AI master's student working on deep learning, computer vision, and applied machine learning systems.
- Currently working on computer vision, video understanding, industrial image analysis, and EEG-text fusion projects.
- Interested in medical imaging, transformer-based vision models, cognitive signal processing, and real-time inference.
- Learning more about meta-learning, active learning, reinforcement learning, and data-efficient AI.
- Ask me about deep learning architectures, PyTorch workflows, image segmentation, and applied AI research.
- Outside AI, I play violin and enjoy composing music.
- Computer vision and industrial image analysis
- Semantic segmentation with UNet, DeepLab, SAM-family models, and SBSNet
- Face recognition, image processing, and model evaluation
- Multi-agent path finding and search algorithm visualization
- Practical tools with clear documentation and reproducible workflows
- DebtPal: Django web app for shared debt tracking and settlement calculation.
- WarehouseBots MAPF Visualizer: Python visualizer for warehouse robot path finding.
- UNet Froth Segmentation Pipeline: PyTorch segmentation pipeline for industrial froth images.
- Froth Impurity Detection: OpenCV pipeline for impurity detection in froth images.
- Real-time Face Recognition: Offline webcam face recognition GUI using MTCNN and FaceNet.