My name is J. Di Dio and I am currently pursuing a master degree in Data Science at EPFL.

Here are some projects I worked on:

• Stop Pre-Training: Adapt Vision-Language Models to Unseen Languages

  Master project done in LSIR lab at EPFL. This project presents a novel approach to enhance a predefined vision language (VL) model by incorporating adapter module directly in the text encoder, enabling parameter-efficient transfer to arbitrary tasks and languages. The focus is on leveraging these adapter layers to facilitate transfer learning on low-resource languages.

• Cluster-based approaches for cryptocurrency portfolio optimization

  The project examines filtering methods for cryptocurrency return correlations to optimize portfolios. Techniques like Random Matrix Theory and clustering are used to identify asset groups with correlated movements. Principal Component Analysis is employed to construct a global minimum variance portfolio. Data from 269 cryptocurrencies between January 2021 and December 2022 is analyzed for cluster stability and risk comparison. Report

• Bob’s journey to adminship: Exploring the world of Wikipedia Requests for Adminship

  Project for the course Applied Data Analysis (CS-401). This project ranked in the top 10 among 122 projects (~600 participants).

  Only a small fraction of wikipedia users vote in the 'request for adminship' (RfA) elections, although admins hold the key to maintaining the integrity and functionality of the world's largest online encyclopedia. The aim is to investigate the factors influencing participation, identify challenges that arise from the low engagement rate, and examine carefully the fairness of the elections. Based on our research, we aim to propose effective solutions to enhance the fairness and overall participation rate.

• Real-Time Emotion Recognition in VR

  In this semester project done at the Immersive Interactive Group at EPFL. The goal was to implement a machine learning model able to detect in real-time the emotions (Anger, Happiness, Disgust, Sadness, Fear and Surprise) from a particular type of data provided by a new facial tracker used in virtual reality environment. This Streamlit app present the evaluation part of the implemented models.

  Here is the final report of this project.

• Tweets sentimental analysis

  In this project, we developed multiple machine learning models for the purpose of conducting sentiment analysis on Twitter posts. The objective was to classify the posts as either positive or negative. To achieve this, we employed some state-of-the-art transformer models, including BERT and RoBERTa, for the classification task. The performance of our best model was evaluated on the testing set, yielding an accuracy of 0.892.

• QAM modulation for complex AWGN communication

  Course project for COM-302 (Principles of Digital Communication). Done in group of 4, the goal of this project was to use QAM modulation and demodulation to correctly communicate through a complex AWGN channel.