Daniele Pessina
I build computational models at the interface of machine learning and biology: differentiable, uncertainty-aware tools that turn messy experimental data into decisions.
Currently
Virtual human avatars of toxicity
I build models that predict when and how a drug becomes toxic in the human body. They combine mechanistic simulation of human physiology with machine learning, working toward drug-safety prediction accurate enough to reduce reliance on animal testing.
Doctoral research
Alongside Deep Origin, I'm finishing my PhD at the Sargent Centre for Process Systems Engineering, Imperial College London, supervised by Dr. Maria Papathanasiou and Prof. Jerry Heng, funded through a CASE studentship with AstraZeneca.
My thesis develops differentiable, uncertainty-aware models for template-induced biomolecule crystallisation and pharmacokinetics which combine mechanistic simulation, machine learning, and probabilistic methods to guide experimental design and accelerate process development.
What I work on
- Multiscale modelling of drug toxicity — mechanistic simulation of human physiology combined with machine learning to predict drug safety.
- Time-series modelling in data-scarce regimes — neural ODEs, transformers, and autoencoders with transfer and reinforcement learning.
- Probabilistic programming & uncertainty quantification for mechanistic, hybrid, and black-box models, enabling robust estimation under uncertainty.
- Differentiable programming for GPU-accelerated, autodiff-compatible simulation in Julia and JAX.
- Hybrid modelling & ML-assisted sensitivity analysis of large, non-linear pharmacokinetic systems.