School of Computing Science

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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.

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This Week’s Events

GIST Seminar: What are interpersonal social signals?

Group: Human Computer Interaction (GIST)
Speaker: Prof. Julie R. Williamson
Date: 23 July, 2026
Time: 13:00 - 14:00
Location: SAWB 423, https://teams.microsoft.com/l/meetup-join/19%3ameeting_MzIzZDQ0ZTMtNThiNi00ZTE3LThmODktZmNlYWE1OWY2NmMz%40thread.v2/0?context=%7b%22Tid%22%3a%226e725c29-763a-4f50-81f2-2e254f0133c8%22%2c%22Oid%22%3a%22fa7393ac-5d11-4f81-8064-7aae2b55dec9%22%7d

Dear All,

We’re pleased to invite you to this week’s GIST Seminar, where Prof. Julie Williamson will give a talk on What are interpersonal social signals? Everyone with an interest is warmly welcome to attend!

Seminar Details:

Topic: What are interpersonal social signals?

Date: 23 July 2026 (Thursday)

Time: 13:00 – 14:00

Location: SAWB 423

Online (Teams): https://teams.microsoft.com/l/meetup-join/19%3ameeting_MzIzZDQ0ZTMtNThiNi00ZTE3LThmODktZmNlYWE1OWY2NmMz%40thread.v2/0?context=%7b%22Tid%22%3a%226e725c29-763a-4f50-81f2-2e254f0133c8%22%2c%22Oid%22%3a%22fa7393ac-5d11-4f81-8064-7aae2b55dec9%22%7d

Abstract: 

This talk is an experimental framing of the interpersonal social signals that form a substantial part of the FUSION project methodology.  This will include the theoretical basis for exploring interpersonal communication, the general approach and structure of analysing interpersonal social signals, and the open questions and challenges of this approach.  This format is open for discussion and debate, where the future ambitions of this approach are still to be determined.
 
Bio:
Julie R. Williamson is Professor of Immersive Interaction in the School of Computing Science at the University of Glasgow, where she leads the Future Immersive Interaction Group. Her research focuses on how people experience immersive realities together, combining social signal processing and mixed-methods evaluation to understand interaction in social and public XR settings, including social VR, co-located immersive experiences, and performative interaction in public spaces. She is principal investigator of the ERC-funded FUSION project on future social interaction in XR and has worked extensively with novel interfaces such as large and spherical displays, multimodal interaction, and head-mounted immersive systems.
 
For more information about GIST: GIST Section Website

CVAS: Beyond Patches: Learning Weakly Supervised Vision–Language Representations for Whole-Slide Histopathology

Group: Computer Vision for Autonomous Systems (CVAS)
Speaker: Shatha Alamri
Date: 24 July, 2026
Time: 13:00 - 14:00
Location: SAWB 423, Sir Alwyn Williams Building

Whole-slide histopathology images (WSIs) are gigapixel-scale images used for cancer diagnosis, making them challenging to analyse with deep learning. This work investigates how vision–language models can be adapted to learn meaningful slide-level representations using only slide-level diagnostic labels, without requiring region-level annotations or paired image–text data. The proposed framework combines multiple instance learning with transformer-based aggregation to model contextual interactions among tissue regions and produce a coherent slide representation, which is then aligned with the natural language representation of the slide label in a pathology-pretrained language space. Unlike conventional vision-only slide classifiers, aligning slide representations to this shared image–text embedding transfers diagnostic semantics learned through large-scale pathology image–text pretraining. This enables capabilities beyond label prediction, including classifier-head-free, prompt-based classification and cross-modal retrieval, while requiring only slide-level supervision. Experimental results show that transformer-based slide aggregation, together with pathology-specific vision and language encoders, provides complementary benefits, leading to more discriminative slide representations and consistently improving both prompt-based classification and cross-modal retrieval over patch-based vision–language baselines. These results demonstrate the potential of slide-level vision–language learning for weakly supervised computational pathology.

Upcoming events

GIST Seminar: What are interpersonal social signals?

Group: Human Computer Interaction (GIST)
Speaker: Prof. Julie R. Williamson
Date: 23 July, 2026
Time: 13:00 - 14:00
Location: SAWB 423, https://teams.microsoft.com/l/meetup-join/19%3ameeting_MzIzZDQ0ZTMtNThiNi00ZTE3LThmODktZmNlYWE1OWY2NmMz%40thread.v2/0?context=%7b%22Tid%22%3a%226e725c29-763a-4f50-81f2-2e254f0133c8%22%2c%22Oid%22%3a%22fa7393ac-5d11-4f81-8064-7aae2b55dec9%22%7d

Dear All,

We’re pleased to invite you to this week’s GIST Seminar, where Prof. Julie Williamson will give a talk on What are interpersonal social signals? Everyone with an interest is warmly welcome to attend!

Seminar Details:

Topic: What are interpersonal social signals?

Date: 23 July 2026 (Thursday)

Time: 13:00 – 14:00

Location: SAWB 423

Online (Teams): https://teams.microsoft.com/l/meetup-join/19%3ameeting_MzIzZDQ0ZTMtNThiNi00ZTE3LThmODktZmNlYWE1OWY2NmMz%40thread.v2/0?context=%7b%22Tid%22%3a%226e725c29-763a-4f50-81f2-2e254f0133c8%22%2c%22Oid%22%3a%22fa7393ac-5d11-4f81-8064-7aae2b55dec9%22%7d

Abstract: 

This talk is an experimental framing of the interpersonal social signals that form a substantial part of the FUSION project methodology.  This will include the theoretical basis for exploring interpersonal communication, the general approach and structure of analysing interpersonal social signals, and the open questions and challenges of this approach.  This format is open for discussion and debate, where the future ambitions of this approach are still to be determined.
 
Bio:
Julie R. Williamson is Professor of Immersive Interaction in the School of Computing Science at the University of Glasgow, where she leads the Future Immersive Interaction Group. Her research focuses on how people experience immersive realities together, combining social signal processing and mixed-methods evaluation to understand interaction in social and public XR settings, including social VR, co-located immersive experiences, and performative interaction in public spaces. She is principal investigator of the ERC-funded FUSION project on future social interaction in XR and has worked extensively with novel interfaces such as large and spherical displays, multimodal interaction, and head-mounted immersive systems.
 
For more information about GIST: GIST Section Website

CVAS: Beyond Patches: Learning Weakly Supervised Vision–Language Representations for Whole-Slide Histopathology

Group: Computer Vision for Autonomous Systems (CVAS)
Speaker: Shatha Alamri
Date: 24 July, 2026
Time: 13:00 - 14:00
Location: SAWB 423, Sir Alwyn Williams Building

Whole-slide histopathology images (WSIs) are gigapixel-scale images used for cancer diagnosis, making them challenging to analyse with deep learning. This work investigates how vision–language models can be adapted to learn meaningful slide-level representations using only slide-level diagnostic labels, without requiring region-level annotations or paired image–text data. The proposed framework combines multiple instance learning with transformer-based aggregation to model contextual interactions among tissue regions and produce a coherent slide representation, which is then aligned with the natural language representation of the slide label in a pathology-pretrained language space. Unlike conventional vision-only slide classifiers, aligning slide representations to this shared image–text embedding transfers diagnostic semantics learned through large-scale pathology image–text pretraining. This enables capabilities beyond label prediction, including classifier-head-free, prompt-based classification and cross-modal retrieval, while requiring only slide-level supervision. Experimental results show that transformer-based slide aggregation, together with pathology-specific vision and language encoders, provides complementary benefits, leading to more discriminative slide representations and consistently improving both prompt-based classification and cross-modal retrieval over patch-based vision–language baselines. These results demonstrate the potential of slide-level vision–language learning for weakly supervised computational pathology.

An Empirical Study of Observability Limits in Advanced Software Supply Chain Attacks

Group: Systems Seminars
Speaker: Zhuoran (Newt) Tan, University of Glasgow
Date: 28 July, 2026
Time: 14:00 - 15:00
Location: Room 422, Sir Alwyn Williams Building and Teams

Abstract: Advanced software supply chain attacks often occur only at runtime and leave fragmented evidence across hosts, services, and dependency layers. We present SynthChain, a multi-source runtime dataset with end-to-end ground truth for seven representative supply-chain attack scenarios across PyPI, npm, and C++ environments. The dataset contains approximately 0.59 million events from 11 telemetry types, annotated with MITRE ATT&CK techniques and 2,919 verified indicators of compromise. Our analysis shows that no single telemetry source can reconstruct a complete attack chain, while carefully selected source combinations substantially improve coverage. We further identify key observability failure modes and derive practical guidelines for telemetry planning and runtime defense evaluation.

Bio: Zhuoran (Newt) Tan received the M.Sc. degree in Information Security (with a year in industry) from Royal Holloway, University of London, in 2020, and is completing his Ph.D. degree in Computing Science at the University of Glasgow in 2026. He has held senior research and engineering roles in the cybersecurity industry, with extensive experience in both industrial R&D and academic research. His work spans AI and LLM security, software supply chain security, threat detection, security automation, and runtime analysis. His first-author research has appeared in venues including ACM CCS, IEEE IOTJ, and MSR, with additional work published in workshops affiliated with IEEE ICDCS and FSE. His industry research on MLOps- based anomaly detection and security automation has also been featured by SecuritySenses.

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

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