I build modelling software for virtual patients and toxicology prediction. Mechanistic simulators, machine learning and biological data, wired into one differentiable system that trains end to end and runs in production.
Now
Virtual patients & in-silico clinical trials
Cell Sims team · ARPA-H CATALYST program · PREDICTS consortium · Deep Origin
I build the software behind virtual patients: multiscale models of human physiology coupled to ML layers, calibrated against heterogeneous data and exposed as workflows scientists can query.
Much of it is making legacy scientific code behave like modern ML code. I wrap C++ ODE engines, SBML models, optimisation solvers and agent-based simulations as differentiable PyTorch components, then build the gradient backends, surrogates and analysis tools around them. Once a model trains end to end, it can be stress-tested under uncertainty and reduced to the mechanisms that drive the output, which makes it usable for experiment design and in-silico decisions.
I also take photos
Experience
- 2026 – now
Modeling & Optimization Scientist · Deep Origin
Cell Sims team · ARPA-H CATALYST · PREDICTS consortium
- Differentiable pipelines that predict patient-level drug toxicity from mechanistic physiology plus ML layers.
- Turning legacy scientific solvers (ODE engines, SBML, optimisers, agent-based models) into trainable PyTorch components.
- 2022 – now
PhD Researcher, Process Systems Engineering · Imperial College London
Sargent Centre · CASE studentship with AstraZeneca · applied to pharmacokinetics and protein crystallisation
- Developed mechanistic ODE/PDE simulators, hybrid mechanistic-ML and pure ML models of dynamic systems from sparse, noisy time series.
- Uncertainty quantification and identifiability methods for pharmacokinetic and process models: profile likelihood and Bayesian inference (MCMC, variational inference, ABC).
- Methods and open-source implementations for fast global sensitivity analysis and model-based design of experiments.
- Closed-loop in-vitro / in-silico work: I was in the lab running the experiments, built the models on that data, and fed the analysis back into experimental and process design.
- 2026
Data Scientist Intern · Quaisr
London
- Agentic LLM workflows for regulated pharma process development.
- 2025
Consultant Data Scientist · Haleon
- ML soft-sensors reading product quality in real time to within 10% of lab reference, enough to retire up to half of offline QA.
Education
- 2022 – nowPhD, Chemical Engineering · Imperial College London
- 2018 – 2022MEng, Chemical Engineering, 1st Class Honours · Imperial College London
Thesis: a hybrid in-silico / in-vitro approach to the design and optimisation of large-molecule crystallisation, supervised by Maria Papathanasiou and Jerry Heng at the Sargent Centre.
Open source
jaxgsa · JAX library for global sensitivity analysis of large dynamic models (Sobol, RS-HDMR, multi-output), hundreds of times faster than SALib on multi-output workloads.
Selected 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.
Digital Chemical Engineering · 2026Editors' Choice
- An in silico / in vitro approach for uncertainty-aware hybrid models for template-induced protein crystallisation systems
Pessina D.; Calderon De Anda J.; Heffernan C.; Heng J. Y. Y.; Papathanasiou M. M.
Systems & Control Transactions · 2026
- Biomolecular Crystallisation Through Soft Templates and Seeding
Heng J.; Verma V.; Mitchell H.; Pessina D.
Advances in Biochemical Engineering / Biotechnology · 2026
- 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.
Systems & Control Transactions · 2025
- Machine learning-enhanced Sensitivity Analysis for Complex Pharmaceutical Systems
Pessina D.; Abbiati R. A.; Manca D.; Papathanasiou M. M.
Systems & Control Transactions · 2025
- 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.
Industrial & Engineering Chemistry Research · 2025
- Mammalian cell growth characterisation by a non-invasive plate reader assay
Grob A. et al. (incl. Pessina D.)
Nature Communications · 2024
Contact
Based in London. Happiest talking about differentiable simulation, hybrid modelling and making scientific code trainable.