Giovanni Spadaro

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

Thesis: Predictive algorithms' application in medical diagnosis

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

Mentor: Prof. Francesco Beritelli, Ing. Michele Russo

Analisys and implementation of GAN based tecniques for anomaly detection in time-series datasets.

1. Text classification system based on the transformer encoder-only architecture and on a custom training recipe. 2. Score-based text matching system tailored to the specific business problem. 3. Data management and ingestion procedures. Including data cleaning, LLM-based data enrichment and third-party services data enrichment. 4. Development of a simple demo web app. Main technologies used: PyTorch, Transformers, Pandas, Duckdb, LangChain, HuggingFace spaces, Gradio.

1. Development of XGBoost-based multiclass classification system aimed atdiscriminating a candidate based on features partly proposed by thecandidate and partly engineered from structured and unstructured sources (PDF curriculum). 2. Gradio-based demo web application to show the system's prototype. 3. Deployment of the serving infrastructure in production on AWS platform. Main services used: Sagemaker, Lambda, Event Bridge. Skills Acquired: Pandas, Scikit-learn, XGBoost, OpenAI, PostgreSQL, Git, AWS, Google Cloud, HuggingFace spaces.

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 competiton. 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 a group project about an innovative project in the field of smart homes.

Introduction to the AWS platform.

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