Dr Matt Roach

Athro Cyswllt, Computer Science

+44 (0) 1792 606662

m.j.roach@abertawe.ac.uk

222
Ail lawr
Y Ffowndri Gyfrifiadol
Campws y Bae

Ar gael ar gyfer Goruchwyliaeth Ôl-raddedig

Dolenni Ymchwil

https://orcid.org/0000-0002-1486-5537

Google Scholar

Trosolwg

Mae Matt yn Uwch-ddarlithydd mewn Cyfrifiadureg ym Mhrifysgol Abertawe. Mae Matt yn ymchwilydd ar brosiectau gwerth £1.5m a ariennir gan Innovate UK, gan gymhwyso dysgu peirianyddol i heriau rheoli traffig dinasoedd clyfar a chanfod twyll. Mae gan Matt brofiad helaeth o sicrhau cyllid ymchwil ac arloesi cydweithredol, ac arwain gweithgareddau ymgysylltu â busnesau: ar y Ffowndri Gyfrifiadol gwerth £31m a Datganiad Data'r EPSRC gwerth £1.2m: Ymddiriedolaeth, prosiectau Hunaniaeth, Preifatrwydd a Diogelwch. Mae ei ddiddordebau ymchwil yn amlddisgyblaethol ac yn canolbwyntio ar groestoriad o bobl a data. Mae'r themâu yn cynnwys: Dysgu Peirianyddol; Tegwch, bias algorithmig; Cyfrifiadura Treiddiol a Pherswadiol; Rhyngweithio rhwng Cyfrifiaduron/Data a phobl; Dylunio cyfranogol; Systemau gwneud penderfyniadau cymdeithasol-dechnegol; Systemau Newid Ymddygiad Cymdeithasol-Dechnegol. Mae prif waith Matt ym meysydd Iechyd a Lles, Dinasoedd Clyfar, yr Economi Ddigidol. Mae ganddo rolau arweinyddiaeth mewn mentrau hyfforddi doethurol: gan ymgynnull Llwybr Economi Ddigidol Partneriaeth Hyfforddiant Doethurol Cymru ESRC Cymru ac mae'n Arweinydd Ymgysylltu Strategol ar gyfer Canolfan Hyfforddiant Doethurol yr EPSRC mewn Gwella Rhyngweithiadau a Chydweithrediadau Dynol â Systemau sydd wedi'u llywio gan Ddeallusrwydd a Data, gwerth £11m. Mae Matt yn aelod o'r ACM. Cyn dod yn academydd amser llawn, cwblhaodd Matt rôl reoli mewn prosiect cyfnewid gwybodaeth, gan wella sgiliau 350 o weithwyr proffesiynol meddalwedd a sgiliau cyfrifiadura 2,900 o berchnogion busnes, gan arwain at gynnydd o £40m mewn GVA (Gwerth Ychwanegol Gros) cenedlaethol. Hefyd, bu'n gweithio am ddegawd ym myd diwydiant fel Sylfaenydd, Cyfarwyddwr a Phrif Swyddog Gweithredol cwmnïau datblygu meddalwedd.

Meysydd Arbenigedd

Dysgu Peirianyddol
Dehongli, Tegwch, Bias Algorithmig
Cyfrifiadura Treiddiol a Pherswadiol
Rhyngweithio rhwng Cyfrifiaduron/Data a phobl
Dylunio cyfranogol
Systemau gwneud penderfyniadau cymdeithasol-dechnegol
Parthau: Iechyd a Lles, Dinasoedd Clyfa

Uchafbwyntiau Gyrfa

Diddordebau Addysgu

Yn ogystal ag addysgu ar y modiwlau isod, mae Matt hefyd yn:

Bennaeth Cyflogadwyedd ar gyfer Cyfrifiadureg.
Arweinydd Cynhadledd Flynyddol y Meistri.
Arweinydd ar gyfer Ffair Prosiect Flynyddol Cyfrifiadureg Israddedig.

Ymchwil Cydweithrediadau

Cyhoeddiadau

Uchafbwyntiau Ymchwil

Alharbi, M., Roach, M., Cheesman, T., & Laramee, B. (2021). VNLP: Visible natural language processing. Information Visualization, 20(4), 245-262.
https://doi.org/10.1177/14738716211038898, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa57876
Zhao, F., Roach, M., Xie, X., & Roach, M. (2015). Computer Vision Techniques for Transcatheter Intervention. IEEE Journal of Translational Engineering in Health and Medicine
https://doi.org/10.1109/JTEHM.2015.2446988, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa22239
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=7128336&queryText%3Dswansea+university
Roach, M., D'Cruze, T., Smith, G., Brookes, P., Laramee, R., Roberts, R., & Roach, M. (2018). A Tale of Two Visions - Exploring the Dichotomy of Interest between Academia and Industry in Visualisation. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (pp. 319-326).
https://doi.org/10.5220/0006635803190326, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa52047
M.J, R., M, P., & Roach, M. (2002). 'Image Characterisation' Patent number WO0239386
https://doi.org/10.1023/A:1021309123214, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa39128
Pierre, M., Matthew, R., John, M., & Roach, M. (2001). Visual Form 2001. Lecture notes in computer science
https://doi.org/10.1007/3-540-45129-3, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa39133
M., R., J.S., M., M., P., & Roach, M. (2001). Motion-based classification of cartoons
https://doi.org/10.1109/ISIMP.2001.925353, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa39142
Roach, M., Brand, J., & Mason, J. (2002). Acoustic and facial features for speaker recognition. In Proceedings 15th International Conference on Pattern Recognition. ICPR-2000IEEE Comput. Soc.
https://doi.org/10.1109/icpr.2000.903534, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa39141
http://dx.doi.org/10.1109/icpr.2000.903534
Roach, M., Brand, J., Mason, J., & Roach, M. (2000). Acoustic and facial features for speaker recognition. In Proceedings - International Conference on Pattern Recognition
SU Repository: https://cronfa.swan.ac.uk/Record/cronfa39140
http://www.scopus.com/inward/record.url?eid=2-s2.0-33750924999&partnerID=MN8TOARS
Roach, M., Mason, J., & Pawlewski, M. (2001). Video genre classification using dynamics. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SU Repository: https://cronfa.swan.ac.uk/Record/cronfa39139
http://www.scopus.com/inward/record.url?eid=2-s2.0-0034853004&partnerID=MN8TOARS
M, J., J.S.D., M., M., P., & Roach, M. (2001). Video Genre Classification using Dynamics
SU Repository: https://cronfa.swan.ac.uk/Record/cronfa39138
Roach, M., Mason, J., Pawlewski, M., & Roach, M. (2001). Motion-based classification of cartoons. In Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2001
SU Repository: https://cronfa.swan.ac.uk/Record/cronfa39137
http://www.scopus.com/inward/record.url?eid=2-s2.0-84872354191&partnerID=MN8TOARS
Brand, J., Mason, J., Roach, M., Pawlewski, M., & Roach, M. (2001). Enhancing face detection in colour images using a skin probability map. In Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2001
SU Repository: https://cronfa.swan.ac.uk/Record/cronfa39136
http://www.scopus.com/inward/record.url?eid=2-s2.0-84872329448&partnerID=MN8TOARS
M, J., John, M., & Roach, M. (2001). Classification of Video Genre using Audio
SU Repository: https://cronfa.swan.ac.uk/Record/cronfa39135
Martin-Granel, P., Roach, M., Mason, J., & Roach, M. (2001). Camera motion extraction using correlation for motion-based video classification
SU Repository: https://cronfa.swan.ac.uk/Record/cronfa39134
http://www.scopus.com/inward/record.url?eid=2-s2.0-84957596092&partnerID=MN8TOARS
Roach, M., Mason, J., Xu, L., & Roach, M. (2002). Video genre verification using both acoustic and visual modes. In Proceedings of 2002 IEEE Workshop on Multimedia Signal Processing, MMSP 2002
https://doi.org/10.1109/MMSP.2002.1203271, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa39132
http://www.scopus.com/inward/record.url?eid=2-s2.0-84942249181&partnerID=MN8TOARS
M, J., John, M., & Roach, M. (2002). Video Genre Verification using both Acoustic and Visual Modes
SU Repository: https://cronfa.swan.ac.uk/Record/cronfa39131
M, J., J.S.D., M., Li-Qun, X., Fred, S., & Roach, M. (2002). Recent trends in video analysis: A taxonomy of video classification problems
SU Repository: https://cronfa.swan.ac.uk/Record/cronfa39130
Matthew, R., Li-qun, X., John, M., Martlesham, H., Ipswich, I., & Roach, M. (2002). Classification of Non-edited Broadcast Video Using Holistic Low-level Features
SU Repository: https://cronfa.swan.ac.uk/Record/cronfa39129
Jain, P., Blandin, A., O'Neill, J., Perry, M., Ibtasam, S., Shahid, S., Chugh, B., Sullivan, D., Candello, H., Pomeroy, J., Jain, R., Dowd, R., Roach, M., & Jones, M. (2022). Platformisation of Digital Financial Services (DFS): The Journey of DFS in the Global North and Global South. In CHI Conference on Human Factors in Computing Systems Extended AbstractsACM.
https://doi.org/10.1145/3491101.3516507, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa59931
Thomas, L., Edwards, M., Capsey, A., Rahat, A., & Roach, M. (2022). Deep Visual Place Recognition for Waterborne Domains. In 2022 IEEE International Conference on Image Processing (ICIP). 16-19th October Bordeaux, France.IEEE.
https://doi.org/10.1109/icip46576.2022.9897962, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60685
https://ieeexplore.ieee.org/xpl/conhome/9897158/proceeding
Lakshmanan, K., Roach, M., Giannetti, C., Bhoite, S., George, D., Mortensen, T., Manduhu, M., Heravi, B., Kariyawasam, S., & Xie, X. (n.d.) A Robust Vehicle Detection Model for LiDAR Sensor Using Simulation Data and Transfer Learning Methods. AI, 4(2), 461-481.
https://doi.org/10.3390/ai4020025, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63515
http://dx.doi.org/10.3390/ai4020025
Doneddu, D., Roach, M., Jones, M., Pearson, J., Blandin, A., & Sullivan, D. (n.d.) A position on establishing effective explanations from human-centred counterfactuals for automated financial decisions. In Berndt Müller (Ed.), Proceedings of the AISB 2023 Convention, Swansea University, Swansea, Wales, April 13-14 2023 (pp. 89-97). AISB.
SU Repository: https://cronfa.swan.ac.uk/Record/cronfa64477
https://aisb.org.uk/wp-content/uploads/2023/05/aisb2023.pdf

Holl Ymchwil

Erthyglau Cylchgronau

Alharbi, M., Roach, M., Cheesman, T., & Laramee, B. (2021). VNLP: Visible natural language processing. Information Visualization, 20(4), 245-262.
https://doi.org/10.1177/14738716211038898, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa57876
Zhao, F., Roach, M., Xie, X., & Roach, M. (2015). Computer Vision Techniques for Transcatheter Intervention. IEEE Journal of Translational Engineering in Health and Medicine
https://doi.org/10.1109/JTEHM.2015.2446988, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa22239
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=7128336&queryText%3Dswansea+university
Pierre, M., Matthew, R., John, M., & Roach, M. (2001). Visual Form 2001. Lecture notes in computer science
https://doi.org/10.1007/3-540-45129-3, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa39133
Lakshmanan, K., Roach, M., Giannetti, C., Bhoite, S., George, D., Mortensen, T., Manduhu, M., Heravi, B., Kariyawasam, S., & Xie, X. (n.d.) A Robust Vehicle Detection Model for LiDAR Sensor Using Simulation Data and Transfer Learning Methods. AI, 4(2), 461-481.
https://doi.org/10.3390/ai4020025, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63515
http://dx.doi.org/10.3390/ai4020025

Llyfrau Awdurol

Martin-Granel, P., Roach, M., Mason, J., & Roach, M. (2001). Camera motion extraction using correlation for motion-based video classification
SU Repository: https://cronfa.swan.ac.uk/Record/cronfa39134
http://www.scopus.com/inward/record.url?eid=2-s2.0-84957596092&partnerID=MN8TOARS

Cyfraniadau Cynadleddau

Thomas, L., Edwards, M., Capsey, A., Rahat, A., & Roach, M. (2022). Deep Visual Place Recognition for Waterborne Domains. In 2022 IEEE International Conference on Image Processing (ICIP). 16-19th October Bordeaux, France.IEEE.
https://doi.org/10.1109/icip46576.2022.9897962, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60685
https://ieeexplore.ieee.org/xpl/conhome/9897158/proceeding
Jain, P., Blandin, A., O'Neill, J., Perry, M., Ibtasam, S., Shahid, S., Chugh, B., Sullivan, D., Candello, H., Pomeroy, J., Jain, R., Dowd, R., Roach, M., & Jones, M. (2022). Platformisation of Digital Financial Services (DFS): The Journey of DFS in the Global North and Global South. In CHI Conference on Human Factors in Computing Systems Extended AbstractsACM.
https://doi.org/10.1145/3491101.3516507, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa59931
Roach, M., D'Cruze, T., Smith, G., Brookes, P., Laramee, R., Roberts, R., & Roach, M. (2018). A Tale of Two Visions - Exploring the Dichotomy of Interest between Academia and Industry in Visualisation. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (pp. 319-326).
https://doi.org/10.5220/0006635803190326, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa52047
Roach, M., Mason, J., Xu, L., & Roach, M. (2002). Video genre verification using both acoustic and visual modes. In Proceedings of 2002 IEEE Workshop on Multimedia Signal Processing, MMSP 2002
https://doi.org/10.1109/MMSP.2002.1203271, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa39132
http://www.scopus.com/inward/record.url?eid=2-s2.0-84942249181&partnerID=MN8TOARS
Roach, M., Brand, J., & Mason, J. (2002). Acoustic and facial features for speaker recognition. In Proceedings 15th International Conference on Pattern Recognition. ICPR-2000IEEE Comput. Soc.
https://doi.org/10.1109/icpr.2000.903534, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa39141
http://dx.doi.org/10.1109/icpr.2000.903534
Roach, M., Mason, J., & Pawlewski, M. (2001). Video genre classification using dynamics. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SU Repository: https://cronfa.swan.ac.uk/Record/cronfa39139
http://www.scopus.com/inward/record.url?eid=2-s2.0-0034853004&partnerID=MN8TOARS
Roach, M., Mason, J., Pawlewski, M., & Roach, M. (2001). Motion-based classification of cartoons. In Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2001
SU Repository: https://cronfa.swan.ac.uk/Record/cronfa39137
http://www.scopus.com/inward/record.url?eid=2-s2.0-84872354191&partnerID=MN8TOARS
Brand, J., Mason, J., Roach, M., Pawlewski, M., & Roach, M. (2001). Enhancing face detection in colour images using a skin probability map. In Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2001
SU Repository: https://cronfa.swan.ac.uk/Record/cronfa39136
http://www.scopus.com/inward/record.url?eid=2-s2.0-84872329448&partnerID=MN8TOARS
Roach, M., Brand, J., Mason, J., & Roach, M. (2000). Acoustic and facial features for speaker recognition. In Proceedings - International Conference on Pattern Recognition
SU Repository: https://cronfa.swan.ac.uk/Record/cronfa39140
http://www.scopus.com/inward/record.url?eid=2-s2.0-33750924999&partnerID=MN8TOARS
Doneddu, D., Roach, M., Jones, M., Pearson, J., Blandin, A., & Sullivan, D. (n.d.) A position on establishing effective explanations from human-centred counterfactuals for automated financial decisions. In Berndt Müller (Ed.), Proceedings of the AISB 2023 Convention, Swansea University, Swansea, Wales, April 13-14 2023 (pp. 89-97). AISB.
SU Repository: https://cronfa.swan.ac.uk/Record/cronfa64477
https://aisb.org.uk/wp-content/uploads/2023/05/aisb2023.pdf

Cynnyrch Ymchwil Arall

M.J, R., M, P., & Roach, M. (2002). 'Image Characterisation' Patent number WO0239386
https://doi.org/10.1023/A:1021309123214, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa39128
M, J., John, M., & Roach, M. (2002). Video Genre Verification using both Acoustic and Visual Modes
SU Repository: https://cronfa.swan.ac.uk/Record/cronfa39131
M, J., J.S.D., M., Li-Qun, X., Fred, S., & Roach, M. (2002). Recent trends in video analysis: A taxonomy of video classification problems
SU Repository: https://cronfa.swan.ac.uk/Record/cronfa39130
Matthew, R., Li-qun, X., John, M., Martlesham, H., Ipswich, I., & Roach, M. (2002). Classification of Non-edited Broadcast Video Using Holistic Low-level Features
SU Repository: https://cronfa.swan.ac.uk/Record/cronfa39129
M., R., J.S., M., M., P., & Roach, M. (2001). Motion-based classification of cartoons
https://doi.org/10.1109/ISIMP.2001.925353, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa39142
M, J., J.S.D., M., M., P., & Roach, M. (2001). Video Genre Classification using Dynamics
SU Repository: https://cronfa.swan.ac.uk/Record/cronfa39138
M, J., John, M., & Roach, M. (2001). Classification of Video Genre using Audio
SU Repository: https://cronfa.swan.ac.uk/Record/cronfa39135

Goruchwyliaeth

Dispelling the 'Fog of War': Contextualising Misinformation in Conflict Zones (cyfredol)

PhD

Goruchwyliwr arall: Dr Nicholas Micallef
The Nefarious use of Out-linking on Twitter. (cyfredol)

PhD

Goruchwyliwr arall: Dr Lella Nouri

Goruchwyliwr arall: Dr Lella Nouri

Goruchwyliwr arall: Dr Alma Rahat
Developing Trust in Algorithmically-Driven Services by Enhancing Explainable and Fair Machine Learning Through Co-Creation and Customer Interactions (cyfredol)

PhD

Goruchwyliwr arall: Prof Jen Pearson

Goruchwyliwr arall: Prof Simon Robinson

Goruchwyliwr arall: Prof Matt Jones
User Centred Design Approach to Designing and Evaluating Fair Unbiased AI-Driven Models in Socio-Technical Decision Support Systems. (cyfredol)

PhD

Goruchwyliwr arall: Prof Alan Dix
Hybrid Human-Algorithm Systems to Support Situated Clinical Decision-Making:«br /»«br /»«br /» A case study in dispatch of pre-hospital critical care (cyfredol)

PhD

Goruchwyliwr arall: Dr Alma Rahat
Beyond Rare Disease Patients: Exploring Machine Learning Interventions To Support People Experiencing a Diagnostic Odyssey (cyfredol)

PhD

Goruchwyliwr arall: Dr Tom Owen
Deep Visual Place Recognition for Shoreline Imagery (cyfredol)

PhD

Goruchwyliwr arall: Dr Alma Rahat
Navigating Filter Bubbles: A Multidimensional Analysis of User Behavior and Cross-Cultural Impacts«br /»«br /»«br /»«br /» «br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /» «br /»«br /»«br /»«br /»«br /»«br /» (cyfredol)

PhD

Goruchwyliwr arall: Dr Muneeb Ahmad
Designing Novel Approaches to Personalise Behaviour Change in Intelligent Systems (dyfarnwyd 2024)

PhD

Goruchwyliwr arall: Dr Stephen Lindsay

Goruchwyliwr arall: Dr Chat Wacharamanotham

Modiwlau a Addysgir

CSC005 Computational Probability

This module gives students an understanding of Monte Carlo methods, which use random simulation to computationally solve physical problems which may be difficult if not impossible to solve analytically. The delivery style of the module is very much tutorial in nature: only basic programming and probability theory will be taught and required, with the module concentrating on practical weekly lab-based problem-solving sessions. The module material will consist of 'bite' sized presentations, and self-paced programming python notebooks.
CSCM10 Computer Science Project Research Methods

This module will introduce students to some fundamental research methodologies and good practice in research. They will undertake background research including a literature review and specify the aims of their MSc project.
CSCM10J Computer Science Project Research Methods

This module will introduce students to some fundamental research methodologies and good practice in research. They will undertake background research including a literature review and specify the aims of their MSc project.
CSCM21 Abuses, Biases and Blessings of Data

This module will take a look at how Fair Machine Learning, Artificial Intelligence and data driven systems are applied in various real-world contexts. It will cover a variety of successful and unsuccessful case studies from academia and industry. Lectures will provide theoretical framework though which students will learn to construct their analysis and evaluate various forms of ML/ AI algorithms for their appropriateness to be deployed in society as decision support systems. Students will be expected to read published literature and contribute to seminars by preparing presentations on new material and take part in reasoned scientific debate on contemporary ethical issues in the design and deployment of data and Intelligent systems. This theoretical understanding and critical analysis will be complemented by honing practical implementation skills by empirically exploring bias in data, ML explanation and fair ML techniques.

Grantiau a Phrosiectau Allweddol

PARSER: "Parking and AiR pollution SEnsoRs" ar gyfer Dinasoedd Clyfar 2019 - 2021

Mae Matt yn Brif Ymchwilydd ar y prosiect ymchwil diwydiannol cydweithredol hwn dan arweiniad Vortex IoT. , £980,000 (£320,000 FEC Abertawe) a ariennir gan Innovate UK
Prosiect Canfod Twyll Cyllid Masnach mewn Nwyddau Defnydd Deuol gyda Dysgu Peirianyddol a Dadansoddeg Weledol 2019 - 2020

Mae Matt yn Gyd-ymchwilydd ar brosiect cydweithredol dan arweiniad Traydstream ynghylch Canfod Twyll ar gyfer y Sector Cyllid , with gyda Xianghua Xie, £500,000 (£172,000 FEC Abertawe). Cydweithredol Innovate UK.
Canolfan Hyfforddiant Doethurol yr EPSRC mewn Gwella Rhyngweithiadau a Chydweithrediadau Dynol â Systemau wedi'u llywio gan Ddata a Deallusrwydd 2020 - 2028

Matt yw Arweinydd Ymgysylltu Strategol y Ganolfan, a fydd yn meithrin ymchwilwyr PhD a all sicrhau bod cymwysiadau data mawr a deallusrwydd peirianyddol yn cael eu hategu gan arloesiadau sy'n blaenoriaethu gwerthoedd, profiad a galluoedd dynol, with gyda Matt Jones, Markus Roggenbach, en Pearson, Simon Robinson, Sherryl Bellfield ac eraill, £11 miliwn Hyfforddiant Doethurol gyda chefnogaeth llawer o noddwyr allanol
Partneriaeth Hyfforddiant Doethurol Cymru yr ESRC 2017

Matt yw Cynullydd llwybr yr Economi Ddigidol a'r Gymdeithas, sy'n canolbwyntio ar hyfforddi gwyddonwyr cymdeithasol ynghylch sut mae anghenion a gwerthoedd dynol yn llywio datblygiadau mewn technoleg ddigidol a'u cymwysiadau., with gyda Matt Jones, Matt Williams (Prifysgol Caerdydd), £500,000 Wedi'i ariannu gan yr ESRC a'i gefnogi gan noddwyr allanol

Swyddi Cysylltiedig

Adolygwr Arbenigol, Rhwydwaith Not-Equal

2019 - Presennol
Aelod, Association for Computing Machinery

2019 - Presennol

Dr Matt Roach

Rhif ffôn

Cyfeiriad ebost

Twitter Account

LinkedIn Account

Dolenni Ymchwil

Trosolwg

Meysydd Arbenigedd

Uchafbwyntiau Gyrfa

Uchafbwyntiau Ymchwil

Holl Ymchwil

Dispelling the 'Fog of War': Contextualising Misinformation in Conflict Zones (cyfredol)

The Nefarious use of Out-linking on Twitter. (cyfredol)

Developing Trust in Algorithmically-Driven Services by Enhancing Explainable and Fair Machine Learning Through Co-Creation and Customer Interactions (cyfredol)

User Centred Design Approach to Designing and Evaluating Fair Unbiased AI-Driven Models in Socio-Technical Decision Support Systems. (cyfredol)

Hybrid Human-Algorithm Systems to Support Situated Clinical Decision-Making:«br /»«br /»«br /» A case study in dispatch of pre-hospital critical care (cyfredol)

Beyond Rare Disease Patients: Exploring Machine Learning Interventions To Support People Experiencing a Diagnostic Odyssey (cyfredol)

Deep Visual Place Recognition for Shoreline Imagery (cyfredol)

Navigating Filter Bubbles: A Multidimensional Analysis of User Behavior and Cross-Cultural Impacts«br /»«br /»«br /»«br /» «br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /» «br /»«br /»«br /»«br /»«br /»«br /» (cyfredol)

Designing Novel Approaches to Personalise Behaviour Change in Intelligent Systems (dyfarnwyd 2024)

CSC005 Computational Probability

CSCM10 Computer Science Project Research Methods

CSCM10J Computer Science Project Research Methods

CSCM21 Abuses, Biases and Blessings of Data

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