School of Computing Science

Events

Students sitting in a lecture theatre

Explore upcoming seminars, guest lectures, workshops, and other events hosted by the School of Computing Science.

Our events bring together students, researchers, industry partners, and the wider community to share ideas, showcase research, and foster collaboration.

Quick links

This Week’s EventsAll Upcoming EventsPast EventsWebapp

This Week’s Events

There are no events scheduled for this week

Upcoming events

10th Summer School and Symposium on Computational Interaction (S³CIX)

Group: Inference, Dynamics and Interaction (IDI)
Speaker: multiple
Date: 20 June, 2026
Time: 09:00 - 16:00
Location: Sir Alwyn Williams Building, 422 Seminar Room

Welcome to the Symposium and Summer School on Computational Interaction! This year we are expanding from a Summer School format to also include a 4 day long academic Symposium. We anticipate about 30 students and 40 academics and invited speakers to attend. There will also be two workshops.

Adaptive and Reliability aware Retrieval-Augmented Generation

Group: Information Retrieval (IR)
Speaker: Payel Santra, Indian Association for the Cultivation of Science (IACS)
Date: 22 June, 2026
Time: 15:00 - 16:00
Location: Sir Alwyn Williams Building, 422 Seminar Room

Abstract:
Retrieval-Augmented Generation (RAG) systems are often built around a single retriever, a single knowledge source, and a fixed processing pipeline applied uniformly to every query. While simple, these assumptions can restrict their effectiveness and efficiency in practice. In this talk, I will present a sequence of contributions that progressively challenge these limitations. First, I will introduce a framework for estimating retrieval quality on a per-query, per-ranker basis, and demonstrate how these estimates can be used as principled fusion weights when combining multiple retrievers. I will then show that quality-aware retriever fusion leads to improved performance at both the retrieval and generation stages, benefiting applications such as retrieval fusion and faithful fact correction. Moving further, I will discuss how combining heterogeneous knowledge sources provides complementary evidence through hierarchical fusion. Finally, I will present DRAG, a dynamic RAG framework that adapts retrieval and generation components to individual queries, assigning lightweight pipelines to simpler queries and more powerful configurations to more challenging ones. The broader message is that effective RAG requires principled decisions about what to retrieve, from where, with which models, and how much effort to spend — all conditioned on the query at hand.

Bio:
Payel Santra is a PhD candidate in Computer Science at the Indian Association for the Cultivation of Science (IACS), Kolkata, advised by Partha Basuchowdhuri from IACS and Debasis Ganguly from UoG. Her research focuses mainly on Information Retrieval and Natural Language Processing. She is particularly interested in query performance prediction, adaptive retrieval systems, and trustworthy AI. Her work investigates how large language models can make effective retrieval and generation decisions on a per-query basis to improve reliability, factuality, and efficiency. She has published in venues including ACL, CIKM, ECIR, and WILEY.

[FATA Internal] Section Meeting

Group: Formal Analysis, Theory and Algorithms (FATA)
Speaker: David Manlove
Date: 23 June, 2026
Time: 15:00 - 16:00
Location: Sir Alwyn Williams Building, 422 Seminar Room

Internal section meeting -- see emails.

Predicting Lakehouse Performance in Clouds AND Augur: Pre-Execution Energy Prediction for Workflow Tasks in Heterogeneous Clusters

Group: Systems Seminars
Speaker: James Nurdin & Kathleen West, University of Glasgow
Date: 30 June, 2026
Time: 14:00 - 15:00
Location: Room 422, Sir Alwyn Williams Building and Zoom

This seminar will feature presentations by James Nurdin and Kathleen West, on two papers accepted at IEEE CLOUD 2026.
 
James' paper:
Title: Predicting Lakehouse Performance in Clouds: An Empirical Exploration of Query Runtime Variance
Abstract:
Data analytics increasingly runs on distributed lakehouse systems, where platform operators must optimise monetary, resource, and environmental costs. Query Performance Prediction (QPP) helps to balance these costs and supports workload management techniques, such as adaptive resource scaling and low-carbon scheduling. However, runtimes in lakehouses can vary substantially, and the impact of runtime variance on QPP accuracy and workload orchestration has not previously been systematically studied for lakehouse systems.
This paper addresses this gap by investigating the runtime variance observed for distributed lakehouse analytical queries and its impact on QPP. First, we quantify the run-to-run variance using Kubernetes deployments across three public clouds and one private cloud, spanning multiple database scales and three analytical benchmarks. Our results demonstrate that repeated executions of the same query can vary in runtime by nearly twofold. Second, we conduct a factor analysis study assessing key sources of this runtime variance such as data locality, co-tenant load, and caching effects. Third, we examine how variance influences state-of-the-art QPP models, revealing that addressing key sources of variance can reduce prediction error up to 80%. Finally, we demonstrate the downstream implications for low-carbon scheduling as an example of a workload management technique that relies on performance prediction, showing that accounting for runtime variance can lead to a significant reduction in carbon costs.
 
Kathleen's paper:
Title: Augur: Pre-Execution Energy Prediction for Workflow Tasks in Heterogeneous Clusters
Abstract: 
Scientific workflows are widely used to process large quantities of data, leading to significant energy consumption and carbon emissions. To reduce this environmental impact, energy and carbon-aware scheduling approaches could be employed. However, such methods require runtime and energy predictions, which are typically only available for workflows that have been executed previously. Meanwhile, scientists may execute new or modified workflows, use workflows with different input data, or run them on alternative infrastructure. To address this critical gap, we propose Augur, a novel method to predict the energy consumption of scientific workflow tasks prior to execution. By efficiently profiling both the available cluster infrastructure and the workflow at hand, Augur is capable of predicting the overall energy consumption of the workflow with a median prediction error of 16.3 ± 15.3% compared to Ichnos, an energy estimation method that uses fitted power models, and 18.2 ± 14.7% compared to Intel RAPL, as observed in our experimental evaluation on public and private cloud infrastructure. Relying on only minimal historical execution data, Augur outperforms two state-of-the-art methods in predicting both task runtime and total workflow energy, providing a robust foundation for energy-efficient and carbon-aware scientific data analysis.

[FATA Seminar] TBA

Group: Formal Analysis, Theory and Algorithms (FATA)
Speaker: Huan Luo, FATA
Date: 30 June, 2026
Time: 15:00 - 16:00
Location: Sir Alwyn Williams Building, 422 Seminar Room

TBA

SPLV’26: Scottish Programming Languages and Verification Summer School 2026

Group: Scottish Informatics and Computer Science Alliance (SICSA)
Speaker: SICSA Event, SICSA
Date: 03 August, 2026
Time: 01:00 - 01:00
Location: TBA

The 2026 edition of SPLV will be held at the University of Glasgow, with the main courses running from within the Gilbert Scott Building. The school is aimed at PhD students in programming languages, verification and related areas. Researchers and practitioners are welcome, as are strong undergraduate and masters students with the support of a supervisor. Participants should have a background in computer science, mathematics or a related discipline. Prospective students may contact the organisers if they have any concerns about background knowledge. Registration will open March 2026. View full programme at SPLV 2026 | SPLV

Past events

To view past events, please click here

Events Webapp