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TEAM LADE

Alessio Ansuini

Researcher at Laboratory of Data Engineering

I am a theoretical physicist with interdisciplinary experience across biology, physics, and computation. My research has addressed individual biological molecules and neurons to learning, cognition, and artificial intelligence. From 2011 to 2019, I collaborated with experimental and theoretical groups at SISSA, tackling problems in neurobiology, cognitive neuroscience, physics, and machine learning, and in 2019, I joined Area Science Park where I helped shaping its scientific research directions. My current focus involves understanding neural networks representations and exploring new frameworks for information processing in biological systems. Since 2018, I have been teaching Deep Learning courses at the University of Trieste and for the joint SISSA–ICTP Master’s in High-Performance Computing. In addition, I have delivered lectures at international schools and remain committed to public speaking and artistic initiatives aimed at sharing scientific ideas with a broad audience.

 

Research Interests 
  • Analysis of neural representations
  • Computational neuroscience
  • Artificial intelligence
Experience & Education
  • Master in High-Performance Computing, SISSA/ICTP, 2016
  • PhD in Theoretical Physics, “Sapienza” University of Rome, 2010
  • MSc in Physics, “Sapienza” University of Rome, 2005
Latest pubblications
18/09/2025
The Narrow Gate: Localized Image-Text Communication in Native Multimodal Models
Abstract Recent advances in multimodal training have significantly improved the integration of image understanding and…
Go to the news The Narrow Gate: Localized Image-Text Communication in Native Multimodal Models
18/05/2026
Perceptual misalignment of texture representations in convolutional neural networks
Abstract Mathematical modeling of visual textures traces back to Julesz’s intuition that texture perception in…
Go to the news Perceptual misalignment of texture representations in convolutional neural networks
04/04/2026
Understanding and inverse design of implicit bias in stochastic learning: a geometric perspective
Abstract A key challenge in machine learning is to explain how learning dynamics select among…
Go to the news Understanding and inverse design of implicit bias in stochastic learning: a geometric perspective