MrDaniele Pessina
Research Postgraduate
Department of Chemical Engineering - Faculty of Engineering
Orcid identifier0009-0004-3107-6857 (opens in a new tab)
- Research PostgraduateDepartment of Chemical Engineering - Faculty of Engineering
- ACE Extension, South Kensington Campus, United Kingdom
RESEARCH
- Time-series modelling in data-scarce regimes with advanced ML architectures like neural ODEs, transformers, autoencoders with transfer learning and reinforcement learning methods
- Probabilistic programming and uncertainty quantification of mechanistic, hybrid and black-box models, enabling robust parameter estimation and decision-making under uncertainty
- Differentiable programming for process modelling and simulation, delivering GPU-accelerated, autodiff-compatible frameworks in Julia and JAX
- Hybrid modelling and ML-assisted sensitivity analysis of large, non-linear pharmacokinetic models, improving interpretability and identifiability
- ML-based soft sensors for real-time product quality monitoring in manufacturing, reducing reliance on offline batch testing
- Experimental validation of modelling hypotheses through medium-scale protein crystallisation studies using MT EasyMax and PAT tools (FTIR, UV-Vis, image analysis)
- Agentic AI for process development