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. And how they can both build and use them.
We will also look a bit into what JetBrains is doing and how you as a student can get involved with our organisation. And end the afternoon with a fun quiz, some food and cool merchandise!
P.S.
The ideas of aleatoric and epistemic uncertainty are widely used to reason about the probabilistic predictions of machine-learning models. We identify incoherence in existing discussions of these ideas and suggest this stems from the aleatoric-epistemic view being insufficiently expressive to capture all the distinct quantities that researchers are interested in. To address this we present a decision-theoretic perspective that relates rigorous notions of uncertainty, predictive performance and statistical dispersion in data. This serves to support clearer thinking as the field moves forward.
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. The session will outline standard KNN‑graph–based analytical approaches and compare them with modern VAE‑based counterfactual modelling, highlighting how each method addresses the challenges of complex biological data.
Mathematical concepts emerge through an interplay of processes, including experimentation, efforts at proof, and counterexamples. In this paper, we present a new multi-agent model for computational mathematical discovery based on this observation. Our system, conceived with research in mind, poses its own conjectures and then attempts to prove them, making decisions informed by this feedback and an evolving data distribution.
Arik Reuter and John Bronskill (University of Cambridge)
Data in tabular form is ubiquitous in industries such as finance, healthcare, and education. Historically, boosted decision trees and multi-layer perceptrons were the models of choice for making predictions on tabular data. In the last couple of years, neural process-based transformers (e.g. TabPFN, TabICL) that are trained on synthetic data have surpassed traditional approaches in terms of speed and accuracy. Well-funded start-ups have recently exploited these advances offering easy to use tools aimed at enterprises.
Dr Thomas Fink, Director, London Institute for Mathematical Sciences
Input-output maps are prevalent throughout science and technology. They are empirically observed to be biased towards simple outputs, but we don't understand why. To address this puzzle, we study the archetypal input-output map: a deep-layered machine in which every node is a Boolean function of all the nodes below it. We give an exact theory for the distribution of outputs, and we confirm our predictions through extensive computer experiments. As the network depth increases, the distribution becomes exponentially biased towards simple outputs.
High-performance computing is becoming increasingly heterogeneous, from the spread of different GPU families, to the rise of new specialised accelerators, in particular related to the machine learning domain.
In this talk we will cover some of the tools available to do high-performance computing with the Julia programming language: from distributed computing with MPI, to accelerating numerical code on GPUs, with particular focus on vendor-agnostic solutions such as KernelAbstractions.jl, to ensure portability.
This seminar summarises PhD research that develops a methodology for scalable neuromorphic control, linking the modelling of individual neuromorphic elements to the design of large event-based control networks. At the element level, it introduces a kernel-based framework for learning fading-memory conductance dynamics from data while preserving causality, memristive structure, and time-scale separation. At the network level, it develops rebound Winner-Take-All motifs that unify rhythmic generation and discrete decision-making in a hierarchical architecture.
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. We are proposing novel methods of program compilation, transformation and scheduling that take advantage of the entire system so that computation takes place in the most appropriate place at the most propitious time.
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.
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/Jx73BwGykJuem4wE7. Sign-up is possible from Mar 12 midday (12pm) until Mar 16 midday or until we reach full capacity, whichever is earlier. If you successfully signed up, we will confirm your appointment by Mar 18 midday.
Users’ subjective experience of a technology’s transparency plays a pivotal role in human-computer interaction, shaping trust, satisfaction, and technology use. Moreover, as interactive systems become more autonomous and complex, industry and policy increasingly acknowledge users’ growing need to understand what a technology is doing, how it functions, and why it produces certain outcomes. Moving beyond the currently fragmented research landscape, this talk offers a comprehensive perspective on technology transparency.
Sarah Bowling, PhD. Assistant Professor in the Department of Developmental Biology at Stanford University School of Medicine
Speaker: Sarah Bowling, Ph.D.
Assistant Professor in the Department of Developmental Biology at Stanford University School of Medicine
Title: “How life finds a way: resilience in mammalian embryogenesis”
Abstract: TBC
Short bio: Dr. Sarah Bowling is an Assistant Professor in the Department of Developmental Biology at Stanford University School of Medicine. Her laboratory focuses on understanding the mechanisms governing resilience in mammalian embryogenesis - i.e. determining how embryos withstand and recover from diverse genetic and environmental perturbations.
When designing complex systems, we need to consider multiple trade-offs at various abstraction levels and scales, and choices of single components need to be studied jointly. For instance, the design of future mobility solutions (e.g., autonomous vehicles, micromobility) and the design of the mobility systems they enable are closely coupled. Indeed, knowledge about the intended service of novel mobility solutions would impact their design and deployment process, while insights about their technological development could significantly affect transportation management policies.
Professor Thomas G. Dietterich, School of EECS, Oregon State University
Exogenous state variables and rewards can slow reinforcement learning by injecting uncontrolled variation into the reward signal. In this talk, I’ll describe our work on formalizing exogenous state variables and rewards. Then I’ll discuss our main result: if the reward function decomposes additively into endogenous and exogenous components, the MDP can be decomposed into an exogenous Markov Reward Process (based on the exogenous reward) and an endogenous Markov Decision Process (optimizing the endogenous reward).
