Daniele Pessina
- 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)
About Me
I am a final year PhD student at the Sargent Centre for Process Systems Engineering in the Department of Chemical Engineering at Imperial. I am supervised by Dr. Maria Papathanasiou and Prof Jerry Heng, and my PhD is funded through a CASE studentship with AstraZeneca.
My research is focused on template-induced biomolecule crystallisation modelling and process design. I work at the interface of process systems engineering, machine learning, and computational modelling — developing tools to quantify uncertainty, guide experimental design, and accelerate crystallisation process development.
I also work on integrating smart machine learning methods for efficient pharmacokinetic modelling, and have explored data-driven high-dimensional time-series monitoring methods for real-time quality assurance in manufacturing.
Research Highlights
Publications
Transfer Learning of Data-driven Crystallisation Processes via Constrained Neural Ordinary Differential Equations
Pessina D.; Tian T.; Watson O.; Heng J. Y. Y.; Papathanasiou M. M. (2026)
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Integrated In Vitro/In Silico Uncertainty Quantification Method for Protein Crystallization Models
Pessina D.; Calderon De Anda J.; Heffernan C.; Heng J. Y. Y.; Papathanasiou M. M. (2025)
Biomolecular Crystallisation Through Soft Templates and Seeding
Heng J.; Verma V.; Mitchell H.; Pessina D. (2026)
📄 Read in Advances in Biochemical Engineering/Biotechnology →
Model-based approach to template-induced macromolecule crystallisation
Pessina D.; Calderon De Anda J.; Heffernan C.; Tian T.; Watson O.; Heng J. Y. Y.; Papathanasiou M. M. (2025)
Machine learning-enhanced Sensitivity Analysis for Complex Pharmaceutical Systems
Pessina D.; Abbiati R. A.; Manca D.; Papathanasiou M. M. (2025)
Code
CriSTool - Julia-based Population Balance Modelling, Uncertainty Quantification and Sensitivity Analysis
GSAX - JAX-based Global Sensitivity Analysis for large models