Dr Alma Rahat

Athro Cyswllt

Computer Science

a.a.m.rahat@abertawe.ac.uk

112
Llawr Cyntaf
Y Ffowndri Gyfrifiadol
Campws y Bae

Ar gael ar gyfer Goruchwyliaeth Ôl-raddedig

Dolenni Ymchwil

https://orcid.org/0000-0002-5023-1371

ResearchGate

Google Scholar

ResearcherID

Scopus

Trosolwg

Darlithydd mewn Gwyddor Data yw Dr Alma Rahat. Mae'n arbenigwr ym maes chwilio ac optimeiddio Bayesaidd ar gyfer problemau cyfrififiannol drud (er enghraifft, optimeiddio geometreg gan ddefnyddio dynameg hylif cyfrifiannol). Ei arbenigedd penodol yw datblygu swyddogaethau caffael effeithiol ar gyfer problemau unigol ac aml-amcan, a lleoli'r gofod dichonadwy. Mae'n un o bedwar ar hugain aelod Tasglu'r Gymdeithas Deallusrwydd Cyfrifiannol IEEE ar Optimeiddio Problemau Drud a yrrir gan ddata, ac ef fu prif drefnydd y gweithdy Optimeiddio Esblygiadol poblogaidd a Gynorthwyir yn Ddirprwyol yng Nghynhadledd Cyfrifiannu Genetig ac Esblygiadol (GECCO) ers 2016. Mae ganddo hanes cadarn o weithio gyda diwydiant ar amrywiaeth eang o broblemau optimeiddio. Mae ei gydweithrediadau wedi arwain at nifer o erthyglau mewn cyfnodolion a chynadleddau blaenllaw, gan gynnwys papur gorau yn llwybr Real World Applications yn GECCO a phatent.

Mae gan Dr Rahat BEng (Anrh.) mewn Peirianneg Electronig o Brifysgol Southampton, DU, a PhD mewn Cyfrifiadureg o Brifysgol Caerwysg, DU. Gweithiodd fel peiriannydd datblygu cynnyrch ar ôl ei radd baglor, a bu mewn swyddi ymchwil ôl-ddoethurol ym Mhrifysgol Caerwysg. Cyn symud i Abertawe, bu'n Ddarlithydd mewn Cyfrifiadureg ym Mhrifysgol Plymouth, DU.

Uchafbwyntiau Gyrfa

Diddordebau Addysgu

Chwilio ac Optimeiddio Bayesaidd
Algorithmau Esblygiadol
Optimeiddio Aml-amcan
Optimeiddio a Gynorthwir yn Ddirprwyol
Cyfrifiannu wedi'i Ysbrydoli gan Natur
Dysgu Gweithredol
Dysgu Peirianyddol

Cyhoeddiadau

Uchafbwyntiau Ymchwil

Roberts, A., Rahat, A., Jarman, D., Fieldsend, J., & Tabor, G. (2024). Multi-objective Bayesian shape optimization of an industrial hydrodynamic separator using unsteady Eulerian-Lagrangian simulations. Optimization and Engineering, 0
https://doi.org/10.1007/s11081-024-09907-2, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67078
Hotchkiss, L. & Rahat, A. (2023). Addressing the Health Versus Economy Dilemma in Data-Driven Policymaking During a Pandemic. In Proceedings of the Companion Conference on Genetic and Evolutionary ComputationACM.
https://doi.org/10.1145/3583133.3590652, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa64015
http://dx.doi.org/10.1145/3583133.3590652
Gibson, F., Fabbro, R., Rahat, A., Torsney-Weir, T., Archambault, D., Gravenor, M., & Lucini, B. (2021). An interactive tool for enhancing hospital capacity predictions using an epidemiological model. In Francisco Chicano (Ed.), Proceedings of the Genetic and Evolutionary Computation Conference CompanionACM.
https://doi.org/10.1145/3449726.3462738, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa64016
http://dx.doi.org/10.1145/3449726.3462738
Ahmad, M., Alzahrani, A., Robinson, S., & Rahat, A. (2023). Modelling Human Trust in Robots During Repeated Interactions. In International Conference on Human-Agent InteractionACM.
https://doi.org/10.1145/3623809.3623892, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa64046
IONS, K., Rahat, A., Reeve, D., & Karunarathna, H. (2024). Gaussian process regression approach for predicting wave attenuation through rigid vegetation. Applied Ocean Research, 145
https://doi.org/10.1016/j.apor.2024.103935, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65710
Gray, A., Rahat, A., Crick, T., & Lindsay, S. (2024). A Bayesian active learning approach to comparative judgement within education assessment. Computers and Education: Artificial Intelligence, 6, 100245
https://doi.org/10.1016/j.caeai.2024.100245, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa66575
Rahat, A., Chugh, T., Fieldsend, J., Allmendinger, R., & Miettinen, K. (2022). Efficient Approximation of Expected Hypervolume Improvement Using Gauss-Hermite Quadrature. In Lecture Notes in Computer Science (pp. 90-103). Springer International Publishing.
https://doi.org/10.1007/978-3-031-14714-2_7, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60513
Ath, G., Everson, R., Fieldsend, J., & Rahat, A. (2021). Greed Is Good: Exploration and Exploitation Trade-offs in Bayesian Optimisation. ACM Transactions on Evolutionary Learning and Optimization, 1(1), 1-22.
https://doi.org/10.1145/3425501, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa55241
http://dx.doi.org/10.1145/3425501
Ath, G., Chugh, T., & Rahat, A. (2022). MBORE: multi-objective Bayesian optimisation by density-ratio estimation. In Proceedings of the Genetic and Evolutionary Computation ConferenceACM.
https://doi.org/10.1145/3512290.3528769, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60512
Gray, A., Rahat, A., Crick, T., Lindsay, S., Wallace, D., GRAY, A., Rahat, A., & Crick, T. (2022). Using Elo Rating as a Metric for Comparative Judgement in Educational Assessment. In Proceedings of the 6th International Conference on Education and Multimedia Technology (pp. 272-278). ACM.
https://doi.org/10.1145/3551708.3556204, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60788
http://dx.doi.org/10.1145/3551708.3556204

Holl Ymchwil

Erthyglau Cylchgronau

Roberts, A., Rahat, A., Jarman, D., Fieldsend, J., & Tabor, G. (2024). Multi-objective Bayesian shape optimization of an industrial hydrodynamic separator using unsteady Eulerian-Lagrangian simulations. Optimization and Engineering, 0
https://doi.org/10.1007/s11081-024-09907-2, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67078
Gray, A., Rahat, A., Crick, T., & Lindsay, S. (2024). A Bayesian active learning approach to comparative judgement within education assessment. Computers and Education: Artificial Intelligence, 6, 100245
https://doi.org/10.1016/j.caeai.2024.100245, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa66575
IONS, K., Rahat, A., Reeve, D., & Karunarathna, H. (2024). Gaussian process regression approach for predicting wave attenuation through rigid vegetation. Applied Ocean Research, 145
https://doi.org/10.1016/j.apor.2024.103935, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65710
Dykes, J., Abdul-Rahman, A., Archambault, D., Bach, B., Borgo, R., Chen, M., Enright, J., Fang, H., Firat, E., Freeman, E., Gönen, T., Harris, C., Jianu, R., John, N., Khan, S., Lahiff, A., Laramee, R., Matthews, L., Mohr, S...., & Xu, K. (2022). Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 380(2233)
https://doi.org/10.1098/rsta.2021.0299, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa59890
Walton, S., Rahat, A., & Stovold, J. (2022). Evaluating Mixed-Initiative Procedural Level Design Tools Using a Triple-Blind Mixed-Method User Study. IEEE Transactions on Games, 14(3), 413-422.
https://doi.org/10.1109/tg.2021.3086215, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa57073
Ath, G., Everson, R., Fieldsend, J., & Rahat, A. (2021). Greed Is Good: Exploration and Exploitation Trade-offs in Bayesian Optimisation. ACM Transactions on Evolutionary Learning and Optimization, 1(1), 1-22.
https://doi.org/10.1145/3425501, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa55241
http://dx.doi.org/10.1145/3425501
Daniels, S., Rahat, A., Tabor, G., Fieldsend, J., & Everson, R. (2021). Application of multi-objective Bayesian shape optimisation to a sharp-heeled Kaplan draft tube. Optimization and Engineering, 23, 689-716.
https://doi.org/10.1007/s11081-021-09602-6, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa56256
Daniels, S., Rahat, A., Tabor, G., Fieldsend, J., & Everson, R. (2020). Shape optimisation of the sharp-heeled Kaplan draft tube: Performance evaluation using Computational Fluid Dynamics. Renewable Energy, 160, 112-126.
https://doi.org/10.1016/j.renene.2020.05.164, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa54659
Daniels, S., Rahat, A., Tabor, G., Fieldsend, J., & Everson, R. (2019). Automated shape optimisation of a plane asymmetric diffuser using combined Computational Fluid Dynamic simulations and multi-objective Bayesian methodology. International Journal of Computational Fluid Dynamics, 33(6-7), 256-271.
https://doi.org/10.1080/10618562.2019.1683165, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa52662
http://dx.doi.org/10.1080/10618562.2019.1683165
Tabish, T., Pranjol, M., Horsell, D., Rahat, A., Whatmore, J., Winyard, P., & Zhang, S. (2019). Graphene Oxide-Based Targeting of Extracellular Cathepsin D and Cathepsin L As A Novel Anti-Metastatic Enzyme Cancer Therapy. Cancers, 11(3), 319
https://doi.org/10.3390/cancers11030319, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa52250
Rahat, A., Wang, C., Everson, R., & Fieldsend, J. (2018). Data-driven multi-objective optimisation of coal-fired boiler combustion systems. Applied Energy, 229, 446-458.
https://doi.org/10.1016/j.apenergy.2018.07.101, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa52254
https://ore.exeter.ac.uk/repository/handle/10871/33710
Wang, C., Liu, Y., Everson, R., Rahat, A., Zheng, S., & Rahat, A. (2017). Applied Gaussian Process in Optimizing Unburned Carbon Content in Fly Ash for Boiler Combustion. Mathematical Problems in Engineering, 2017, 1-8.
https://doi.org/10.1155/2017/6138930, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa52259
Tabish, T., Pranjol, M., Hayat, H., Rahat, A., Abdullah, T., Whatmore, J., & Zhang, S. (2017). In vitrotoxic effects of reduced graphene oxide nanosheets on lung cancer cells. Nanotechnology, 28(50), 504001
https://doi.org/10.1088/1361-6528/aa95a8, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa54033
http://dx.doi.org/10.1088/1361-6528/aa95a8
Tabish, T., Pranjol, M., Hayat, H., Rahat, A., Abdullah, T., Whatmore, J., & Zhang, S. (2017). In vitrotoxic effects of reduced graphene oxide nanosheets on lung cancer cells. Nanotechnology, 28(50), 504001
https://doi.org/10.1088/1361-6528/aa95a8, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa53203
Tabish, T., Pranjol, M., Hayat, H., Rahat, A., Abdullah, T., Whatmore, J., & Zhang, S. (2017). In vitrotoxic effects of reduced graphene oxide nanosheets on lung cancer cells. Nanotechnology, 28(50), 504001
https://doi.org/10.1088/1361-6528/aa95a8, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa52255
Rahat, A., Everson, R., & Fieldsend, J. (2016). Evolutionary multi-path routing for network lifetime and robustness in wireless sensor networks. Ad Hoc Networks, 52, 130-145.
https://doi.org/10.1016/j.adhoc.2016.08.005, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa52261
Rahat, A., Everson, R., & Fieldsend, J. (2015). Hybrid Evolutionary Approaches to Maximum Lifetime Routing and Energy Efficiency in Sensor Mesh Networks. Evolutionary Computation, 23(3), 481-507.
https://doi.org/10.1162/evco_a_00151, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa52262
http://dx.doi.org/10.1162/evco_a_00151

Penodau Llyfrau

Chugh, T., Rahat, A., & Palar, P. (2020). Trading-off Data Fit and Complexity in Training Gaussian Processes with Multiple Kernels. Machine Learning, Optimization, and Data Science (pp. 579-591). Springer International Publishing
https://doi.org/10.1007/978-3-030-37599-7_48, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa53580
Chugh, T., Rahat, A., Volz, V., & Zaefferer, M. (2020). Towards Better Integration of Surrogate Models and Optimizers. High-Performance Simulation-Based Optimization (pp. 137-163). Springer International Publishing
https://doi.org/10.1007/978-3-030-18764-4_7, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa52249
http://dx.doi.org/10.1007/978-3-030-18764-4_7

Cyfraniadau Cynadleddau

Morgan, M., Rahat, A., Jenkins, G., & Zhang, J. (2024). Explainable Machine Learning: Predicting Clinical Outcomes in Welsh Emergency Departments. In Lecture Notes in Computer Science (pp. 290-301). Springer Nature Switzerland.
https://doi.org/10.1007/978-3-031-67285-9_21, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67432
Ahmad, M., Alzahrani, A., Robinson, S., & Rahat, A. (2023). Modelling Human Trust in Robots During Repeated Interactions. In International Conference on Human-Agent InteractionACM.
https://doi.org/10.1145/3623809.3623892, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa64046
Sadler, S., Rahat, A., Walker, D., & Archambault, D. (2023). Extrema Graphs: Fitness Landscape Analysis to the Extreme!. In GECCO 2023: The Genetic and Evolutionary Computation Conference, Lisbon. July 15-19 2023.ACM (Association for Computing Machinery).
https://doi.org/10.1145/3583133.3596343, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63398
Hotchkiss, L. & Rahat, A. (2023). Addressing the Health Versus Economy Dilemma in Data-Driven Policymaking During a Pandemic. In Proceedings of the Companion Conference on Genetic and Evolutionary ComputationACM.
https://doi.org/10.1145/3583133.3590652, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa64015
http://dx.doi.org/10.1145/3583133.3590652
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
Rahat, A., Chugh, T., Fieldsend, J., Allmendinger, R., & Miettinen, K. (2022). Efficient Approximation of Expected Hypervolume Improvement Using Gauss-Hermite Quadrature. In Lecture Notes in Computer Science (pp. 90-103). Springer International Publishing.
https://doi.org/10.1007/978-3-031-14714-2_7, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60513
Gray, A., Rahat, A., Crick, T., Lindsay, S., Wallace, D., GRAY, A., Rahat, A., & Crick, T. (2022). Using Elo Rating as a Metric for Comparative Judgement in Educational Assessment. In Proceedings of the 6th International Conference on Education and Multimedia Technology (pp. 272-278). ACM.
https://doi.org/10.1145/3551708.3556204, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60788
http://dx.doi.org/10.1145/3551708.3556204
Ath, G., Chugh, T., & Rahat, A. (2022). MBORE: multi-objective Bayesian optimisation by density-ratio estimation. In Proceedings of the Genetic and Evolutionary Computation ConferenceACM.
https://doi.org/10.1145/3512290.3528769, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60512
Gibson, F., Fabbro, R., Rahat, A., Torsney-Weir, T., Archambault, D., Gravenor, M., & Lucini, B. (2021). An interactive tool for enhancing hospital capacity predictions using an epidemiological model. In Francisco Chicano (Ed.), Proceedings of the Genetic and Evolutionary Computation Conference CompanionACM.
https://doi.org/10.1145/3449726.3462738, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa64016
http://dx.doi.org/10.1145/3449726.3462738
Rahat, A. & Wood, M. (2021). On Bayesian Search for the Feasible Space Under Computationally Expensive Constraints. In Machine Learning, Optimization, and Data Science (pp. 529-540). Springer International Publishing.
https://doi.org/10.1007/978-3-030-64580-9_44, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa55060
Ath, G., Everson, R., Fieldsend, J., & Rahat, A. (2020). ϵ-shotgun: ϵ-greedy batch bayesian optimisation. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference (pp. 787-795). ACM.
https://doi.org/10.1145/3377930.3390154, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa54660
Palar, P., Zuhal, L., Chugh, T., & Rahat, A. (2020). On the Impact of Covariance Functions in Multi-Objective Bayesian Optimization for Engineering Design. In AIAA Scitech 2020 ForumAmerican Institute of Aeronautics and Astronautics.
https://doi.org/10.2514/6.2020-1867, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa54034
Rahat, A., Ledbetter, R., Dawn, A., Byrne, R., & Everson, R. (2019). Solving the first and last mile problem with connected and autonomous vehicles. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 397-398). ACM.
https://doi.org/10.1145/3319619.3321919, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa52252
Daniels, S., Rahat, A., Everson, R., Tabor, G., & Fieldsend, J. (2018). A Suite of Computationally Expensive Shape Optimisation Problems Using Computational Fluid Dynamics. In Parallel Problem Solving from Nature – PPSN XV (pp. 296-307).
https://doi.org/10.1007/978-3-319-99259-4_24, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa52253
Rahat, A., Everson, R., & Fieldsend, J. (2017). Alternative infill strategies for expensive multi-objective optimisation. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 873-880). ACM.
https://doi.org/10.1145/3071178.3071276, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa52258
http://dx.doi.org/10.1145/3071178.3071276
Rahat, A., Everson, R., & Fieldsend, J. (2014). Multi-objective routing optimisation for battery-powered wireless sensor mesh networks.
https://doi.org/10.1145/2576768.2598311, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa52263

Cynnyrch Ymchwil Arall

Jarman, D., Daniels, S., Rahat, A., Fieldsend, J., Everson, R., & Tabor, G. (2020). Separator for Separating Solids From a Fluid
SU Repository: https://cronfa.swan.ac.uk/Record/cronfa54032
http://www.freepatentsonline.com/y2020/0108333.html

Goruchwyliaeth

Untitled (cyfredol)

PhD

Goruchwyliwr arall: Dr Sean Walton
Novel uses of counterfactuals for improving Post-Hoc explanations (cyfredol)

PhD

Goruchwyliwr arall: Dr Bertie Muller
Adaptive and Explainable AI for Predicting Patient Presentations in NHS Emergency Departments. (cyfredol)

PhD

Goruchwyliwr arall: Prof Jiaxiang Zhang
Artificial Intelligence assisted decadal scale beach change forecasting (cyfredol)

PhD

Goruchwyliwr arall: Prof Harshinie Karunarathna
A People-First Bayesian Approach for Decision Making in Quality Control. (cyfredol)

PhD
Using efficient reinforcement learning and Bayesian optimisation to create robust train planning solutions (cyfredol)

PhD

Goruchwyliwr arall: Dr Sean Walton
Using Small Data to Improve Co-Creative Writing Tools (cyfredol)

PhD

Goruchwyliwr arall: Dr Amanda Rogers

Goruchwyliwr arall: Prof Matt Jones
Alternative Ranking Methods for Comparative Judgement: Making Marking more Affordable for Educators (cyfredol)

PhD

Goruchwyliwr arall: Prof Tom Crick
The Nefarious use of Out-linking on Twitter. (cyfredol)

PhD

Goruchwyliwr arall: Dr Lella Nouri

Goruchwyliwr arall: Dr Lella Nouri

Goruchwyliwr arall: Dr Matt Roach
ML-guided dynamical systems modelling of sepsis (cyfredol)

PhD

Goruchwyliwr arall: Dr Noemi Picco
Wave Hydrodynamics and Sediment Transport inside«br /» Coastal Plant Canopies (cyfredol)

PhD

Goruchwyliwr arall: Prof Dominic Reeve

Goruchwyliwr arall: Prof Harshinie Karunarathna
Hybrid Human-Algorithm Systems to Support Situated Clinical Decision-Making: A case study in dispatch of pre-hospital critical care (cyfredol)

PhD

Goruchwyliwr arall: Dr Matt Roach
Deep Visual Place Recognition for Shoreline Imagery (cyfredol)

PhD

Goruchwyliwr arall: Dr Matt Roach

Goruchwyliwr arall: Dr Matt Roach

Modiwlau a Addysgir

CS-165 Introduction to Data Science

We live in the age of data: it is ubiquitous in modern life. However, data in itself cannot inform or inspire. We need to ¿do science¿ with the data to extract knowledge and actionable insights. In this module, we will explore scientific methods and processes that make data so valuable to us and society and gain an insight into the world of practical data science and its challenges. We will also cover the ethical issues relating to data.
CSC372 Optimisation

Optimisation is at the core of many disciplines. Whether we want to improve the performance of a machine learning model, increase the efficiency of an aircraft design, or simply reduce the costs of productions in a business operation, we must deploy computational optimisation methods for achieving the best results. In this module, we will cover mathematical and algorithmic fundamentals of optimisation, including derivative and derivative-free approaches for both linear and non-linear problems. We will also discuss advanced topics, such as multi-objective optimisation, handling uncertainty, principled methods when problem evaluations are computationally expensive, and performance comparison between stochastic optimisers, in the context of real-world problems.
CSCM72 Optimisation

Optimisation is at the core of many disciplines. Whether we want to improve the performance of a machine learning model, increase the efficiency of an aircraft design, or simply reduce the costs of productions in a business operation, we must deploy computational optimisation methods for achieving the best results. In this module, we will cover mathematical and algorithmic fundamentals of optimisation, including derivative and derivative-free approaches for both linear and non-linear problems. We will also discuss advanced topics, such as multi-objective optimisation, handling uncertainty, principled methods when problem evaluations are computationally expensive, and performance comparison between stochastic optimisers, in the context of real-world problems.

Dr Alma Rahat

Cyfeiriad ebost

Youtube Account

LinkedIn Account

Dolenni Ymchwil

Trosolwg

Uchafbwyntiau Gyrfa

Cyhoeddiadau

Uchafbwyntiau Ymchwil

Holl Ymchwil

Erthyglau Cylchgronau

Penodau Llyfrau

Cyfraniadau Cynadleddau

Cynnyrch Ymchwil Arall

Goruchwyliaeth

Untitled (cyfredol)

Novel uses of counterfactuals for improving Post-Hoc explanations (cyfredol)

Adaptive and Explainable AI for Predicting Patient Presentations in NHS Emergency Departments. (cyfredol)

Artificial Intelligence assisted decadal scale beach change forecasting (cyfredol)

A People-First Bayesian Approach for Decision Making in Quality Control. (cyfredol)

Using efficient reinforcement learning and Bayesian optimisation to create robust train planning solutions (cyfredol)

Using Small Data to Improve Co-Creative Writing Tools (cyfredol)

Alternative Ranking Methods for Comparative Judgement: Making Marking more Affordable for Educators (cyfredol)

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

ML-guided dynamical systems modelling of sepsis (cyfredol)

Wave Hydrodynamics and Sediment Transport inside«br /» Coastal Plant Canopies (cyfredol)

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

Deep Visual Place Recognition for Shoreline Imagery (cyfredol)

Modiwlau a Addysgir

CS-165 Introduction to Data Science

CSC372 Optimisation

CSCM72 Optimisation