Laurents Marker - Software developer, National Centre for Atmospheric Science
Laurents Marker, a software developer at the National Centre for Atmospheric Science, works as part of a team that provides weather forecasting, data visualisation and planning tools to operational and scientific teams during observation campaigns. His work is centred around developing and deploying web applications, but over the past year has become increasingly focussed on their performance and robustness. This talk will cover areas on both the client and the server where developer effort has greatly improved user experience and service quality.
Prof. Eugene Wong, University of California at Berkeley
Conditional expectation and machine learning can be used to deal with the same problem, namely, regression. As capability for computation has advanced, machine learning has become the unchallenged champion for solving data driven regression. Its scalability to large data vectors, ability to accommodate vast training sets, independence from models, and above all, a remarkable ability for generalization, have made it the foundation for the AI revolution that is sweeping the world economy.
Prof. Eugene Wong, University of California at Berkeley
Conditional expectation and machine learning can be used to deal with the same problem, namely, regression. As capability for computation has advanced, machine learning has become the unchallenged champion for solving data driven regression. Its scalability to large data vectors, ability to accommodate vast training sets, independence from models, and above all, a remarkable ability for generalization, have made it the foundation for the AI revolution that is sweeping the world economy.
Training generative AI is not a one-step process. In the case of large language models (LLMs), self-supervision is often followed by supervised and reinforcement learning stages to improve instruction following, safety, and other desirable qualities. This multi-stage process that has emerged in the last 3 years has led to huge leaps in model capabilities. It has also led to new challenges and risks. In this talk, I will overview some of our group's work to identify and address such challenges by focusing on the training data used at different stages.
Prof Isabelle Augenstein (University of Copenhagen)
Language Models (LMs) acquire parametric knowledge from their training process, embedding it within their weights. The increasing scalability of LMs, however, poses significant challenges for understanding a model's inner workings and further for updating or correcting this embedded knowledge without the significant cost of retraining. Moreover, when using these language models for knowledge-intensive language understanding tasks, LMs have to integrate relevant context, mitigating their inherent weaknesses, such as incomplete or outdated knowledge.
In this talk, I will present CodeScaler, a novel framework designed to overcome the scalability bottlenecks of Reinforcement Learning from Verifiable Rewards (RLVR) in code generation. While traditional RLVR relies heavily on the availability of high-quality unit tests—which are often scarce or unreliable—CodeScaler introduces an execution-free reward model that scales both training and test-time inference.
We warmly invite you to the C2D3 Computational Biology Annual Symposium 2026. This event is open to everyone in the Computational Biology Community.
https://www.c2d3.cam.ac.uk/events/comp-bio-2026
Early Career Researcher: Abstract Submission
We are inviting Early Career Researchers to present their research during the symposium. Talks should be 17 minutes each, and a short Q&A will follow. Abstract submission - Deadline 9am 1st April 2026.
Registrations
Registration is essential. A waitlist will open if capacity is reached. Registrations - Deadline 9am Monday 4th May 2026.
This free event is open only to members of the University of Cambridge (and affiliated institutes). Please be aware that we are unable to offer consultations outside clinic hours.
If you would like to participate, please sign up as we will not be able to offer a consultation otherwise. Please sign up through the following link: https://forms.gle/Tbk2JKH6Sm3CbA8SA. Sign-up is possible from May 7 midday (12pm) until May 11 midday or until we reach full capacity, whichever is earlier. If you successfully signed up, we will confirm your appointment by May 13 midday.
Lotem Peled-Cohen (Technion - Israel Institute of Technology)
This talk presents my PhD research, supervised by Prof. Roi Reichart, exploring the intersection of Large Language Models (LLMs) and Alzheimer’s and related dementias. I begin by presenting our survey and perspective paper, in which we map the field’s current state and identify critical research gaps, such as data scarcity and the need for LLM-based simulation.
Scientific discovery emerges not from isolated reasoning, but from the intersection of diverse epistemic traditions. This talk proposes that the modern AI ecosystem, a structured network of heterogeneous reasoning agents spanning approximate and rigorous inference, constitutes a new form of collaborative intelligence for scientific inquiry. Drawing on Simon's conception of reasoning as adaptive search, we argue that such ecosystems do not merely accelerate known reasoning pathways, but create conditions under which genuinely novel representations may emerge.
Scientific discovery emerges not from isolated reasoning, but from the intersection of diverse epistemic traditions. This talk proposes that the modern AI ecosystem, a structured network of heterogeneous reasoning agents spanning approximate and rigorous inference, constitutes a new form of collaborative intelligence for scientific inquiry. Drawing on Simon's conception of reasoning as adaptive search, we argue that such ecosystems do not merely accelerate known reasoning pathways, but create conditions under which genuinely novel representations may emerge.
Scientific discovery emerges not from isolated reasoning, but from the intersection of diverse epistemic traditions. This talk proposes that the modern AI ecosystem, a structured network of heterogeneous reasoning agents spanning approximate and rigorous inference, constitutes a new form of collaborative intelligence for scientific inquiry. Drawing on Simon's conception of reasoning as adaptive search, we argue that such ecosystems do not merely accelerate known reasoning pathways, but create conditions under which genuinely novel representations may emerge.
Kirsty Pringle - Software Sustainability Institute; EPCC, University of Edinburgh
Research Software Engineers (RSEs) collaborate with researchers to develop and maintain software, helping to embed best practices that improve reliability and reduce inefficiencies in research workflows.
As awareness grows of the environmental impact of computational research, a new specialism - Green RSE - is beginning to emerge.
Green RSEs integrate sustainability into software development, ensuring environmental considerations are addressed alongside performance and usability.
Abstract: Neural networks have shown remarkable performance across data domains, especially in regimes of increasing compute budgets. However, fundamental insights into how neural networks process information, share representations and traverse loss landscapes remain uncertain. In this work, we quantify the functional impact of distribution matching, facilitated by knowledge sharing mechanisms such as knowledge distillation, under student-teacher optimisation strategies.
