Teaching

COMSATS University Islamabad (Assistant Professor) — Jan 2022–May 2023

Computer Programming (2022–2023)

• Course Summary: Introductory programming with problem-solving foundations and algorithmic thinking. Topics include variables, control flow, functions, arrays/lists, basic data structures, file I/O, and modular program design.
• Labs: Weekly coding labs (C/C++/Python) emphasizing testable, well-documented code and Git basics.
  PDF (Syllabus)

Artificial Intelligence (2022–2023)

• Course Summary: Fundamentals of AI including state-space search, informed heuristics, constraint satisfaction, knowledge representation, and an introduction to probabilistic reasoning. Applied mini-projects with real datasets.
• Assessment: Mix of quizzes, programming assignments, and a term project (reproducible report + demo).
  PDF (Syllabus)

Machine Learning (2022–2023)

• Course Summary: Supervised and unsupervised learning, feature engineering, model selection, cross-validation, and evaluation metrics (precision/recall/F1/AUC). Practical emphasis on Python (NumPy, Pandas, scikit-learn) and experiment tracking.
• Capstone: End-to-end ML project on a real-world dataset (reproducibility & ethical considerations).
  PDF (Syllabus)

Control Systems (2022–2023)

• Course Summary: Time- and frequency-domain analysis, stability (Routh–Hurwitz), root locus, Bode and Nyquist plots, and controller design (PID/state-space). MATLAB/Simulink across labs and assignments.
  PDF (Syllabus)

Laboratory Supervision:
Electronics Labs • Power Systems Labs • Computer Programming Labs

Student Supervision:
15+ undergraduate capstone teams and 8+ graduate research projects (smart grids, renewable integration, AI/ML applications).

Assessment & Accreditation:
Developed course assessments aligned with ABET student outcomes (problem-solving, design experience, ethics, and professional communication).

Edge Hill University & Lancaster University (Research Fellow / Curriculum Design) — Jun 2023–Jul 2024

• Curriculum design: Co-developed material on AI applications in electrical engineering and digital forensics (syllabus outlines, lecture packs, lab activities).
• Topics integrated: ML pipelines, anomaly detection for cyber-physical systems, secure data handling, and reproducible research practices.
• Teaching contributions: Short seminars/guest sessions on AI-driven security in smart grids and next-gen wireless networks.
  PDF (Outline)

Additional Teaching Activities

Mentoring & Supervision

• Mentored undergraduate and graduate students on research methods, experimental design, and paper writing (from dataset curation to reproducible pipelines).

Workshops / Short Sessions

• AI for Smart Grids: Hands-on session building theft-detection baselines and evaluating class-imbalance strategies.
• ML Experimentation Best Practices: Feature engineering, validation leakage prevention, and lightweight model governance.

Materials & Policies

• Office Hours / Mentoring: By appointment.
• Reproducibility: All coding assignments encourage version control, environment lockfiles, and clear READMEs.
• Academic Integrity: Strict adherence to institutional policies; collaboration allowed where explicitly stated.

Note: Syllabi and outlines above are representative and may vary by semester. If you need a current copy, please reach out.