT and large language models (LLMs)
    Diffusion Models: Foundations and applications in image, video, and audio generation
    3. Retrieval-Augmented Generation (RAG)
    Overview of RAG pipelines: Combining generative models with external retrieval systems
    Knowledge-grounded text generation and document synthesis
    Fine-tuning RAG systems for domain-specific tasks
    Integration with vector databases and external APIs
    4. Multi-Agent Generative AI Systems
    Introduction to Multi-Agent RAG: Enabling interaction between generative agents
    Architectures for collaborative agents in content generation and reasoning tasks
    Use cases in chatbots, virtual assistants, and multi-modal AI systems
    5. Applications of Generative AI
    Natural Language Processing: Text-to-text generation, summarization, dialogue systems
    Computer Vision: Image and video synthesis, style transfer
    Audio Processing: Speech synthesis, music generation, and audio restoration
    Multimodal Generative AI: Cross-domain systems like text-to-image and image-to-video generation
    6. Advanced Topics
    Alignment and tuning of large generative models
    Prompt engineering and few-shot learning techniques
    Scaling generative models for production-level applications
    7. Ethical and Societal Implications
    Risks of misinformation and bias in generative AI outputs
    Intellectual property, data privacy, and security concerns
    Frameworks for responsible and ethical AI development
    Course Structure:
    Lectures: Focus on foundational and advanced concepts with real-world case studies.
    Labs: Hands-on sessions for implementing and testing generative models, including RAG systems.
    Assignments: Practical tasks involving the integration of generative techniques with retrieval mechanisms.
    Final Project: Design and deploy a generative AI application using cutting-edge models and frameworks.

  • C#

    • US$20
    • Duration: 4 Weeks
    • Delivery mode: Online
    • Group size: Individual
    • Instruction language: English, Urdu
    • Certificate provided: No
    This course provides a comprehensive introduction to C# programming, a versatile and modern object-oriented language widely used for developing applications on the Microsoft .NET platform. Designed for beginners and intermediate learners, the course emphasizes foundational programming concepts, advanced features of C#, and practical application development.

    Students will gain hands-on experience in creating console applications, Windows-based applications, and basic web applications. By exploring the C# language features such as LINQ, async programming, and exception handling, learners will develop the skills to write efficient, maintainable, and scalable code. The course also introduces tools and frameworks like Visual Studio and .NET Core, equipping students for real-world software development.

    Learning Objectives:
    By the end of this course, students will be able to:

    Understand the syntax and structure of C# programming.
    Develop applications using object-oriented programming (OOP) principles.
    Implement error-handling, asynchronous programming, and LINQ queries.
    Build simple GUI applications using Windows Forms or WPF.
    Interact with files, databases, and APIs in C# applications.
    Apply best practices for debugging, testing, and code optimization.
    Topics Covered:
    1. Introduction to C# and .NET Framework
    Overview of C# language and its applications
    The .NET ecosystem: .NET Core vs .NET Framework
    Setting up the development environment (Visual Studio, Visual Studio Code)
    2. Basics of C# Programming
    Variables, data types, and operators
    Control flow: Conditionals, loops, and switch statements
    Functions and parameter passing
    3. Object-Oriented Programming (OOP) in C#
    Classes and objects
    Inheritance, polymorphism, encapsulation, and abstraction
    Interfaces and abstract classes
    4. Advanced C# Features
    Delegates and events
    Generics and collections
    LINQ (Language-Integrated Query) for data manipulation
    Asynchronous programming with async/await
    5. File and Data Handling
    Reading and writing files
    Exception handling and debugging
    Introduction to database access using Entity Framework
    6. User Interface Development
    Building desktop applications with Windows Forms and WPF
    Basics of XAML for UI design
    Handling user input and events
    7. Introduction to Web Development with C#
    Basics of ASP.NET Core for web application development
    Working with APIs and JSON in C#
    8. Best Practices and Real-World Application Development
    Writing clean and maintainable code
    Unit testing and debugging in Visual Studio
    Deploying applications using .NET tools
    Course Structure:
    Lectures: Detailed walkthroughs of concepts with examples.
    Labs: Hands-on coding exercises to reinforce learning.
    Assignments: Real-world tasks like building small applications.
    Final Project: Develop a C# application demonstrating learned concepts.

  • Natural Language Processing (NLP)

    • US$20
    • Duration: 4 Weeks
    • Delivery mode: Online
    • Group size: Individual
    • Instruction language: English, Urdu
    • Certificate provided: No
    This course provides a comprehensive introduction to Natural Language Processing (NLP), a core field of artificial intelligence that enables computers to understand, interpret, and generate human language. You will explore the foundational concepts of NLP, including text preprocessing, tokenization, and word embeddings, and progress to advanced topics such as transformer-based models, sentiment analysis, machine translation, and text generation.

    Through hands-on projects and real-world applications, you will learn how to build and evaluate NLP models using popular libraries like NLTK, spaCy, and Hugging Face. Whether you're a beginner or looking to deepen your knowledge, this course equips you with the skills needed to apply NLP techniques in diverse domains like chatbots, search engines, and sentiment analysis.

    What You’ll Learn:

    Fundamentals of text processing and feature extraction
    Language modeling and word embedding techniques (Word2Vec, GloVe)
    Advanced deep learning methods for NLP, including RNNs, LSTMs, and transformers
    Applications of state-of-the-art models like BERT, GPT, and T5
    Practical skills for building NLP systems using Python
    Who Should Take This Course:
    This course is designed for students, researchers, and professionals in data science, machine learning, or AI who want to master the concepts and applications of NLP. Basic programming knowledge in Python is recommended.

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