Education & Outreach
Workshops, seminars, mentoring sessions, and presentations on AI, computer vision, and research methodology.
Workshop Instructor · ICTU — Student Research Club
Designed and delivered a 6-session workshop covering foundational concepts in deep learning and computer vision for undergraduate and master's students. Topics included CNNs, transfer learning, object detection, and model evaluation. Hands-on sessions used PyTorch with real image datasets.
Seminar Presenter · ICTU — Research Seminar Series
Presented an overview seminar on the current landscape of AI for medical imaging, covering diagnostic applications in radiology, challenges of limited data and class imbalance, and open research problems in explainability and clinical validation.
Paper Reading Group Presenter · ICTU — AI Research Lab
Led a paper reading session reviewing the ViT (Vision Transformer) paper and subsequent medical imaging adaptations (TransUNet, Swin Transformer). Explained the self-attention mechanism, patch embedding, and the trade-off between ViT and CNN architectures for small medical datasets.
Research Mentor · ICTU — Department of Information Technology
Supervised and mentored 3 undergraduate students on capstone projects involving image classification and object detection. Guided project scoping, methodology selection, experiment design, and presentation preparation.
Workshop Instructor · ICTU — Graduate School
A one-day workshop on research paper structure, academic writing conventions, and submission strategies for computer science venues. Covered abstract writing, related work framing, experimental reporting, and common reviewer concerns.
My teaching philosophy is centered on building genuine understanding rather than mechanical skill. In AI and computer vision, the gap between copying code and understanding why a model works—or fails—is significant. I try to bridge this gap by grounding every topic in concrete problems and questions before introducing methods.
I believe students learn best when they encounter real obstacles. In workshops, I deliberately include challenging datasets and underperforming baselines so students must diagnose and reason, not just reproduce results. I prioritize teaching research habits—asking the right questions, designing controlled experiments, interpreting results critically—over any specific tool or framework.
Mentoring graduate and undergraduate students in their first research projects is something I find particularly rewarding. I try to help students develop independent scientific judgment, so they can design experiments and evaluate findings without relying on constant guidance.