Seminars

I will present a family of sampling-based uncertainty measures that generalise surprisal and allow expressing a wider range of hypotheses about the workings of incremental language processing.

*Abstract*

Abstract: Automating the development of machine learning algorithms (i.e., meta-learning) has the potential to unlock new frontiers in the field. However, our ability to learn to discover has been limited by a focus on small, static benchmarks.

Abstract: Popularly known for its IDEs and for being behind the Kotlin language, JetBrains is also heavily involved in AI, not just by integrating external tools into its IDEs but also by developing its own. This talk will be beginner-friendly, helping students understand how we reached the current age of AI agents.

This paper is accepted to proceedings of the International Symposium on Code Generation and Optimization (CGO).
The paper is available at https://arxiv.org/abs/2511.08848

Abstract:
The long-held vision of wearable computing is to move beyond simple activity tracking towards proactive, intelligent assistance. However, achieving true contextual understanding on resource-constrained devices like smart glasses remains a substantial challenge.

The presentation will introduce how machine learning supports drug discovery at Astex, with a focus on modelling single‑cell RNA‑seq data to understand neurodegenerative diseases such as ALS. This is an area where modelling objectives do not naturally fit traditional supervised or unsupervised learning frameworks.

Abstract to be confirmed

Link to join virtually: https://cam-ac-uk.zoom.us/j/89473073451

Modern computers are collections of heterogenous components, including GPUs, TPUs, NPUs, FPGAs and other devices that carry out computing tasks but which are not the central CPU.