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Job Title
SCiML Group Leader
Office Phone Number
Office Location
RAL R116,FirstFloor
Mobile Phone Number


Dr. Jeyan Thiyagalingam BScEng(Hons), PhD (Imperial), DIC, FBCS, FHEA, SMIEEE, MIET

Head of the Scientific Machine Learning (SciML) Group

Prior to joining STFC-RAL,  I was a faculty in the school of Electrical Engineering and​ Electronics and Computer Sciences at the University of Liverpool. Prior to that, I was based at MathWorks; and at the University of Oxford both as a post-doctoral researcher and later as a James Martin Fellow focusing on high performance computing, and Big data processing. I am also a member of the HiPEAC Network. I have a very strong background in high performance computing, scientific data processing algorithms, signal processing and machine learning. ​ I am also a Turing Fellow.​

 Current (External) Engagements:



  • ​S. Pinilla, S. Yeung, and J. Thiyagalingam, ​ Global Optimality for non-linear constrained Image Restoration via Invexity, Accepted for International Conference on Machine Learning (ICML), 2024. 
  • S. Pinilla, S. Yeung, and J. Thiyagalingam, iADMM: Global Convergence of Alternating Direction Method of Multipliers for Invex Objective Losses, Accepted for International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2024. 
  • K. Leng, and J. Thiyagalingam, Unsupervised Few-shot Image Segmentation with Dense Feature Learning and Sparse Clustering, Accepted for International Conference on Computer Vision, Imaging and Computer Graphics Theory and Application (VISAPP), 2024.


  • K. Leng, and J. Thiyagalingam, Padding-free Convolution based on Preservation of Differential Characteristics of Kernels, International Conference on Machine Learning and Applications (ICMLA), 2023.
  • G. von Laszewski, J.P. Fleischer, G. C. Fox, J.Papay, S. L. Jackson, J. Thiyagalingam, Templated Hybrid Reusable Computational Analytics Workflow Management with Cloudmesh, Applied to the Deep Learning MLCommons Cloudmask Application, Proceedings of the eScience'23, 2023. ​
  • J. Cha, J. Thiyagalingam, Learning Disentangled Representations in Autoencoders by Introducing Orthogonality in the Latent Space, Proceedings of the 40th International Conference on Machine Learning (ICML), PMLR 202:3913-3948, 2023. 
  • A. S. Anker, K. T.  Butler, M. D. Le, T. G. Perring, J. Thiyagalingam, Using generative adversarial networks to match experimental and simulated inelastic neutron scattering data,  Digital Discovery, 2023 (2), 578-590, 2023.
  • J. Wu, T. Mu, J. Thiyagalingam, Y.  Goulermas, Memory-Aware Attentive Control for Community Question Answering With Knowledge-Based Dual Refinement, IEEE Transactions on Systems, Man, and Cybernetics: Systems. p. 1-14, 2023.
  • K. Choudhary,  B. DeCost, L. Major, K. T. Butler, J. Thiyagalingam, F. Tavazza, Unified graph neural network force-field for the periodic table: solid state applications, Digital Discovery, 2023.
  • H. Hu, R. Ye, J. Thiyagalingam. et al. Triple-kernel gated attention-based multiple instance learning with contrastive learning for medical image analysis. Appl Intelligence, 2023.
  • J.  Wu, T.  Mu, J. Thiyagalingam, J. Y. Goulermas, Memory-Aware Attentive Control for Community Question Answering With Knowledge-Based Dual Refinement, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 1-14, 2023​.​​​


  • ​K. Leng, S. King, T. Snow, S. Rogers, A. Markvardsen, S. Maheswaran, and J. Thiyagalingam, Parameter Inversion of a Polydisperse System in Small-angle Scattering, Journal of Applied Crystallography, (55)​, 966-977, 2022.​​
  • L. Lucie-Smith, H. V. Peiris, A. Pontzen, B. Nord, J. Thiyagalingam, and D. Piras, Discovering the building blocks of dark matter halo density profiles with neural networks, Physical Review D, 105, 103533, 2022.
  • J. Thiyagalingam , M. Shankar , G. C. Fox , and T. Hey, Scientific Machine Learning Benchmarks, Nature Reviews Physics, (4), 413–420, 2022.
  • B. Henghes,  J. Thiyagalingam, C. Pettitt, T. Hey, O. Lahav, Deep Learning Methods for Obtaining Photometric Redshift Estimations from Images, Monthly Notices of Royal Astronomical Society (MNRAS), 512(2), 1696–1709, 2022.
  • ​​Y. Huang, T. G. Fleming, S. J. Clark, S. Marussi, K. Fezzaa, J. Thiyagalingam, C. L. A.  Leung, P. D. Lee, Keyhole fluctuation and pore formation mechanisms during laser powder bed fusion additive manufacturing, Nature Communications, (13), 1170, 2022.
  • S. Pinilla and T. Mu and N. Bourne and J. Thiyagalingam, Improved Imaging by Invex Regularizers with Global Optima Guarantees, Advances in Neural Information Processing Systems (NeurIPS), 2022.
  • D. White,  M. Jahangi​r, M. Antoniou, C. Baker, J. Thiyagalingam, S. Harman and C. Bennett, Multi-rotor Drone Micro-Doppler Simulation Incorporating Genuine Motor Speeds and Validation with L-band Staring Radar, IEEE Radar Conference, 2022​.​
  • A. Saoulis, K. Baker, S. Basak, H. Cavanagh, J. Cha, J. Thiyagalingam, R. Williamson, Using Surrogate Models to Assist Accelerator Tuning at ISIS, 13th International Particle Accelerator Conference (IPAC'22), 1133-1136, 2022.
  • K. Baker, S. Basak, J. Cha, I. Finch, S. Lawrie, A. Saoulis, J. Thiyagalingam,  Using Surrogate Models to Assist Accelerator Tuning at ISIS, 13th International Particle Accelerator Conference (IPAC'22), 969-972, 2022. 


  • B. Henghes, C. Pettitt, J. Thiyagalingam, T. Hey, O. Lahav, Benchmarking and Scalability of Machine Learning Methods for Photometric Redshift Estimation, Monthly Notices of Royal Astronomical Society (MNRAS),  4847-4856, 505 (4), 2021.
  • J. Allotey,  K. T. Butler, J. Thiyagalingam, Entropy-based active learning of graph neural network surrogate models for materials properties, Journal of Chemical Physics 155, 174116, 2021​.
  • S. Malhotra, A. P Joseph, J. Thiyagalingam, M. Topf, Assessment of protein-protein interfaces in cryo-EM derived assemblies, Nature Communications, 12, 3399 (2021).
  • C. Wang, F. Yu, Y. Liu, X. Li, J. Thiyagalingam and A. Sepe, Deploying the Big Data Science Center at the Shanghai Synchrotron Radiation Facility: The First Superfacility Platform in China, IOP Machine Learning for Science and Technology, 2(2021), 035003, 2021.
  • K. T. Butler, M. D. Le, J. Thiyagalingam, and T. G. Perring, Interpretable, calibrated neural networks for analysis and understanding of inelastic neutron scattering data, Journal of Physics: Condensed Matter, 33(19),  194006, 2021.
  • K. Adámek, J. Novotný, J. Thiyagalingam, W. Armour, Efficiency Near the Edge: Increasing the Energy Efficiency of FFTs on GPUs for Real-time Edge Computing, IEEE Access,  9(2021),  18167-18182, 2021.
  • Z. Zhang, K. Cumanan, J.  Thiyagalingam, Y.  Tang, W. Wang, Exploiting Deep Learning for Secure Transmission in an Underlay Cognitive Radio Network, IEEE Transactions on Vehicular Technology,  70(1), 726-741, 2021.
  • S. Yeung, R. Tharmarasa, J. Thiyagalingam, A Parallel Retrodiction Algorithm for Large-Scale Multitarget Tracking, IEEE Transactions on Aerospace and Electronic Systems, 57(1), 5-21 ,2021
  • B. Akbari, J. Thiyagalingam, R. Lee, T. Kirubarajan, A multilane tracking algorithm using IPDA with Intensity Feature, Sensors, 21(2), 461 ,2021.
  • Y.  Huang, C. L. A Leung, S. J.  Clark, S. Yeung, Y. Chen, L. Sinclair, S. Marussi, K. Fezzaa, J. Thiyagalingam, P. D.  Lee, *In situ X-ray Observation and Quantification of Keyhole-induced Porosity during Laser Additive Manufacturing, Symposium on Additive Manufacturing of Metals: Applications of Solidification Fundamentals*,  TMS Annual Meeting & Exhibition, 2021.​​


  • T. Hey, J.  Thiyagalingam, M. Winn, M. Vollmar, AI for Science at Large-Scale Experimental Facilities, Crystallography News, December 2020.
  • H.  Al-Obiedollah, K. Cumanan, J. Thiyagalingam, J. Tang, A. G. Burr, Z. Ding, and O. A. Dobre, Spectral-Energy Efficiency Trade-off-based Beamforming Design for MISO Non-Orthogonal Multiple Access Systems, Transactions on Wireless Communications, Accepted for Publications, June 2020.
  • J.  Wu, T.  Mu,  J. Thiyagalingam, J. Y. Goulermas, Building Interactive Sentence-aware Representation based on Generative Language Model for Community Question Answering, Neurocomputing, Volume 389, 93-107, January 2020. 
  • T. Hey, K. T.  Butler, S. L.  Jackson and J. Thiyagalingam, Machine Learning and Big Scientific Data, Philosophical Transactions of the Royal Society A, 378 (2020), 20190054,, 2020. 

2019 & Pre-2019

  • X. Gao, T. Mu, J. Y. Goulermas, J. Thiyagalingam, Meng Wang, An Interpretable Deep Architecture for Similarity Learning Built Upon Hierarchical Concepts, IEEE Transactions on Image Processing, Accepted for Publication, December 2019. 
  • M. Zhang, K. Cumanan, J. Thiyagalingam, W. Wang, A. G. Burr, Z. Ding, and O. A. Dobre, Energy Efficiency Optimization for Secure Transmission in MISO Cognitive Radio Network with Energy Harvesting, IEEE Access, Accepted for Publicaiton, August 2019. 
  • Y. Zuo, R. Tharmarasa, R. Zargani, N. Kashyap, J. Thiyagalingam, T. Kirubarajan, MILP Formulation for Aircraf tPath Planning in Persistent Surveillance, IEEE Transactions on Aerospace and Electronic Systems, Accepted for Publication, August 2019.
  • S. Siso, W. Armour, J. Thiyagalingam, Evaluating Auto-Vectorizing Compilers through Objective Withdrawal of Useful Information, ACM Transactions on Architecture and Code Optimization, Accepted for Publication, August 2019. 
  • H. Al-Obiedollah, K. Cumanan, J. Thiyagalingam, A. G. Burr, Z. Ding, O. A. Dobre,  Energy Efficient Beamforming Design for MISO Non-Orthogonal Multiple Access Systems, IEEE Transactions on Communications, July Accepted for Publication, 2019 
  • F. Zhang, P. Zhao, J. Thiyagalingam, T. Kirubarajan, Terrain-Influenced, Incremental Watchtower Expansion for Wildfire Detection, Science of the Total Environment, Volume 654, 1 March 2019, Pages 164-176.
  • Y. Xie, J. Xiao, K. Huang, J. Thiyagalingam, Y. Zhao, Correlation Filter Selection for Visual Tracking Using Reinforcement Learning, IEEE Transactions on Circuits and Systems for Video Technology, Accepted, December 2018 
  • B. Yang, J. Wang, J. Thiyagalingam and T. Kirubarajan, Multi-Object Bayesian Filter with Amplitude Information in Clutter Background, Elsevier Journal of Signal Processing, Volume 152, November 2018, Pages 22-34 
  • H. Zhou, J. Huang, Feng Lu, J. Thiyagalingam and T.  Kirubarajan, Echo State Kernel Recursive Least Squares Algorithm for Machine Condition Prediction, Mechanical Systems and Signal Processing, Volume 111, October 2018, Pages 68-86.
  • F. Zhang, J. Thiyagalingam, T. Kirubarajan, S. Xu, Speed-Adaptive Multi-Copy Routing for Vehicular Delay Tolerant Networks, Future Generation Computer Systems, 2018. 
  • S. Zhang, J. Thiyagalingam, W. Sheng, T. Kirubarajan, X, Ma, Low Complexity Adaptive Broadband Beamforming Based on the Non-Uniform Domain Decomposition Method, Elsevier Journal of Signal Processing, 66-75, (151), 2018. 
  • ​A. Varsi, K. Lykourgos, J. Thiyagalingam, S. Maskell, Parallelising Particle Filters with Deterministic Runtime on Distributed Memory Systems, Accepted, IEE Conference on Intelligent Signal Processing, 2017
  • H. Fang, J. Thiyagalingam, N. Bessis, E. Edirisinghe, Fast and Reliable Human Action Recognition In Video Sequences By Sequential Analysis, IEEE Conference on Intelligent Image Processing, 2017​
  • J. Thiyagalingam, L. Kekampanos and S. Maskell, Exact Resampling-based MapReduce Particle Filter, EURASIP Journal of Signal Processing, Nov 2017 
  • B. Duffy, J. Thiyagalingam, S. Walton, A. E. Trefethen, D. J. Smith, J. C. Kirkman-Brown, E. A. Gaffney and M. Chen, Glyph-Based Video Visualization for Semen Analysis, IEEE Transactions on Visualisation and Computer Graphics, 
  • S. Walton, K. Berger, J. Thiyagalingam, B. Duffy, H. Fang, C. Holloway, Anne E. Trefethen, Min Chen, Visualizing Cardiovascular Magnetic Resonance (CMR) Imagery: Challenges and Opportunities, Journal of Progress in Biophysics and Molecular Biology, 115(2-3):349-58, 2014 
  • T. Nissen-Meyer, J. Thiyagalingam, M. van Driel, S. Staehler, HPC-scaling and energy consumption of the global seismic wave-propagation code AxiSEM, Geophysical Research Abstracts, Vol. 16, 2014
  • J. Guo, B. Bernecky, J. Thiyagalingam, S. Scholz, Polyhedral Methods for Improving Parallel Update-in-Place, IMPACT 2014, Proceedings of the 4th International Conference in Polyhedral Compilation Techniques, pages 1-9,  2014.​
  • A. Solernou, J. Thiyagalingam,  M. Duta,  A. Trefethen , The Effect of Topology-Aware Process and Thread Placement on Performance and Energy, International Conference in Supercomputing, 2013, Vol. 7905, pages 357-371,  Lecture Notes in Computer Science, 2013.​
  • J. Thiyagalingam, B. Duffy, S. Walton, M. Chen, Complexity Plots, Computer Graphics Forum and EuroVis2013,  32(3), 111-120, 2013. 
  • G.R. Mudalige, M.B. Giles, J. Thiyagalingam, I. Reguly, C. Bertolli, P.H.J. Kelly, A.E. Trefethen,  Design and Performance of a High-level Unstructured Mesh Framework on Heterogeneous Parallel Systems, Journal of Parallel Computing,  39(11), 669 - 692, 2013. 
  • A. Trefethen, J. Thiyagalingam, Energy-Aware Software: Challenges, Opportunities and Strategies, Journal of Computational Sciences, 4(6), 444–449, 2013.
  • J. Thiyagalingam, A. Trefethen, Mathematical Libraries and Energy Efficiency,  SIAM Conference on Parallel Processing for Scientific Computing (SIAM PP12), Savanah, Georgia, February 2012.
  • J. Thiyagalingam, D. Goodman, J. Schnabel, A. Trefethen, V. Grau, On the usage of GPUs for Efficient Motion Estimation in Medical Image Sequences,  Journal of Biomedical Imaging, Volume 2011, Article 137604, pages 1:1-1:15, 2011.
  • J. Thiyagalingam, A. Trefethen, Towards Developing Energy-Aware Algorithms, Proceedings of the Many Core, Reconfigurable Supercomputing (MRSC 2011), Bristol, UK, 2011.
  • J. Guo, W. Rodrigues, J. Thiyagalingam, F. Guyomarch, S.B. Scholz, P. Boulet, Harnessing the Power of GPUs without Losing Abstractions in   SaC and ArrayOL: A Comparative Study, Proceedings of the HIPS/IPDPS 2011, pages 1183 – 1190, 2011
  • J. Guo, J. Thiyagalingam, S.B. Scholz, Breaking the GPU Programming Barrier with the Auto-Parallelising SAC Compiler, pages 15-24, DAMP 2011.
  • J. Guo, J. Thiyagalingam, S.B.Sholz, Towards Compiling SaC to CUDA, Trends in Functional Programming, Vol. (10), Pages 33-48, 2010.
  • M. Duta,  J.Thiyagalingam, A. Trefethen, A.Goyal,V. Grau, N. Smith, Parallel Simulation for Parameter Estimation of Optical Tissue Properties, Lecture Notes In Computer Sciences Vol. 6271, pages 51 62, Euro-Par 2010.​
  • T. Weigold, P. Buhler, J. Thiyagalingam, A. Basukoski, V. Getov, Advanced Grid Programming with Components: A Biometric Identification Case Study, Proceedings of the 32nd IEEE International Conference on Computer Software and Applications (COMPSAC) 2008, pages 401-408, 2008.
  • J, Thiyagalingam J., V. Getov, S. Panagiotidi, O. Beckmann, J. Darlington. Domain-Specific Metadata for Model Validation and Performance Optimisation. Achievements in European Research on Grid Systems,  pages. 165-178, 2007.
  • J. Thiyagalingam. O. Beckmann, Paul H. J. Kelly, Minimising Associativity Conflicts in Morton Layout, Vol. 3911, Sixth International Conference On Parallel Processing And Applied Mathematics, PPAM-2005.
  • J. Thiyagalingam. P. H. J. Kelly, Is Morton Layout Competitive for Large Two Dimensional Arrays? Lecture Notes In Computer Sciences Vol. 2400, pages 280 288, Euro-Par 2002, Paderborn, Germany.
  • J. Thiyagalingam. O. Beckmann, P. H. J. Kelly, Improving the Performance of Basic Morton Layout by Array Alignment and Loop Unrolling, Lecture Notes in Computer Sciences,  Vol. 2400, pages 241-257, LCPC 2003, Texas,
  • J. Thiyagalingam. O. Beckmann, P. H. J. Kelly, Exhaustive Evaluation of Row-major, Column-major and  Morton Layouts for Large Two-Dimensional Arrays,  Proceedings of the UKPEW 2003, pp 340—351. UKPEW 2003, Warwick, England.
  • J. Thiyagalingam. S. Isaiadis, V. Getov, Towards Building a Generic Grid Services Platform: A Component Oriented Approach, In Component Models and Systems for Grid Applications, Proceedings of the ICS 2004, France, pp 39—56, ICS-2004, St. Malo, France.
  • A. Basukoski, V. Getov, J. Thiyagalingam, Component-Based Development Environment For Grid: Design And Implementation, To appear in Lecture Notes of Computer Science, The Proceedings of the Annual Core-Grid, 2007, Heraklion, Greece.
  • J. Thiyagalingam, V. Getov, A Metadata Extraction Tool for Software Components in Grid Applications, Proceedings of the IEEE Conference in Modern Computing Systems, Sofia, Bulgaria. 2006 .pp 189-196.
  • J. Thiyagalingam, N. Parlavantzas, S. Isaiadis, L. Henrio, D. Caromel, V. S. Getov, Proposal for a Lightweight Generic Grid Platform Architecture, Proceedings of the IEEE Symposium on High Performance Distributed Computing 2006,  Paris, France.​​
  • Thiyagalingam, J., Beckmann, O. and Kelly, P. H. J, Is Morton layout competitive for large two-dimensional arrays yet?. Journal of Concurrency and Computation: Practice and Experience, 18: 1509–1539, 2006.​