About Me
I am an Electrical and Electronics Engineer working at the intersection of Deep Learning and Computer Vision — currently focused on building efficient, scalable AI systems across the full stack: from model architecture design and training to real-world deployment. My work spans knowledge distillation, object detection, and representation learning, with an emphasis on production-grade applied research.
Beyond engineering, I am drawn to the Renaissance ideal of broad intellectual curiosity. I pursue ongoing study in Mathematics, Physics, Philosophy, and Music — disciplines that sharpen both analytical thinking and creative problem-solving.
Research Interests
- Deep Learning — efficient architectures, knowledge distillation, model compression
- Computer Vision — object detection, image recognition, visual representation learning
- Applied AI Research — closing the gap between research advances and production systems
Selected Projects
Knowledge Distillation on CIFAR-10 Systematic study on knowledge distillation with ablation experiments on teacher-student architectures using the CIFAR-10 benchmark. Investigated the effect of temperature scaling, intermediate feature alignment, and loss weighting on student model accuracy and generalization.
Object Detection with RT-DETR on COCO Explored transformer-based real-time object detection using RT-DETR on the COCO dataset, analyzing trade-offs between detection speed and accuracy in resource-constrained settings.
Background
I hold a degree in Electrical and Electronics Engineering and have built a strong applied research foundation in ML/AI through academic work (including Stanford’s CS229) and hands-on project experience — with a focus on taking models from prototype to deployment.
Get in Touch
Open to research discussions, collaborations, or exchanging ideas.