Dr Muneeb Ahmad

Senior Lecturer, Computer Science

+44 (0) 1792 604640

m.i.ahmad@swansea.ac.uk

Research Links

Available For Postgraduate Supervision

About

Dr. Muneeb Ahmad joined the Department of Computer Science as Lecturer at Swansea University in 2020. His research focuses on employing methods from the field of human-centred design to create intelligent interactive systems such as social robots, and user interfaces that can adapt to users through real-time analysis of their social signals and physiological data. He is interested in the applications of machine learning to implement intelligent interactive systems. The work further involves evaluation of these interactive systems to study their impact on human behaviour mainly to see their level of social engagement, cognitive load, perceived trust, and performance. Prior to joining Swansea University, He has worked as a Research Associate at the Edinburgh Centre for Robotics in the Department of Computer Science at the Heriot-Watt University on the EPSRC ORCA-Hub project, where he led the development of a measurement of Cognitive load in the Human-Robotic/Computer Interaction.

Areas Of Expertise

Human-Robot Interaction,
Human-Computer Interaction,
Human-Agent Interaction,
AI/Machine Learning for Human-Robot Interaction

Publications

Research Highlights

Ahmad, M., Alzahrani, A., & Robinson, S. (2022). Exploring Factors Affecting User Trust Across Different Human-Robot Interaction Settings and Cultures. In HAI '22 - Proceedings of the 10th International Conference on Human-Agent Interaction (pp. 123-131). ACM.
https://doi.org/10.1145/3527188.3561920, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60811
Ahmad, M. & RAFIK, R. (2022). “No Chit Chat!” A Warning From a Physical Versus Virtual Robot Invigilator: Which Matters Most?. Frontiers in Robotics and AI, 9
https://doi.org/10.3389/frobt.2022.908013, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60247
Lohan, K., Ahmad, M., Dondrup, C., Ardón, P., Pairet, È., & Vinciarelli, A. (2020). Adapting Movements and Behaviour to Favour Communication in Human-Robot Interaction. Modelling Human Motion (pp. 271-297). Springer International Publishing
https://doi.org/10.1007/978-3-030-46732-6_13, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa57767
Mubin, O., Wijayarathne, J., Ahmad, M., Ginige, A., & Hewapathirana, R. (2016). Using Persuasive Mobile Apps to Enhance Children's Health and Well Being. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing SystemsACM.
https://doi.org/10.1145/2851581.2892447, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa57766
Ahmad, M., Mubin, O., & Orlando, J. (2016). Understanding Behaviours and Roles for Social and Adaptive Robots In Education. In Proceedings of the Fourth International Conference on Human Agent Interaction (pp. 297-304). ACM.
https://doi.org/10.1145/2974804.2974829, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa57765

All Research

Journal Articles

Ahmad, M. & RAFIK, R. (2022). “No Chit Chat!” A Warning From a Physical Versus Virtual Robot Invigilator: Which Matters Most?. Frontiers in Robotics and AI, 9
https://doi.org/10.3389/frobt.2022.908013, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60247
Ahmad, M., Gao, Y., Alnajjar, F., Shahid, S., & Mubin, O. (2021). Emotion and memory model for social robots: a reinforcement learning based behaviour selection. Behaviour & Information Technology, 2021, 1-27.
https://doi.org/10.1080/0144929x.2021.1977389, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa57746
Mubin, O., Cappuccio, M., Alnajjar, F., Ahmad, M., & Shahid, S. (2020). Can a robot invigilator prevent cheating?. AI & SOCIETY, 35(4), 981-989.
https://doi.org/10.1007/s00146-020-00954-8, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa57756
Ahmad, M., Keller, I., Robb, D., & Lohan, K. (2020). A framework to estimate cognitive load using physiological data. Personal and Ubiquitous Computing
https://doi.org/10.1007/s00779-020-01455-7, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa56610
Ahmad, M., Mubin, O., Shahid, S., & Orlando, J. (2019). Robot’s adaptive emotional feedback sustains children’s social engagement and promotes their vocabulary learning: a long-term child–robot interaction study. Adaptive Behavior, 27(4), 243-266.
https://doi.org/10.1177/1059712319844182, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa57754
Mubin, O., Tejlavwala, D., Arsalan, M., Ahmad, M., & Simoff, S. (2018). An assessment into the characteristics of award winning papers at CHI. Scientometrics, 116(2), 1181-1201.
https://doi.org/10.1007/s11192-018-2778-7, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa57757
Ahmad, M., Mubin, O., & Orlando, J. (2017). Adaptive Social Robot for Sustaining Social Engagement during Long-Term Children–Robot Interaction. International Journal of Human–Computer Interaction, 33(12), 943-962.
https://doi.org/10.1080/10447318.2017.1300750, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa55334
Ahmad, M., Mubin, O., & Orlando, J. (2017). A Systematic Review of Adaptivity in Human-Robot Interaction. Multimodal Technologies and Interaction, 1(3), 14
https://doi.org/10.3390/mti1030014, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa55333
Mubin, O., Mahmud, A., & Ahmad, M. (2017). HCI down under: reflecting on a decade of the OzCHI conference. Scientometrics, 112(1), 367-382.
https://doi.org/10.1007/s11192-017-2293-2, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa57755

Book Chapters

Lohan, K., Ahmad, M., Dondrup, C., Ardón, P., Pairet, È., & Vinciarelli, A. (2020). Adapting Movements and Behaviour to Favour Communication in Human-Robot Interaction. Modelling Human Motion (pp. 271-297). Springer International Publishing
https://doi.org/10.1007/978-3-030-46732-6_13, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa57767

Conference Contributions

Ahmad, M., Alzahrani, A., & Robinson, S. (2022). Exploring Factors Affecting User Trust Across Different Human-Robot Interaction Settings and Cultures. In HAI '22 - Proceedings of the 10th International Conference on Human-Agent Interaction (pp. 123-131). ACM.
https://doi.org/10.1145/3527188.3561920, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60811
Ahmad, M., Robb, D., Keller, I., & Lohan, K. (2020). Towards a Multimodal Measure for Physiological Behaviours to Estimate Cognitive Load. In International Conference on Human-Computer Interaction: Engineering Psychology and Cognitive Ergonomics. Mental Workload, Human Physiology, and Human Energy (pp. 3-13). Springer International Publishing.
https://doi.org/10.1007/978-3-030-49044-7_1, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa57758
Robb, D., Ahmad, M., Tiseo, C., Aracri, S., McConnell, A., Page, V., Dondrup, C., Garcia, F., Nguyen, H., Pairet, È., Ramírez, P., Semwal, T., Taylor, H., Wilson, L., Lane, D., Hastie, H., & Lohan, K. (2020). Robots in the Danger Zone: Exploring Public Perception through Engagement. In Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (pp. 93-102). ACM.
https://doi.org/10.1145/3319502.3374789, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa57759
Ahmad, M. & Mubin, O. (2018). Emotion and Memory Model to Promote Mathematics Learning - An Exploratory Long-term Study. In Proceedings of the 6th International Conference on Human-Agent Interaction (pp. 214-221). ACM.
https://doi.org/10.1145/3284432.3284451, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa57760
Mubin, O., Ahmad, M., Kaur, S., Shi, W., & Khan, A. (2018). Social Robots in Public Spaces: A Meta-review. In Social Robotics (pp. 213-220). Springer International Publishing.
https://doi.org/10.1007/978-3-030-05204-1_21, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa57761
Mubin, O., Manalo, M., Ahmad, M., & Obaid, M. (2017). Scientometric Analysis of the HAI Conference. In Proceedings of the 5th International Conference on Human Agent Interaction-45). ACM.
https://doi.org/10.1145/3125739.3125747, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa57762
Ahmad, M., Shahid, S., & Tahir, A. (2017). Towards the Applicability of NAO Robot for Children with Autism in Pakistan. In Human-Computer Interaction – INTERACT 2017 (pp. 463-472). Springer International Publishing.
https://doi.org/10.1007/978-3-319-67687-6_32, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa57763
Ahmad, M., Mubin, O., & Orlando, J. (2016). Children views' on social robot's adaptations in education. In Proceedings of the 28th Australian Conference on Computer-Human Interaction - OzCHI '16 (pp. 145-149). ACM Press.
https://doi.org/10.1145/3010915.3010977, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa57764
Ahmad, M., Mubin, O., & Orlando, J. (2016). Understanding Behaviours and Roles for Social and Adaptive Robots In Education. In Proceedings of the Fourth International Conference on Human Agent Interaction (pp. 297-304). ACM.
https://doi.org/10.1145/2974804.2974829, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa57765
Mubin, O., Wijayarathne, J., Ahmad, M., Ginige, A., & Hewapathirana, R. (2016). Using Persuasive Mobile Apps to Enhance Children's Health and Well Being. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing SystemsACM.
https://doi.org/10.1145/2851581.2892447, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa57766

Supervision

Novel human-in-the-loop 3D point cloud completion for sub sea surveying (current)

PhD

Other supervisor: Prof Mark Jones

Other supervisor: Dr Gary Tam
Social Robots for Older Adults’ Mental Well-being. (current)

PhD

Other supervisor: Dr Deepak Sahoo
Developing Novel Machine Learning Techniques with Human-in-the-Loop Approach to Enable Better Decision Making on Operations Maintenance (current)

PhD

Other supervisor: Prof Cinzia Giannetti
Expanding and Assembling Approaches to Improve Decisions on Identification and Classification of Online Terrorist Content (current)

PhD

Other supervisor: Dr Bertie Muller
Measuring Trust in Real-time During Human-Robot Interaction (current)

PhD

Other supervisor: Dr Simon Robinson
Recommender algorithms and user preferences on social media platforms«br /»«br /»«br /» (Examining algorithms and filter bubbles in relation to user preferences on social media platforms, in enhancing user wellbeing)«br /»«br /»«br /» (current)

PhD

Other supervisor: Dr Matt Roach

Taught Modules

CSC349 User Experience

This module presents an overview of concepts, design for and evaluation of User Experience (UX). Students will come to understand what UX is, why the concept has become so prominent, what differentiates UX from aesthetic design, and what UX practitioners do in their work. The module will examine specific design approaches, and the student will gain some practical skills applying these techniques. Techniques that will be covered include brainstorming, personas and participatory design. The module will also cover the research methods that are employed to understand specific experiences with technology and impart the beginnings of the skills needed to employ these methods and evaluate the results. This module develops practical skills through the coursework and theoretical understanding is demonstrated in the exam. The module builds on skills developed in the second year HCI module but this is not a pre-requisite for it.
CSCM39 Human Computer Interaction

This module gives an overview on the main topics in Human Computer Interaction and helps students understand research and research processes in Human Computer Interaction. Students explore the advanced literature and research results underpinning the field of HCI. Classic papers and controversies are covered, as well as recent work from the leading figures. Students achieve a clear view of the 'cutting edge' and issues in the field and where things are happening. The module is very interactive, and students will be expected to give presentations.
CSDM001 Theory of Digital Information and Society

This module gives an overview on theoretical and methodological debates contemporary Digital Economy and Society research with specific focus on Human Computer Interaction. This module helps students understand how human experience can influence the design and adoption of digital into services and the lives of individuals and communities.Students explore the advanced literature and research results underpinning the field. Understand, through a series of Classic papers, the practical application of qualitative and quantitative techniques for the study of soci-technical assemblages in a digital by default world, as well as recent work from the leading figures. Students achieve a clear view of the 'cutting edge' and issues in the field and where things are happening. The module is very interactive, and students will be expected to give presentations.

Dr Muneeb Ahmad

Telephone number

Email address

Twitter Account

Research Links

About

Areas Of Expertise

Publications

Research Highlights

All Research

Journal Articles

Book Chapters

Conference Contributions

Supervision

Novel human-in-the-loop 3D point cloud completion for sub sea surveying (current)

Social Robots for Older Adults’ Mental Well-being. (current)

Developing Novel Machine Learning Techniques with Human-in-the-Loop Approach to Enable Better Decision Making on Operations Maintenance (current)

Expanding and Assembling Approaches to Improve Decisions on Identification and Classification of Online Terrorist Content (current)

Measuring Trust in Real-time During Human-Robot Interaction (current)

Recommender algorithms and user preferences on social media platforms«br /»«br /»«br /» (Examining algorithms and filter bubbles in relation to user preferences on social media platforms, in enhancing user wellbeing)«br /»«br /»«br /» (current)

Taught Modules

CSC349 User Experience

CSCM39 Human Computer Interaction

CSDM001 Theory of Digital Information and Society