Kevin Monteiro, Department of Computer Science and Technology
Sleep disorders, particularly insomnia, and mental health
conditions affect a significant fraction of adults worldwide, posing seriousmmental and physical health risk. Music therapy offers promising, low-cost, and non-invasive treatment, but current approaches rely heavily on expert-curated playlists, limiting scalability and personalisation. We propose a low-cost generative system leveraging recent advances in diffusion models to synthesize music for therapy. We focus on insomnia and curate a dataset of waveform sleep music to generate audio tailored to sleep.
What’s a numerically verified proof? In pure maths we want to prove theorems, usually using pen and paper. On the other side there exist hundreds of very elaborate ways to approximately solve equations, for example physics-informed neural networks. Due to the advent of greater computational power it has recently become possible to use such approximate solutions in a theorem proofs. In the talk, I’ll explain how that works in a toy example and then briefly mention some applications of this in pure maths.
New medical treatments are costly to develop and unlikely to successfully make their way through clinical trials, market access, and reimbursement. Moreover, new treatments
for rare diseases are limited in both patient populations and reimbursement levels, further reducing their attractiveness as an investment. To increase the incentive to develop new treatments, portfolios of investments in new treatments in clinical trials have been proposed to financially de-risk drug development.
Mike Lee PhD, Associate Professor Department of Systems Biology, UMass Chan Medical School
*Talk Title:* Life, death, and the discovery of PDAR: the Pol II Degradation-dependent Apoptotic Response
*Abstract:* Many cellular functions are considered “life essential”, but why are they actually essential? Why does a cell die, for instance, when transcription or translation are inhibited, and can we improve cancer therapies by developing a more complete understanding of how cellular life/death decisions are made? To answer these questions, we developed a suite of new tools for studying all forms of cell death.
Sign up on Eventbrite: https://medai-april2026.eventbrite.co.uk
Join us for the *Cambridge AI in Medicine Seminar Series*, hosted by the *Cancer Research UK Cambridge Centre* and the *Department of Radiology at Addenbrooke’s*. This series brings together leading experts to explore cutting-edge AI applications in healthcare – from disease diagnosis to drug discovery.
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.
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.
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.
