Caroline Morton
Medical doctor, epidemiologist, and systems engineer using Rust to make scientific software safer and more open.
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Dr. Caroline Morton is a medical doctor, epidemiologist, and software engineer working at the intersection of public health and modern software. She is the founder of two companies, the author of over 70 academic papers and the creator of open-source tools that bring systems-grade reliability to epidemiology. She also founded and runs Women in Rust. Her recent work focuses on using Rust to build auditable, reproducible infrastructure for scientific research and public good. She is on a mission to make scientific software safer, clearer, and more open - one crate at a time.
Simulating a Million Patients: Realistic Health Data Generation in Rust
Healthcare researchers desperately need realistic synthetic patient data: for teaching, for testing analysis pipelines, and for sharing results without compromising real patients' privacy. But generating data that's actually realistic is surprisingly hard. Naïve approaches produce patients who are statistically implausible: twenty-year-olds with dementia, smokers whose blood pressure is unaffected by their habit, populations where diabetes and hypertension never co-occur
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This talk presents a graph-based approach to synthetic patient generation, built in Rust, that models how diseases actually progress through a human life. Using directed acyclic graphs with age-banded transition probabilities, the system walks each simulated patient through decades of accumulating risk factors, diagnoses, and complications, producing population-level data that preserves the statistical relationships epidemiologists rely on.
We'll trace a concrete clinical scenario, elderly patients developing diabetes, then hypertension, then suffering a transient ischaemic attack, from its representation as a DAG, through its implementation in Rust, to its output as a million-row dataset. Along the way, we'll cover the Rust design decisions that made this tractable: strong types that prevent impossible patient states, seeded RNG for scientific reproducibility, and the adaptor pattern that lets the same patient model emit records in different clinical formats.
You don't need a medical background to follow this talk. You'll leave with transferable patterns for modelling complex real-world processes as graphs, and a new appreciation for why Rust's type system is a gift to scientific computing
Clean Code for Good Science: Rust in Research and Health
Good science demands transparency, reproducibility, and rigour. The software underpinning it should be no different. In labs, hospitals, and research institutes, Rust is beginning to appear where it matters most: places where correctness and clarity aren't just nice-to-haves, but the foundations of trustworthy research.
This talk explores what it means to write scientific software that lives up to the standards we expect of science itself. We'll look at how Rust's emphasis on explicitness and safety aligns naturally with the principles of open, reproducible research, and how we can go further by treating tests as proof, documentation as methodology, and readable code as a form of scientific communication.
Drawing on examples from epidemiology, synthetic data, and biomedical infrastructure, we'll examine how to build tools that are auditable, maintainable, and built to last. We'll also reflect on how the choices we make today, in our dependencies, our environments, and our defaults, shape whether the next generation of researchers can understand, verify, and build on our work.
Rust for Good: Systems Programming in Science and Public Health
This proposed keynote will explore the emerging use of Rust in scientific and public health software - domains where reliability, safety, and transparency are essential. While Rust is still new in these fields, its guarantees align strongly with the needs of science: correctness, reproducibility, and long-term maintainability.
I’ll share examples from my own work in epidemiology, including tools for synthetic data generation and codelist management, and highlight other efforts across climate science, biomedical research, and open data platforms. I want to inspire researchers learning systems programming, and Rust developers looking for meaningful projects, to come together around a shared vision: using Rust to build better tools for science and the public good.
This talk will reflect both on what’s possible now and what we can do better - learning from the past to ensure the scientific software we build with Rust is open, reproducible, and built to last.
RustConf 2026 Sessionize Event Upcoming
Rustikon 2026 Sessionize Event
RustLab 2025 Sessionize Event
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