Alessandro E. Pasqui

TL;DR

I am currently a Postdoc researcher at the MICS lab at CentraleSupélec, where I work on interpretability methods for foundation models trained on biological data.

I recently completed my PhD at the Collège de France as a Marie Skłodowska-Curie Fellow, under the supervision of Dr. Hervé Turlier and in collaboration with Dr. Maxence Ernoult from Google DeepMind.

My doctoral research focused on AI-based methods for inverse problems in biological cell systems, developing computational frameworks that bridge physics-based modeling, machine learning, and cell biology.

Key projects include:

• VertAX — a differentiable vertex model implemented in JAX for efficient forward and inverse modeling of confluent tissues, leveraging automatic differentiation and bilevel optimization to infer cellular parameters and reproduce tissue-scale behavior. (Manuscript in preparation for Nature Computational Science)
• ZAugNet — a self-supervised generative model for 3D bio-imaging (built in PyTorch) that uses adversarial learning and knowledge distillation to enhance axial resolution in microscopy data. (Manuscript under revision at Nature Communications)

Before my PhD, I completed a Master’s Degree in Statistical Physics at Sapienza University of Rome and the Italian Institute of Technology, where I developed high-performance algorithms for shape matching and protein–receptor interaction studies. My Bachelor’s thesis, also at Sapienza, explored percolation in models with long-range interactions using analytical and numerical methods.