research
∙
05/27/2023
On random number generators and practical market efficiency
Modern mainstream financial theory is underpinned by the efficient marke...
share
research
∙
03/24/2023
Physics-informed neural networks in the recreation of hydrodynamic simulations from dark matter
Physics-informed neural networks have emerged as a coherent framework fo...
research
∙
12/10/2020
Hybrid analytic and machine-learned baryonic property insertion into galactic dark matter haloes
While cosmological dark matter-only simulations relying solely on gravit...
research
∙
05/18/2020
Ridges in the Dark Energy Survey for cosmic trough identification
Cosmic voids and their corresponding redshift-aggregated projections of ...
research
∙
02/24/2020
Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning
Standard methods and theories in finance can be ill-equipped to capture ...
research
∙
07/06/2019
Filaments of crime: Informing policing via thresholded ridge estimation
Objectives: We introduce a new method for reducing crime in hot spots an...
research
∙
05/23/2019
Gaussbock: Fast parallel-iterative cosmological parameter estimation with Bayesian nonparametrics
We present and apply Gaussbock, a new embarrassingly parallel iterative ...
research
∙
12/23/2018
Stress testing the dark energy equation of state imprint on supernova data
This work determines the degree to which a standard Lambda-CDM analysis ...
research
∙
11/27/2018
Lagged correlation-based deep learning for directional trend change prediction in financial time series
Trend change prediction in complex systems with a large number of noisy ...
research
∙
11/07/2018
