About
Hi! I'm Salvo Cavallaro, a Machine Learning Engineer from Catania, Italy. I am currently working at Loop AI where I contribute to realize cool stuff related to the world of Artificial Intelligence. I typically spend my time improving my skills in this field, realizing many different projects like these. I love travelling and visiting new places, learning about new cultures, languages and customs. I also like going to the gym, a place where I spend a lot of my time every week!
Skills
Resume
Education
Master of Science in Computer Engineering
Oct 2019 - Mar 2022
University of Catania, Catania, Italy
GPA: 3.90/4.00
Courses: IT Security, Software Eng., Distributed Systems, Cognitive Computing and Artificial Intelligence
Erasmus Exchange Student
Feb 2021 - Jun 2021
Universitat Politécnica de Catalunya, Barcelona, Spain
Courses: Multimedia Encoding, Innovation and Creativity: Tools for Engineering
Bachelor of Science in Computer Engineering
Sep 2015 - Mar 2019
University of Catania, Catania, Italy
GPA: 3.84/4.00
Courses: Algorithms, Operating Systems, OOP in Java, Databases, Videogames, Computer architectures
Experiences
Machine Learning Engineer
Jan 2022- Present
Loop AI Labs, Remote (EMEA)
Research Fellow
Sep 2021 - Feb 2022
Universitat Politécnica de Catalunya, Barcelona, Spain
- Created a VAE model in TensorFlow for molecular generation able to group similar compounds
- Investigated how to keep molecular properties in the latent space
- Used clustering density-based algorithm and visualization tools (matplotlib)
Machine Learning Competition
Apr 2021 - May 2021
Loop Q Prize
- Realized five models for emotion classification of images
- Implemented one SVM model in scikit-learn using classical CV techniques
- Developed four CNNs for automatic feature extraction using TensorFlow
Portfolio
Here there is a series of example projects I worked on, mainly at University.
VAEs - Inverse Molecular Design
Code developed as a research work in the field of Bioinformatics applied to the study of molecular structures using Deep Generative Models.
Main Technologies: TensorFlow, Python, Google Colab
Emotion Recognition - Loop Challenge
Code developed to solve a Machine Learning Challenge organized by Loop AI Labs. It is composed by 5 different models (classical CV and CNN) developed both using ML and DL techniques for emotion classification of images.
Main Technologies: TensorFlow, Python, Google Colab, OpenCV
Cognitive Computing and Artificial Intelligence homeworks
Collection of three homeworks realized to study and apply Machine Learning and Deep Learning principles.
Main Technologies: PyTorch, Python, Google Colab
IOT Railway Monitor System
App developed for a University project. It is composed by a gateway server for Twitter Sentiment Analysis and retrieval of data from sensors on board of trains.
Main Technologies: Python, Flask, NLTK library, Tweepy library, ESP32 microcontrollers
Medical Insurance App
App developed for a University project. It is composed by a client and a server to implement a system for a medical insurance company.
Main Technologies: Python, Go, R, Django, MySQL
E-commerce - microservice
App developed for a University project. It is composed by a main microservice to manage orders for an e-commerce app and some supplementary minor microservices.
Main Technologies: Java, Spring, Docker, Apache Kafka
Finch Airlines
App developed for a University project. It is a simple booking system for an airline company developed to apply practically Software Engineering principles.
Main Technologies: Java
Zombie Camp
App developed for a University project. It is a Survival Shooter videogame where the player has to escape a zombie invasion taking place in a camp.
Main Technologies: Unity 3D, C#