David E. Singh is Associate Professor of Computer Science at the Universidad Carlos III de Madrid which he joined in 2004. He obtained a B.Sc. in Physics (specializing in Electronics and Computing) in 1997 followed by an M.S. in High Performance Computing and Artifical Intelligence in 1999 and a Ph.D. in Physics in 2003.  The Ph.D. topic was the developtment of run-time techniques for the automatic parallelization of irregular applications. 

He is a member of the IEEE and ACM associations and his current research interests include:

• Computational epidemiology: parallel simulator that performs realistic stochastic simulations of the propagation of the COVID-19 virus across wide geographic areas. 
• Performance optimization of parallel applications: leverage of runtime monitoring for optimization techniques of HPC applications on clusters and clouds. These techniques include: data reordering for improving the performance on shared memory architectures, providing malleability capacities to MPI parallel applications and reducing the communication overhead by means of compression.
• High performance I/O: improving the efficiency of parallel I/O by means of locality-aware techniques.

He is currently involved in several research projects including the coordination of the project  Medium and Long-term Simulation of Covid-19 funded by the Instituto de Salud Carlos III, and participation in the EU-funded projects European Regimen Acceleration for Tuberculosis (ERA4TB),  Exascale Programming Models for Extreme Data Processing (ASPIDE) and Adaptive Multi-tier Intelligent Data Manager for Exascale (ADMIRE) as well as the Spanish-funded project New Methods in High-end and Edge Computing for Data Intensive Computing.