Teaching
Graduate Course (NYU)
Graduate-level course covering fundamental and advanced topics in computer vision, from classical image processing techniques to modern deep learning approaches.
My Responsibilities
- Code Labs & Interactive Sessions: Conducted hands-on programming sessions with live coding demonstrations and guided implementations using comprehensive notebooks
- Student Mentoring & Office Hours: Provided one-on-one project guidance and technical support through 6 hours of weekly office hours
- Grading & Assessment: Evaluated student assignments, projects, and exam performance with detailed feedback
- Kaggle Competition and Coding Projects: Created assignments and homeworks, and organized class-wide constellation detection competition for real-world ML experience
Key Topics Covered
Teaching Resources & Materials
Interactive Jupyter Notebooks
Created comprehensive Jupyter notebooks covering computer vision algorithms, feature detection, and deep learning techniques with hands-on implementations.
View on ColabAssessment Materials
Developed programming assignments and projects that challenge students to implement computer vision algorithms and apply them to real-world image processing tasks.
View ExamplesKaggle Competition
Organized a class-wide Kaggle competition on constellation detection, providing students with real-world experience in computer vision model development and evaluation.
View CompetitionGuest Lectures & Workshops
Mentored workshop guiding students through Arduino-based line follower bot development, covering sensor integration, motor control, and algorithm implementation.
Guided a team of 5 students in developing an innovative robotics headgear project, providing mentorship in hardware integration, programming, and project management.