Journal papers and preprints

  • Moews, B. et al. (2019), “Filaments of crime: Informing policing via thresholded ridge estimation”, submitted to Journal of Quantitative Criminology (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”, accepted for publication in Monthly Notices of the RAS (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)

  • Moews, B. and Zuntz, J., (2019), “Gaussbock: Fast parallel-iterative cosmological parameter estimation with Bayesian nonparametrics”, submitted to The Astrophysical Journal (arXiv)

  • Boucaud, A. et al. (2019), “Photometry of high-redshift blended galaxies using deep learning”, submitted to Monthly Notices of the RAS (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)

  • 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)

  • 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)

Conference presentations

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

  • Talk: “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

  • Talk: “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

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

  • Talk: “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

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

  • Talk: “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

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

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

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

  • Talk: “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