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 Assessment of Data-driven Crystallisation Processes via Constrained Neural Ordinary Differential Equations Pessina D.; Tian, T.; Watson, O.; Heng, J. Y. Y.; Papathanasiou, M. M. (2025) 📄 Read on bioRxiv →
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) 📄 Read in I&EC Research →
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) 📄 Read Paper →
Machine learning-enhanced Sensitivity Analysis for Complex Pharmaceutical Systems Pessina, D.; Abbiati, R. A.; Manca, D.; Papathanasiou, M. M. (2025) 📄 Read Paper →
Open Source Projects
🔬 CriSTool – Crystallisation In-Silico Toolbox
A Julia toolbox for simulating crystallisation population balance models, estimating kinetics from data, and exploring uncertainty and sensitivity. ⭐ View on GitHub →
🧪 Crystalline – Neural ODEs for Transfer Learning
Research code for training Augmented Neural ODE (AugNODE) models on crystallisation kinetics using JAX, Equinox, and Diffrax. Includes a Dash dashboard for interactive runs and plots. ⭐ View on GitHub →