Dustin Brandon Wright (Aalborg University Copenhagen)
Abstract: AI systems are rapidly replacing traditional information seeking media such as web search, yet their epistemic diversity - defined as the diversity of real-world claims in their outputs - has never been measured. Low epistemic diversity would pose a risk of knowledge collapse as homogeneous LLMs mediate a shrinking in the range of accessible information over time.
Vera Hazelwood, Director of Data Science Skills and Partnerships, AstraZeneca
The Beyond Academia: AI & Data Science Applications seminar series at the University of Cambridge explores the transformative role of data and artificial intelligence in the world. Designed for postgraduate researchers, early-career academics, and students, the series bridges the gap between high-level research and real-world deployment.
Local SGD, also known as Federated Averaging, is a simple approach to communication-efficient distributed optimization in which each client performs several stochastic-gradient steps between communication rounds.
Wastewater-based epidemiology, although dating back almost a century, has undergone a renaissance since the last pandemic. By providing an aggregated signal of infections, it offers strong operational advantages over individual testing for tracking pathogens at the population level, but also introduces distinct analytical challenges.
While edge devices like commercial smartphones can now run inference with on-device large language models (LLMs), enabling on-device fine-tuning of LLMs remains a formidable challenge. Addressing this challenge is vital for democratizing on-device LLMs in numerous privacy-sensitive domains such as agentic AI, mobile health, and home robots.
Rust might seem like a newcomer on the scene but today it is heavily used in production. Combining memory safety and interoperability with top tier speed, Rust is a great choice for low-resource and green solutions. This talk takes a look at where and when Rust is the right tool for the job, navigating the landscape of big data bioinformatics and modern research pipelines while sharing the journey of building a community around an open source crate package.
Since its inception, the Turing Test - the seemingly audacious vision of machines that speak with such fluency that they blur the boundary between human and artificial minds - has served as the north star of language understanding and AI. In the era of large language models, this vision feels tantalizingly close, yet increasingly hollow, as AI systems now routinely surpass humans not only in language-related tasks but across a variety of complex domains.
As coding agents move from autocomplete to autonomous software engineering, they need an execution substrate designed for non-human developers. This talk will explore the systems challenges behind forkable, isolated environments, including filesystem state, networking and credentials, reproducibility, fast cloning, build/test execution, recovery, and observability. The focus will be on the underlying architecture, trade-offs, and open systems problems.
Professor Michael Elliott, University of Michigan School of Public Health
Longitudinal data has become a major part of the landscape for clinical and epidemiological research. While variance is typically understood as nuisance – the “noise” in “signal-to-noise” – there is increasing evidence that underlying variability in subject-level measures over time may also be important in predicting future health outcomes of interest.
Matthew McCormack - Software Developer, National Oceanography Centre
BODC (British Oceanographic Data Centre) receives data with a diverse range of sources, formats, and content. Previously this was a heavily manual process requiring data managers to invest significant time into each dataset to standardise the format, apply controlled vocabularies, and apply QC to the data. This also involved moving the data across multiple file systems and disparate software tools.
Christina Matteotti, Head of Universities, EMEA Partnerships, Google
The Beyond Academia: AI & Data Science Applications seminar series at the University of Cambridge explores the transformative role of data and artificial intelligence in the world. Designed for postgraduate researchers, early-career academics, and students, the series bridges the gap between high-level research and real-world deployment.
Dr Francesco Paolo Casale - Human Technopole, Milan, Italy; Helmholtz Munich, Munich, Germany; Faculty of Informatics, Technical University of Munich, Garching, Germany
Talk title: AI in Human Genetics: Linking Genetic Variation to Phenotypes Across Scales
The Beyond Academia: AI & Data Science Applications seminar series at the University of Cambridge explores the transformative role of data and artificial intelligence in the world. Designed for postgraduate researchers, early-career academics, and students, the series bridges the gap between high-level research and real-world deployment.
