Hamiltonian Monte Carlo (HMC) and its variants are among the most widely used algorithms for sampling from probability distributions. Despite their popularity, quantitative convergence guarantees for unadjusted HMC remain limited, especially in divergences that provide strong relative-density control such as KL divergence and Rényi divergence. In this talk, we establish regularization properties for unadjusted HMC via one-shot couplings, which enable Wasserstein convergence guarantees to be upgraded to guarantees in KL and Rényi divergence.
Rohit Bhattacharya, Assistant Professor, Williams College, USA
A recurring question in network studies is whether two connected units resemble each other because one influenced the other (contagion) or because they were alike due to unmeasured background conditions (latent confounding, of which homophily is the canonical case). These are famously hard to separate from a single observed network.
Standard model-free reinforcement learning (RL) algorithms, built around the incremental updating of state-action-values, have long served as a dominant computational framework for understanding reward-guided behaviour. Traditionally, such models are hand-crafted: a scientist defines the model architecture and learning rules based on theoretical assumptions, then fits a small number of free parameters to behavioural data.
This talk presents my recent research on the use of Large Language Models in combination with structured sources of evidence, such as explainable AI signals, semantic sampling strategies, hypothesis spaces, and knowledge graphs. The main goal is to show how LLM-based systems can move beyond text generation toward more reliable, interpretable, and evidence-grounded reasoning for opinion analysis, problem diagnosis, and decision support.
Large Language Models (LLMs) are rapidly transforming the way organizations extract knowledge from complex and large-scale data. Beyond conversational applications, these models provide powerful capabilities for information extraction, classification, summarization, reasoning, and automated report generation across diverse domains. This talk presents recent research on the use of LLMs for real-world data analytics, focusing on applications in social media analysis, disaster monitoring, cybersecurity, and healthcare.
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/oKKFG78k4CrcE6JK6. Sign-up is possible from June 4 midday (12pm) until June 8 midday or until we reach full capacity, whichever is earlier. If you successfully signed up, we will confirm your appointment by June 10 midday.
Stephan Druskat, Software Engineering Researcher - Humboldt-Universität zu Berlin
The importance of high-quality software for modern research is clear to us as RSEs. While the central role of software is also increasingly acknowledged by the wider research community, software is still not treated on par with traditional research outputs, in that it is still not cited regularly and formally per default. This negatively affects RSEs, who may struggle to build a career in an environment where the high-impact journal paper is still regarded as the prime proxy for excellence in research.
Abstract: Explainable AI (XAI) works on providing explanations that justify a model's behaviour or decision. But what is an explanation worth if the user it is meant for cannot understand it? “Social XAI”, a recent interdisciplinary offshoot at the intersection of XAI, dialogue research, and the social sciences (Rohlfing et al. 2021, 2026), shifts the focus to the practice of explaining: the dialogic process through which explanations and their understanding are co-constructed between explainer and explainee.
Professor Yinqing Li, The IDG/McGovern Institute for Brain Research, Tsinghua University & Gurdon Institute, University of Cambridge (Sabbatical Visitor)
The Careers Beyond Academia Seminar Series provides PhD students and Early Career Researchers with realistic, experience-based insights into career pathways outside academia. Through invited talks from professionals working across industry and organisations, the series helps researchers understand how to successfully transition their skills and expertise into impactful roles beyond the university environment.
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/HdHM5kKYuxcdRPzr6. Sign-up is possible from June 18 midday (12pm) until June 22 midday or until we reach full capacity, whichever is earlier. If you successfully signed up, we will confirm your appointment by June 24 midday.
