class Matteo:
def __init__(self):
self.name = 'Matteo'
self.surname = 'Merlo'
self.education = ['Politecnico di Torino']
self.experience = ['Icarus Polito Team', 'Links Foundation']
self.achievements = ['Best Paper Award at ECML PKDD 2023']
self.interests = ['Space Exploration', 'Climate change', 'Automotive', 'Computer Vision', 'Finance']
self.hobbies = ['Chess', 'Reading scientific papers', 'Gym', 'Hiking']
- π₯
Multitask Semantic Segmentation from satellite imagery for burned area and severity estimation
: A Multitask Learning in Semantic Segmentation approach is employed for targeting both wildfire delineation and burn severity estimation. - π°οΈ
CEMS Wildfire Dataset
: A large dataset (500+ images) of past wildfire from Copernicus EMS using Sentinel-2 images. - π©
Skymap path planner
: UAV flight route planner though the clouds using pathfinding algorithm.
- π€οΈ
Path finding in a Weighted Environment
: Try and testing different pathfinding algorithms in weighted environments.
- π
Real-Time Domain Adaptation in Semantic Segmentation
: A class-based styling approach for Real-Time Domain Adaptation in Semantic Segmentation applied within the realm of autonomous driving solutions. - π
Smart Home Vigilance System
: an indoor vigilance system that is capable of recognizing the presence of a human intrusion through video-audio recordings. - π³
Default of Credit Card Clients Dataset Analisys
: in depth mathematical analysis of Random Forest, SVM and Logistic Regression; - π₯
Twitter-Sentiment-Analisys
: Sentiment analysis of a dataset of tweets through machine learning techniques. - π
Simulation Epidemics on Graphs
: SIR simulation of the evolution with the parameter of 2009 pandemic in Sweden with the goal of learning the network structure characteristics and disease-dynamics.
- π
Machine Learning for IOT
: Homeworks from the course "Machine Learning for IOT", multi-step forecasting and Edge-Cloud Collaborative Inference. - π
Network dynamics and learning homework
: Homeworks from the course "Network Dynamics and Learning", averaging dynamics through network and epidemic simulation model. - π
Distributed architectures for big data processing and analytics
: Homeworks from the course "Distributed architectures for big data processing and analytics", pyspark and hadoop exercises. - π
Datascience Lab: process and methods
: Laboratories from the course "Datascience Lab: process and methods";