David E. Singh is an Associate Professor in the Computer Architecture research group at the Universidad Carlos III de Madrid, Spain. His current research interests include:
- Computational epidemiology: coordinating the EpiGraph project, which consists of the development of an agent-based parallel simulator that performs realistic stochastic simulations of the propagation of influenza, COVID-19 and other similar viruses across wide geographic areas.
- High-Performance Computing: application malleability, monitoring, modelling and multi-criteria scheduling: leadership of the FlexMPI project, a runtime system that extends the functionalities of the MPI library by providing malleable, load-balance and monitoring capabilities to MPI applications. He is also involved in the development of application modelling techniques combining monitoring information collected at application level and system level. A further research area is leveraging these two models for developing multi-criteria application scheduling of malleable applications.
- High performance I/O: including research lines such as improving the efficiency of parallel I/O by means of locality-aware techniques, developing I/O scheduling algorithms and exploring the use of malleability for enhancing the application I/O performance.
David E. Singh is currently coordinating the following projects:
- Medium and Long-term Simulation of COVID-19 funded by the Instituto de Salud Carlos III.
- Multi-source and multi-method prediction to support COVID-19 policy decision making (PredCov), which is funded by European REACT-EU funds.
- Development of a tool for prediction of epidemiological scenarios and vaccination against COVID-19, funded by the Ministry of Health and Innovation.
He is also participating as a researcher 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.
David E. Singh is a member of the IEEE and ACM associations, as well as the HiPEAC, CABAHLA, and CAPAP-H research networks. He participates in the ECDC’s European forecast COVID-19 Hub and European scenario COVID-19 Hub by providing short-term and long-term forecasts for COVID-19 incidence in Spain. He also is member of the Academic Evaluation Committee for Computer Science and Engineering and Dual Bachelor’s degree in Computer Science and Engineering and Business Administration in Colmenarejo UC3M campus. David is additionally a member of the UC3M’s Contract Evaluation Committee for the recruitment of new research assistants.
In terms of research collaborations, he is currently actively collaborating with the following research groups:
- In the scope of the EpiGraph and PredCov projects, he is collaborating on joint publications with the Barcelona Supercomputing Center (BSC), the Spanish National Epidemiology Center and the Ministry of Health and Innovation of Spain. He participates in the Ponencia de Vacunas del Consejo Interterritorial del SNS as a modeling team leader -a taskforce created by the Spanish Government for designing the COVID-19 vaccination program for Spain-, with active contribution from the EpiGraph team. He is also collaborating on joint publications with the London School of Hygiene & Tropical Medicine and the European Center for Disease Control and Prevention (ECDC).
- In the scope of ADMIRE project, he has active collaborations -and joint publications- with the LaBRI group at INRIA Bordeaux Sud-Ouest, the Parallel Programming group at Technische Universität Darmstadt, the Efficient Computing and Storage Group at Johannes Gutenberg – Universität Mainz, the Computer Architecture Group of Universidad de Castilla-La Mancha, the Barcelona Supercomputing Center (BSC) , the Department of Science and Technologies of University of Naples “Parthenope’” (UNP) and the Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT)