Data Driven
Data-driven approaches are revolutionizing scientific research and engineering by leveraging vast datasets to build predictive models and automate complex tasks. Current research focuses on developing and refining algorithms like neural networks (including transformers and graph neural networks), Gaussian processes, and ADMM for diverse applications, ranging from autonomous systems and financial forecasting to scientific discovery and healthcare. This shift towards data-centric methodologies promises to accelerate scientific progress and improve the efficiency and effectiveness of various technological systems, particularly in areas where traditional modeling approaches are limited by complexity or data scarcity.
Papers
Safe Reinforcement Learning using Data-Driven Predictive Control
Mahmoud Selim, Amr Alanwar, M. Watheq El-Kharashi, Hazem M. Abbas, Karl H. Johansson
Estimating Task Completion Times for Network Rollouts using Statistical Models within Partitioning-based Regression Methods
Venkatachalam Natchiappan, Shrihari Vasudevan, Thalanayar Muthukumar
DL-Corrector-Remapper: A grid-free bias-correction deep learning methodology for data-driven high-resolution global weather forecasting
Tao Ge, Jaideep Pathak, Akshay Subramaniam, Karthik Kashinath
How fair were COVID-19 restriction decisions? A data-driven investigation of England using the dominance-based rough sets approach
Edward Abel, Sajid Siraj