Andrea Porri

Computer Vision Engineer with a Master ’s Degree in Artificial Intelligence and Automation

Engineering, and over 10 months of hands-on research and development experience. Solid background in Computer Vision, Machine Learning, and Deep Neural Networks, with proven expertise in designing and implementing deep learning solutions for real-world challenges,

such as a clinical screening tool for nystagmus detection in emergency settings. Passionate about applying advanced AI techniques to practical applications, I excel in collaborative environments, contributing initiative, analytical rigor, and creativity to deliver innovative and high-quality solutions.

Curricular Internship of the Master’s Degree Program (06/2024 – 03/2025), SAILab – University of Siena

Research project in Computer Vision aimed at developing a flexible and user-friendly screening system for the detection of nystagmus symptoms. The tool, designed for use in emergency departments by nursing staff, allows recording via mobile devices, without strict acquisition constraints, thus ensuring high adaptability to real clinical contexts. Afterwards, the videos could be analysed simply by uploading them via a simple GUI on the PC.

Activities 

• Creation of datasets for segmentation and object detection aimed at extracting key ocular features (eye tracking).
• Development of datasets using real patient videos provided by the Department of Experimental and Clinical Medicine, University of Florence, and Careggi University Hospital, in addition to other public data.
• Design of an eye-tracking pipeline, leveraging video stabiliser, object detection and segmentation tools for feature extraction.
• Data cleaning and preprocessing of extracted features.
• Analysis of data using Convolutional Neural Networks (CNNs) to identify patterns associated with nystagmus within the videos.
• Generation of annotated videos highlighting the time segments in which potential episodes of nystagmus were detected, to support clinical screening and facilitate subsequent analysis by specialists.

• Bachelor's degree in Management Engineering from the University of Siena (2015–2020), obtained with a grade of 106/110.
• Master's Degree in Artificial Intelligence and Automation Engineering, Intelligent Systems curriculum, at the University of Siena (2020–2025), obtained with a grade of 110/110.
  • During the studies, I explored subjects such as Machine Learning, Artificial Intelligence, Neural Networks, Advanced Digital Image Processing, NLP, Big Data and not only.
  • Thesis title: Automatic Nystagmus Detection in Video Acquired with Mobile Devices: Development and Preliminary Validation (It is linked to the nystagmus research project mentioned above).
  • Projects Titles: (Python) 
    • Real-Time 3D Localization System for Hansen Connectors (6 months) - Computer Vision.
    • Face Generator Using Generative Adversarial Networks (2 months) - Generative Computer Vision.
    • 1D GMM Data Generator for MLP Regression (4 months) - Data Generation (Regression)
    • Cyberbullying Tweet Classifier fine-tuned from Microsoft (2 months) - NLP
    • Cryptocurrency Market Analysis Using Pandas Library (2 months) - Data Analysis (Big Data)

PUBLICATIONS

• “A deep learning approach for nystagmus recognition in RGB ”
  • Status: Accepted. 
  • Presentation: IEEE MetroXRAINE 2025
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CERTIFICATIONS

• Computer Vision Certification
  • HuggingFace – In preparation.
• B2 language certificate
  • CLA (Università di Siena)

SKILLS

• English: Intermediate
• Python: Advanced
• C++: Basic
• SQL: Basic

KNOWLEDGE

Computer Vision, Generative AI, Deep Learning, Object Detection, Object Segmentation, Pytorch, Tensorflow, Keras, OpenCV, Ultralytics YOLO, Huggingface, Pandas, Git, Wandb, Docker