Presented “Multiclass Local Calibration with the Jensen–Shannon Distance”, “On the Global and Local Calibration of Graph Neural Networks”, and “Divide et Calibra: Multiclass Local Calibration via Vector Quantization” at AISTATS 2026
I attended AISTATS 2026, where I presented and discussed “Multiclass Local Calibration with the Jensen–Shannon Distance”, “On the Global and Local Calibration of Graph Neural Networks”, and a preliminary version of “Divide et Calibra: Multiclass Local Calibration via Vector Quantization” during the poster sessions.
I am a PhD candidate in Artificial Intelligence at the University of Pisa, and I work with the Structured Machine Learning Group at the University of Trento.
My research focuses on uncertainty quantification and reliable machine learning, with particular interests in probabilistic calibration, conformal prediction, and human-AI decision making. More broadly, I am interested in developing the next generation of uncertainty-aware, trustworthy and human-centric AI frameworks.
I am always happy to chat about research, possible collaborations or interesting ideas, so feel free to reach out.