Advanced data-driven frameworks to develop reproducible data science applications at scale

Description

There is commensurate growth in expectations about what can be achieved with this wealth of data and computational power. To meet these expectations with available expertise requires new data and software engineering and management frameworks, new data architectures, and big data processing techniques that make it easier to reliably formalise data-driven methods to extract and analyse information and translate them into actionable insights. Furthermore, it also requires new advanced methods to improve the adoption, sustainability, searchability and reusability of those data-driven methods and other scientific methods/software from and to the scientific communities. This talk will focus on the work that I am doing on developing new advanced information processing technologies and interfaces to extract knowledge from data and software to accelerate: 1) scientific discovery; 2) scientific software adoption, reproducibility, automation, parallelisation, orchestration, and software component re-usability. This includes the development of new scientific data processing workflows/frameworks and programming abstractions, software feature extraction, and scalable and adaptive optimisation algorithms among others.

Period	7 Sept 2021
Event title	Workshop on Re-envisioning Extreme-Scale I/O for Emerging Hybrid HPC Workloads: Held in conjunction with IEEE Cluster 2021.
Event type	Workshop
Degree of Recognition	International