Daniele Pessina
- Time-series modelling in data-scarce regimes with advanced machine learning 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.
- Development of 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).
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Pessina D.; Tian, T.; Watson, O.; Heng, J. Y. Y.; Papathanasiou, M. M. (2025) Transfer Learning Assessment of Data-driven Crystallisation Processes via Constrained Neural Ordinary Differential Equations
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Pessina, D.; Calderon De Anda, J.; Heffernan, C.; Heng, J. Y. Y.; Papathanasiou, M. M. (2025) Integrated In Vitro/In Silico Uncertainty Quantification Method for Protein Crystallization Models
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Pessina, D.; Calderon De Anda, J.; Heffernan, C.; Tian, T.; Watson, O.; Heng, J. Y. Y.; Papathanasiou, M. M. (2025) Model-based approach to template-induced macromolecule crystallisation
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Pessina, D.; Abbiati, R. A.; Manca, D.; Papathanasiou, M. M. (2025) Machine learning-enhanced Sensitivity Analysis for Complex Pharmaceutical Systems
About me:
I am a 3rd 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.
The past few years I’ve worked on:
I am interested in exploiting advanced numerical and computational techniques to quantify modelling uncertainty, facilitate process design, and support decision-making. Check out my publications: