Conference Agenda

Brief Description and Outline:

This workshop introduces TwinWeaver, a generative artificial intelligence framework for building digital twins of longitudinal systems. Participants will learn how generative models can move beyond static prediction toward dynamic trajectory simulation, counterfactual reasoning, and intervention modelling. The session combines conceptual foundations with practical design principles and a guided walkthrough of the TwinWeaver pipeline.

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Hour 1 Conceptual Foundations

• Introduction to digital twins and longitudinal modelling
• Limitations of discriminative prediction models
• Principles of generative modelling for temporal systems
• Representation of trajectories as structured token sequences
• Ethical, governance, and reproducibility considerations

Hour 2 TwinWeaver Framework

• Architecture of the TwinWeaver generative model
• Tokenisation of longitudinal events and state transitions
• Training pipeline and data preprocessing
• Conditioning and intervention modelling
• Evaluation of trajectory forecasts and simulation realism

Hour 3 Practical Walkthrough and Discussion

• Demonstration of converting longitudinal data into TwinWeaver format
• Overview of model training workflow
• Generation of simulated future trajectories
• Counterfactual scenario modelling
• Discussion of scaling, infrastructure requirements, and deployment strategies
• Interactive discussion on adapting TwinWeaver to participant use cases.

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Goals:

Participants will understand the conceptual foundations of digital twins and how they differ from traditional predictive models. They will learn how generative transformer architectures can represent structured temporal data. They will gain insight into how to construct longitudinal token representations, train generative models, and simulate future trajectories. They will leave with a clear roadmap for implementing digital twins in their own research domain. Participants will understand how to design and implement a generative digital twin framework for longitudinal modelling. They will gain practical insight into training and evaluating generative trajectory models and will be equipped to initiate digital twin projects within their own research or applied context.

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Presenters Experience:

This workshop has already been tested in a hackathon setting in Melbourne and internally at Roche.

The underlying paper has led to invitations to present at AstraZeneca, attracted industry funding, and contributed to the launch of a women’s health start up supported by venture capital. We have published the code under an Apache 2.0 licence, making it freely available for use in both academic and commercial settings.

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Target Audience:

This workshop is designed for researchers and practitioners working with longitudinal data, including computational scientists, data scientists, clinicians, biomedical researchers, and doctoral candidates. Basic familiarity with machine learning concepts is recommended, but prior experience with generative modelling is not required.

Participants should bring a laptop capable of running Python notebooks. A prepared example dataset and code templates will be provided.

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Keywords:

Digital Twins