Our Google Scholar profile is under maintenance. Please take a look at some of the recently published papers, books and reports from our members and associates​.​

• K. Leng, S. King, T. Snow, S. Rogers, A. Markvardsen, S. Maheswaran, J. Thiyagalingam, Parameter Inversion of a Polydisperse System in Small-angle Scattering, Journal of Applied Crystallography, (55), 966-977​, 2022.
  
• ​Luisa Lucie-Smith, Hiranya V. Peiris, Andrew Pontzen, Brian Nord, Jeyan Thiyagalingam, and Davide Piras, Discovering the building blocks of dark matter halo density profiles with neural networks,  Physical Review D, 105, 103533, 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.
  
• J. Thiyagalingam , M. Shankar , G. Fox , and T. Hey, Scientific Machine Learning Benchmarks, Nature Reviews Physics, (4), 413-420, 2022.
  
• Y. Huang, T. G. Fleming, S. J. Clark, S. Marussi, K. Fezzaa, J. Thiyagalingam, C. A. Leung, P. D. Lee, Keyhole fluctuation and pore formation mechanisms during laser powder bed fusion additive manufacturing, Nature Communications, (13), Article Number: 1170, 2022.
  
• B.Henghes, C. Pettitt, Jeyan Thiyagalingam, Tony Hey, Ofer Lahav, Benchmarking and Scalability of Machine Learning Methods for Photometric Redshift Estimation, Monthly Notices of Royal Astronomical Society (MNRAS),  4847-4856, 505 (4), 2021.
  
• S. Malhotra, A. Praveen, J. Thiyagalingam, and M. Topf, Assessment of protein-protein interfaces in cryo-EM derived assemblies, Nature Communications, 12, 3399 (2021). 2021.
  
• Chunpeng Wang, Feng Yu, Yiyang Liu, Xiaoyun Li, Jeyan Thiyagalingam and Alessandro 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, 
  
• Gordon S. Blair, et.al,  The Role of Digital Technologies in Responding to the Grand Challenges of the Natural Environment: The Windermere Accord, Patterns, Volume 2, Issue 1, 2021. 
  
• T. Hey, J. Thiyagalingam, M. Winn, M. Vollmar, AI for Science at Large-Scale Experimental Facilities, Crystallography News,  Issue 155, Pages 16-18, December 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,  Pages 93-107, 2020.
  
• X. Gao, T. Mu, J. Y. Goulermas, J. Thiyagalingam and M. Wang, An Interpretable Deep Architecture for Similarity Learning Built Upon Hierarchical Concepts,  IEEE Transactions on Image Processing, Volume 29, pp. 3911-3926, 2020.
  
• T. Hey, A. Trefethen, The Fourth Paradigm 10 Years On. Informatik Spektrum 42, 441–447, 2020.
  
• T. Hey, K. T. Butler, S. Jackson and J. Thiyagalingam, Machine Learning and Big Scientific Data, Philosophical Transactions of the Royal Society A, 378: 20190054, 2019. ​
  
• K. T. Butler, D. W. Davies, H. Cartwright, O. Isayev, and A. Walsh, Machine learning for molecular and materials science, Nature 559, 547 2018.​