Introduction:
I, Umar Aftab, a dedicated Lecturer in the Computer Science Department at NUTECH, with 6.5 years of academic experience and 2 years in the industry. My interest areas include Software Engineering, Data Mining, and Data Warehousing. Passionate about exploring new knowledge avenues, real-life problem solving, and industry collaboration, I am committed to providing students with a thorough understanding, problem identification skills, and diverse perspectives. My teaching philosophy focuses on equipping students with practical skills and knowledge to excel in their careers.
Experience
Lecturer, Department of Computer Science, NUTECH University (2024–Present)
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- Teach undergraduate and graduate courses in Parallel and Distributed Computing, Software Engineering, Formal Methods and Automata Theory, and Data Structures.
- Advise graduate students on research projects related to natural language processing, computer vision, Data Science, Software DevOps.
Lecturer, Department of Computer Science, HITEC University (2022–2024)
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- Teach undergraduate and graduate courses in Compiler Construction, Software Engineering, Microprocessor and Assembly Language, Introduction to Information and Communication Technologies, IT in Business, Operating Systems.
- Advise graduate students on research projects related to natural language processing, computer vision, Data Science, Software DevOps.
- Batch Advisor to CS 2022
Franchise Tutor, Liverpool John Moore’s University (B.Sc SE) (2021-2022)
Franchise Tutor, University of London (B.Sc CS) (2021-2022)
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- Taught introductory programming courses, including Python and Java, for undergraduate students.
- Developed new course materials and assignments to better integrate industry-relevant technologies into the curriculum.
Qualification
- Academia:
- Ph.D. in Computer Science, COMSATS Wah Campus (2023-In Progress)
- M.Phil in Computer Science, Quaid-i-Azam University, Islamabad (2019)
- BS in Computer Science, University of Engineering and Technology Taxila (C@SE Affilated) (2016)
I have published research in IEEE International Conference on Big Data with 50 citations. I also serve as a reviewer for prominent academic journals such as IEEE Access.
Taught Courses
I have taught a variety of undergraduate and graduate-level courses in the Computer Science Department. Below are the courses I have been involved in teaching:
- Introduction to Programming (CS101)
An introductory course focusing on programming concepts using Python, covering topics such as variables, control structures, functions, and basic data structures.
- Data Structures and Algorithms (CS210)
A core course that covers fundamental data structures (e.g., arrays, linked lists, trees, graphs) and algorithmic techniques (e.g., sorting, searching, recursion) with a focus on computational complexity.
- Parallel and Distributed Computing (CS4301)
A graduate-level course covering the parallel and distributed computing, including parallel and distributed architectures and systems, parallel and distributed programming paradigms, parallel algorithms, and scientific and other applications of parallel and distributed computing.
- Software Engineering (CS250)
A course focused on the theory with understanding principles, concepts, methods, and techniques of the software engineering approach to producing quality software.
- Theory of Automata (CS3101)
An course designed to introduce the basic concepts of finite state models. The students are familiarizing themselves with the FA and their equivalence. Moreover, the students are guided to design CFL and PDAs. The students are also familiar with Turing Machines.
Research
My research interests lie at the intersection of software engineering, requirement engineering, software DevOps, data mining, and data warehousing. I aim to explore innovative methodologies and tools that enhance the efficiency and effectiveness of software development processes. My work in requirement engineering focuses on developing robust frameworks for capturing, analyzing, and managing software requirements to ensure alignment with stakeholder needs and project goals.
Projects
- Smart Car Pooling App (TRAX) (2023–Present): Led the research, analysis and development of smart carpooling solution that will assists customers in gathering along for a similar destination. The app is currently in trials with promising results. Project won “Ignite National Technology Fund’’ under code NGIRI-2024-26621.
