This project explores multiple deep learning models: CNNs, ResNet50, and Vision Transformers, for classifying coins in images. We use advanced segmentation techniques, custom over-segmentation filtering, and data augmentation to improve generalization. Developed for the EPFL IAPR course, this system demonstrates competitive accuracy and robustness for automated coin recognition tasks.
This project builds a full-scale movie recommendation system using Apache Spark, incorporating data analytics, keyword-based filtering. Implemented on the MovieLens dataset, the system supports efficient data preprocessing, incremental rating updates, and personalized movie recommendations through LSH and predictive models.
This project explores the design and implementation of a decentralized DNS system using blockchain and a gossip-based peer protocol. The system supports secure domain registration, updates, transfers, and resolution with robust anti-entropy synchronization and Proof-of-Work consensus. The project evaluates network resilience, consensus reliability, and mining efficiency.
This project combines personality and emotion detection with machine learning to predict the number of ideas generated by entrepreneurial teams. We process multimodal data (transcripts and audio) to extract MBTI traits, emotional profiles, and speaker features, and use these to model team-level idea generation. This work advances behavioral modeling in early-stage startup evaluation.
A robotic system developed for real-time order collection in a restaurant environment, combining line-following, object detection, and GUI-based control. The robot uses image processing, multithreading, and Bluetooth communication to collect orders from tables and transmit them for analysis and optimization.