Publications


Journal papers and preprints

  • Caceres, H. E. and Moews, B. (2024), “Evaluating utility in synthetic banking microdata applications”, working paper (arxiv)

  • Ibikunle, G. et al. (2024), “Can machine learning unlock new insights into high-frequency trading?”, working paper (arxiv)

  • Pessi, P. J. et al. (2024), “ELEPHANT: Extragalactic alert pipeline for hostless astronomical transients”, Astronomy & Astrophysics, Vol. 691, pp. A181 (journal | arXiv)

  • Moews, B. (2024), “On random number generators and practical market efficiency”, Journal of the Operational Research Society, Vol. 75(5), pp. 907-920 (journal | arXiv)

  • Dai, Z. et al. (2024), “Physics-informed neural networks in the recreation of hydrodynamic simulations from dark matter”, Monthly Notices of the RAS, Vol. 527(2), pp. 3381-3394 (journal | arxiv)

  • Moews, B. and Gieschen, A. (2023), “SCADDA: Spatio-temporal cluster analysis with density-based distance augmentation and its application to fire carbon emissions”, working paper (arxiv)

  • Zhang, T. et al. (2023), “Photometric redshift uncertainties in weak gravitational lensing shear analysis: Models and marginalization”, Monthly Notices of the RAS, Vol. 518(1), pp. 709-723 (journal | arxiv)

  • Moews, B. et al. (2021), “Hybrid analytic and machine-learned baryonic property insertion into galactic dark matter haloes”, Monthly Notices of the RAS, Vol. 504(3), pp. 4024-4038 (journal | arxiv)

  • Moews, B. et al. (2021), “Filaments of crime: Informing policing via thresholded ridge estimation”, Decision Support Systems, Vol. 144, pp. 113518 (journal | arXiv)

  • Moews, B. et al. (2021), “Ridges in the Dark Energy Survey for cosmic trough identification”, Monthly Notices of the RAS, Vol. 500(1), pp. 859-870 (journal | arxiv)

  • Moews, B. and Zuntz, J., (2020), “Gaussbock: Fast parallel-iterative cosmological parameter estimation with Bayesian nonparametrics”, The Astrophysical Journal, Vol. 896(2), pp. 98 (journal | arXiv)

  • Moews, B. and Ibikunle, G., (2020), “Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning”, Physica A, Vol. 547, pp. 124392 (journal | arxiv)

  • Boucaud, A. et al. (2020), “Photometry of high-redshift blended galaxies using deep learning”, Monthly Notices of the RAS, Vol. 491(2), pp. 2481-2495 (journal | arXiv)

  • Giblin, B. et al. (2019), “On the road to per cent level accuracy II: Calibration of the non-linear matter power spectrum for arbitrary cosmologies”, Monthly Notices of the RAS, Vol. 490(4), pp. 4826-4840 (journal | arXiv)

  • Moews, B. et al. (2019), “Stress testing the dark energy equation of state imprint on supernova data”, Physical Review D, Vol. 99, pp. 123529 (journal | arXiv)

  • Fussell, L. and Moews, B. (2019), “Forging new worlds: High-resolution synthetic galaxies with chained generative adversarial networks”, Monthly Notices of the RAS, Vol. 485(3), pp. 3203-3214 (journal | arXiv)

  • Cantat-Gaudin, T. et al. (2019), “Gaia DR2 unravels incompleteness of nearby cluster population: New open clusters in the direction of Perseus”, Astronomy & Astrophysics, Vol. 624, pp. A126 (journal | arXiv)

  • Moews, B. et al. (2019), “Lagged correlation-based deep learning for directional trend change prediction in financial time series”, Expert Systems with Applications, Vol. 120, pp. 197-206 (journal | arXiv)

 

Research data products

  • Pessi, P. J. et al. (2024), “COIN/Fink transient discovery report” (multiple), Transient Name Server Transient Report, 2024-2379 onwards (NASA ADS | TNS)

  • Pessi, P. J. et al. (2024), “Hostless candidates detected by ELEPHANT” (multiple), Transient Name Server AstroNote, 2024-153 onwards (NASA ADS | TNS)

  • Moews, B. et al. (2020), “VizieR Online Data Catalog: Ridges in the Dark Energy Survey”, VizieR Online Data Catalog, J/MNRAS/500/859 (NASA ADS | VizieR)

  • Cantat-Gaudin, T. et al. (2019), “VizieR Online Data Catalog: New open clusters in Perseus direction”, VizieR Online Data Catalog, J/A+A/624/A126 (NASA ADS | VizieR)

 

Conference presentations

  • “Spatio-temporal analysis of variable-density clusters in hot spot policing”, 33rd Eur. Conference on OR (EURO 2024), Copenhagen, Denmark, June-July 30-3, 2024

  • “Data sensitivity and risk adjustment in high-dimensional predictive clustering of operating fundamentals”, 3rd Edinburgh Conference on the Economics of FinTech (EFT 2024), Edinburgh, UK, June 19-21, 2024

  • “Spatio-temporal crime analysis and the impact of COVID-19 on hot spots”, Royal Statistical Society 2023 International Conference (RSS 2023), Harrogate, UK, September 4-7, 2023

  • “Physics-informed deep learning with distributional loss for complete hydrodynamic simulations”, National Astronomy Meeting of the Royal Astronomical Society (NAM2023), Cardiff, UK, July 3-7, 2023

  • “Time-varying information efficiency and the impact of small inefficient market subsets”, 2nd Edinburgh Conference on the Economics of FinTech (EFT 2023), Edinburgh, UK, June 21-23, 2023

  • “Public safety planning and health policy impacts on crime hot spot composition”, 1st Digital Footprints Conference of the Alan Turing Institute, Bristol, UK, May 11, 2023

  • “Exploiting geospatial large-scale structure for crime prevention”, 22nd International Conference of the International Federation of OR Societies (IFORS 2021), Seoul, Korea, August 22-27, 2021

  • “Parallelized ridge estimation as a predictive tool for Part I crime coverage in narrow route envelopes”, 31st Eur. Conference on OR (EURO 2021), Athens, Greece, July 11-14, 2021

  • “Policing route optimization via density-based principal curves”, 13th International Conference on Computational and Methodological Statistics (CMStatistics 2020), London, UK, December 19-21, 2020

  • “Machine learning frameworks to inpaint baryonic properties in N-body simulations”, AI and Benchmarking in Astrophysics, EPSRC ExCALIBUR Programme (online), December 7-8, 2020

  • “Ridges across fields: From criminology to cosmology”, Benchmarking for AI for Science at Exascale (BASE), EPSRC ExCALIBUR Programme (online), September-October 28-2, 2020

  • “Deep-learning for predictive correlations in stable and volatile markets”, 1st Edinburgh Conference on the Economics of FinTech (EFT 2020), Edinburgh, UK, postponed due to COVID‑19

  • “Ridge-estimating DES”, 16th Durham-Edinburgh eXtragalactic Workshop: 2020 Vision - Progress and Tensions in Astronomy (DEX-XVI), Durham, UK, January 6-7, 2020

  • “How random are intraday stock prices? Evidence from deep learning”, 3rd Eur. Capital Markets Cooperative Research Center Workshop, Dublin, Ireland, July 5, 2019

  • “Deep-learned baryons: Hybrid emulators for high-speed cosmological simulations”, National Astronomy Meeting of the Royal Astronomical Society (NAM2019), Lancaster, UK, June-July 30-4, 2019

  • “Deep learning for portfolio risk and financial economics: Investigating trend change predictability through lagged correlations”, 30th Eur. Conference on OR (EURO 2019), Dublin, Ireland, June 23-26, 2019

  • “Synthetic datasets for modern cosmology: Creating galaxies with multi-stage GANs”, Artificial Intelligence Methods in Cosmology (AICosmo2019), Ascona, Switzerland, June 9-12, 2019

  • “What we might miss: Stress-testing measurements of dark energy”, 5th Joint Meeting of the German Consortium in Statistics (DAGStat 2019), Munich, Germany, March 18-22, 2019

  • “Cosmology and beyond: Solutions for high-dimensional parameter estimation”, The Data Lab: Data Innovation in Scotland (DataTech19), Edinburgh, UK, March 14, 2019

  • “Synthetic galaxies with chained deep learning models”, 15th Durham-Edinburgh eXtragalactic Workshop: Recent Innovations in Theory and Observations (DEX-XV), Edinburgh, UK, January 7-8, 2019

  • “Massively parallel iterative Bayesian nonparametrics for cosmological parameter estimation”, Royal Statistical Society 2018 International Conference (RSS 2018), Cardiff, UK, September 3-6, 2018

  • “High-dimensional posterior sampling with expensive likelihoods”, International Society for Bayesian Analysis 2018 World Meeting (ISBA 2018), Edinburgh, UK, June 24-29, 2018

  • “Gaussbock: Fast parallel-iterative cosmological parameter estimation”, Statistical Challenges in 21st Century Cosmology (Cosmo21), Valencia, Spain, May 22-25, 2018

  • “Non-parametric Bayesian methods for cosmological parameter estimation”, Statistical Challenges for Large-Scale Structure in the Era of LSST (SCLSS), Oxford, UK, April 18-20, 2018