Daniele Pessina
- Uncertainty quantification methods for population-balance models.
- Data-driven surrogates and hybrid modelling methodologies with transfer learning.
- ML-assisted sensitivity analysis of pharmacokinetic models.
- AD-compatible Julia and JAX coding and package development.
- Protein crystallisation experiments in MT EasyMax reactors.
- 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
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 sponsored by AstraZeneca.
My research is focused on template-induced biomolecule crystallisation modelling and process design. I also work on integrating smart machine learning methods with pharmacokinetic modelling and for real-time process quality assurance using high-throughput sensor data.
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 publication: