Gaia A. Bertolino

About

I am a Marie Skłodowska-Curie doctoral researcher (PhD) at the University of Cambridge, working on uncertainty-aware and resource-efficient machine learning for on-device health monitoring. My research integrates uncertainty quantification, continual learning, and embedded AI to enable trustworthy and adaptive systems deployable on constrained hardware.

Driven by curiosity and a commitment to innovation, I aim to harness AI to address practical challenges and improve quality of life. My path, from a BSc and MSc in Computer Engineering (AI/ML) at the University of Calabria to international research and industry experiences in Luxembourg and Cambridge, has shaped a multidisciplinary and collaborative approach to scientific inquiry.

News

Research

Research Paper — RAMoEA-QA: Hierarchical Specialization for Robust Respiratory Audio Question Answering

Hierarchically routed multimodal QA model

Proposed RAMoEA-QA, a two-stage specialized generative system for respiratory audio QA: an Audio Mixture-of-Experts routes each recording to the most suitable pre-trained audio encoder, while a Language Mixture-of-Adapters selects a LoRA adapter on a shared frozen LLM to match query intent and answer format, improving robustness under domain, modality, and task shifts.

Data Paper — RA-QA: Towards Respiratory Audio-based Health Question Answering

Respiratory audio + natural language benchmark

Curated and harmonized 11 respiratory audio corpora to build RA-QA, the first respiratory audio question-answering dataset—about 7.5M QA pairs across 60+ attributes and three formats (verification, multiple-choice, open-ended)—and introduced a benchmark comparing audio-text generative models against traditional audio classifiers.

Teaching

Machine Learning & Real World Data

Supervisor — Syllabus

Lent Term 2026

Education

PhD in Computer Science

University of Cambridge, UK

2025–2029 (expected)

Research on uncertainty-aware and resource-efficient machine learning for on-device health monitoring, integrating uncertainty quantification, continual learning, and embedded AI to enable trustworthy and adaptive systems deployable on constrained hardware.

Master’s Degree in Computer Engineering (AI/ML)

University of Calabria, Italy

2022 – July 2025

MSc specialization in Artificial Intelligence and Machine Learning.
Final grade: 105/110

Thesis research conducted in-person at the Department of Computer Science and Technology, University of Cambridge, under the supervision of Prof. Cecilia Mascolo and Prof. Domenico Talia.

The dissertation focused on advancing machine learning models for the automation of clinical consultations and auscultation, exploring multimodal approaches, audio-based insights, and explainable AI techniques.

Bachelor’s Degree in Computer Engineering

University of Calabria, Italy

2019–2022

Final grade: 106/110
Thesis: Thesis: API Development for IoT Applications using the Mulesoft Suite, Supervisor Prof. G. Fortino (Highly Cited Researcher in Computer Science by Clarivate for five consecutive years)

Foundation projects

Talks

Beyond research

Beyond my technical roles, I’m deeply engaged in mentoring, outreach, and student leadership. I’m the co-founder of Leonardo, the University of Calabria’s largest engineering student association, and a Regional Ambassador for SheTech, promoting gender equality in technology. A passionate tennis player and lifelong sports enthusiast (swimming, dance, tennis), I’m also part of the Cambridge University Italian Society and the Cambridge University Lawn Tennis Club (CULTC). Fluent in English and conversational in French, I believe in innovation as a driver of accessible and meaningful social impact.

© Gaia A. Bertolino — Last update: December 2025