matteom95 / matteom95.github.io

Hello Folks! I am Matteo Merlo

💻 A Data Science and Engineering student from Italy

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']

✅ Finished projects

• 🔥 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.
• 🛰️ CEMS Wildfire Dataset: A large dataset (500+ images) of past wildfire from Copernicus EMS using Sentinel-2 images.
• 🛩 Skymap path planner: UAV flight route planner though the clouds using pathfinding algorithm.

🚧 Repositories in progress

• 🛤️ Path finding in a Weighted Environment: Try and testing different pathfinding algorithms in weighted environments.

📑 Academic Projects 👨🏼‍🎓

• 🚙 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.
• 🛃 Smart Home Vigilance System: an indoor vigilance system that is capable of recognizing the presence of a human intrusion through video-audio recordings.
• 💳 Default of Credit Card Clients Dataset Analisys : in depth mathematical analysis of Random Forest, SVM and Logistic Regression;
• 🐥 Twitter-Sentiment-Analisys: Sentiment analysis of a dataset of tweets through machine learning techniques.
• 💉 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.

📚 Academic Repositories 👨🏻‍💻

• 📘 Machine Learning for IOT: Homeworks from the course "Machine Learning for IOT", multi-step forecasting and Edge-Cloud Collaborative Inference.
• 📗 Network dynamics and learning homework: Homeworks from the course "Network Dynamics and Learning", averaging dynamics through network and epidemic simulation model.
• 📕 Distributed architectures for big data processing and analytics: Homeworks from the course "Distributed architectures for big data processing and analytics", pyspark and hadoop exercises.
• 📙 Datascience Lab: process and methods: Laboratories from the course "Datascience Lab: process and methods";

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