Giovanni Spadaro

Areas of study: Machine Learning, Deep Learning, Computer Vision, Natural Language Processing, Statistics, Data Analytics.

Thesis: Predictive algorithms' application in medical diagnosis

Advisor: Prof. Maurizio Palesi

Final grade: 110/110. Areas of study: Computer Science, Machine Learning, Signal Processing, Electronics, Automation.

Supervisors: Prof. Francesco Beritelli, Ing. Michele Russo

Study and implementation of SOTA GAN based architectures for anomaly detection in industrial multivariate time-series datasets. Training and evaluation of such models in order to provide a baseline aimed at predictive maintenance and early machineries fault detection. Technologies: GANs, LSTMs, PyTorch, Scikit-learn.

Designed and implemented a multiclass classification system to evaluate candidate profiles, leveraging advanced feature engineering across both structured datasets and unstructured text sources. Built an interactive Gradio web application to demonstrate the system’s prototype and support rapid model validation and overall feedbacks. Deployed a scalable, production-grade inference infrastructure on AWS, ensuring reliable model serving and smooth integration with existing business workflows. Technologies and Services: AWS (mainly SageMaker, Lambda), Google Cloud, PostgreSQL, Pandas, Scikit-learn, XGBoost, OpenAI APIs, HuggingFace Spaces, Git.

Private lessons of Mathematics, Physics, Computer Science, Electrotechnics, Machine Learning, Natural Language Processing and Computer Vision for MSc, BSc and high school students. Make students grow academically and get them excited about scientific subjects.

I'm part of the Low Voltage division of the Catania team taking that should take part in the Formula SAE competition. My role since now has been to manage the workflow of the Low and High Voltage divisions and taking part to the R&D of a communication system based on the CAN-bus protocol.

Series of seminars about Basics of Quantum Mechanics, Quantum Computing and Qiskit framework. Solving 3 sets of challenges using Qiskit.

Understand the basics of cloud computing and modern data architectures and learn about the main AWS technologies for creating and managing Data Storage, Data Processing and Data Analysis steps.

Match analysis, opposition scouting e talent scouting from Volleyball scout datasets.

Series of lessons about Machine Learning, Deep Learning, IoT and Cyber Security from UNICT professors and Samsung experts. Development of an innovative group project: Hexagarden, an intelligent home wall garden.

Introduction to the AWS platform.

Solve a business problem related to the supermarket checkout queue forecast in 32 hours using Machine Learning techniques.