Shortly, I'm a developer who's currently specializing in Machine Learning and Deep Learning topics. My passions are coding, technology and motorbikes! Here you can find my CV, contacts and some of my projects!
Hi everyone, I'm Daniele, a 23-year-old computer scientist. I was born on January 18, 2001, in Rome, where I just completed my master's degree in Computer Science at Sapienza University of Rome. I really like AI-related topics such as classical Machine Learning, Computer Vision, and Deep Learning in general. Computer vision is the topic I've mostly studied, also due to my academic path, but I am currently trying to expand my horizons by studying the other topics on my own. Outside of professional pursuits, I enjoy driving my Honda while listening to great music and engaging in discussions about nerdy topics like gaming!
2023-2024
I completed my master's degree in computer science at Sapienza Universitu of Rome with a final grade of 110L/100. My studies were not focused on a single branch, indeed, I preferred to study a wide spectrum of technologies and aspects of the computer science. I become pretty familiar with the Blockchain tool (developing an Ethereum project), Cloud Computing (also earning the AWS certificate), Machine Learning and Computer Vision.
2021-2022
I earned my bachelor's degree in Computer Science at Sapienza University of Rome with a final grade of 110L/110. During this program, I gained a comprehensive understanding of various aspects of computer science.
2018-2019
I earned my diploma from Vito Volterra High School with a grade of 90/100. My academic focus was in the scientific field, with additional computer science classes.
Oct. 2024 - Now
Starting from October 2024 I'm being part of the Reasearch&Innovation team in ELT Group as full-time Machine Learning Engineer. Even though, the job title is about Machine Learning, I'm into all the AI branches such as Deep Learning, Machine Learning, Computer Vision, Reinforcement Learning and etc...
Feb. 2024 - Oct. 2024
This project was developed in collaboration with my collegue Marco Guarino for our master's degree thesis. Starting from an existing project named PoET, we developed a full pipeline for creating your own 6D pose estimation dataset and training the PoET deep learning algorithm on that dataset. This project was developed for an real-world idustrial scenario. If you're interested to the details, you can download my master's degree thesis from the button below.
2023
In the second half of 2023, I continued working with the Psychology department, specifically under contract with the PNRR (Piano Nazionale di Ripresa e Resilienza), to develop a new tool for research purposes. This involved creating an augmented reality environment where users (target age 60+ years old) were encouraged to engage in physical activities, such as running, walking, and coordination exercises, guided by a virtual coach. Once again, I utilized Unity as the game engine for this project. If you're interested, you can read more about this project by looking this detailed description (i will provide as soon as I can to translate this document in english, for the moment is available only in italian):
2021-2022
This work experience is closely related to my bachelor's degree. My thesis focused on developing a virtual reality environment using the Oculus Quest 2. The thesis was a collaboration with the Psychology department at Sapienza University. I worked with them to create a tool that is currently used for research purposes. The tool was developed using Unity as the game engine and C# as the programming language.
Here, you can find some of the projects I'm currently working on. I'll do my best to keep the list updated; however, you can always drop me an email if you want to know more about them!
These notes were taken during the years in a self-learning process. You can find general deep learning notions but also more deep and detailed sections dedicated to well-known architectures.
These notes were taken while I was studying RL by my own. In particular, these notes were taken while reading the book "Reinforcement Learning: An Introduction" published by Richard S. Sutton and Andrew G. Barto. These are basic and also quite outdated notions, however they also represent the basis for the RL approach.
This project offers a simple yet efficient way to retrieve raw camera images from a Varjo visor. It is part of a larger GitHub project, IndustrialPoseEstimationFramework. In the linked project, there is functionality to run a pose estimation model in inference mode through a client-server architecture. However, in that setup, the client uses a standard webcam to capture images. With this script, we can integrate raw camera images from the Varjo visor as input for the model.
This project was developed to simplify and speed up the Linux installation and configuration process. Currently, the dotfiles support two Linux distributions: Ubuntu and Arch-based systems. For Ubuntu, the configuration primarily involves removing unnecessary packages, such as Snap, and applying some styling themes. For Arch-based systems, the configuration installs all the required packages and then applies styling elements. The chosen desktop environment for Arch is Hyprland.
This project facilitates console emulation on Linux and Windows, featuring a game save system. Users specify a server for file storage, enabling seamless download and upload of game files.
This project allows users to generate synthetic videos from CAD models, including .npy files with additional information. Models are loaded dynamically into a Blender scene, and the camera smoothly moves along spherical points to create the final video.
This project consists of a dashboard that can be used during fantasy football bids. With this tool, you can keep track of every player sold, as well as the current players' credits and their players.
The primary objective of this project is to develop a decentralized application (DApp) on the Ethereum blockchain, facilitating a crowdfunding system. Users have the flexibility to either create their fundraising campaigns or contribute to existing ones. Key features include MetaMask integration for secure wallet interactions, the creation of DSTs (Dragon Sphere Tokens) to empower users, the ability to participate in crowdfunding activities, and the implementation of Truffle for streamlined smart contract development and testing.
Vision Odyssey is an innovative project that leverages advanced computer vision and machine learning techniques for creating a unique gaming experience. It includes facial and emotion recognition for user registration, login, and dynamic difficulty adjustment, as well as gaze and head pose tracking for character control, offering accessibility to individuals with partial or total paralysis. The game, developed in Unity, communicates with Python-based servers that analyze webcam inputs to control the game and adjust difficulty, utilizing datasets and state-of-the-art algorithms for its functionalities.
You can drop me an email at the address: danielebbertagnoli@gmail.com or by compiling the following form: