Teaching

🎓 Teaching Experience

I have had the opportunity to teach and support students at both undergraduate and postgraduate levels. My teaching style emphasizes hands-on application, conceptual clarity, and readiness for research and industry.

🧠 Introduction to Artificial Intelligence

• Level: Bachelors, 2nd Year
• Institution: Innopolis University
• Class Size: 60 students
• Key Topics: Fundamentals of AI, Machine Learning basics, Neural Networks, Intelligent Systems, Real-World AI Applications
• Teaching Approach: Visual lectures, coding assignments, and applied case studies to solidify foundational understanding

🔍 Information Retrieval

• Level: Bachelors, 3rd Year
• Institution: Innopolis University
• Class Size: 90 students
• Key Topics: Indexing, Query Processing, Ranking Algorithms, Vector Space Models, Evaluation Metrics
• Teaching Approach: Hands-on labs, real-world search engine demos, and practical assignments

📚 Advanced Information Retrieval

• Level: Masters, 1st Year
• Institution: Innopolis University
• Class Size: 30 students
• Key Topics: Web Search Engines, NLP in IR, Recommender Systems, Semantic Search, BERT-based Retrieval
• Teaching Approach: Research-driven instruction using academic papers, code reproduction, and project-based evaluation

🌱 Teaching Philosophy

I strive to create an engaging environment where students move beyond understanding to innovation. My courses balance theory with implementation and are built around inquiry, collaboration, and curiosity.

📬 Let’s Collaborate

• Open to guest lectures, panels, or seminars in AI/CV/IR
• Willing to co-design short courses or tutorials
• Available to supervise student projects and theses in related domains