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Zahra Sadeghibogar

RWTH Aachen University, Process and Data Science
CSG Data Science and Machine Learning

Scientific Staff

Contact

RWTH Aachen University
Chair of Process and Data Science
Ahornstraße 55
52074 Aachen


data_science@nhr.tu-darmstadt.de

LinkedIn

Biography

Zahra completed her undergraduate and graduate degrees in Information Technolog (IT). She has experience of working as mobile application front-end developer and bython-based software developer for more than three years. Since Nov 2021 she starts her PhD in PADS, Process and Data Science group at RWTH Aachen university and as one of the team members of Crosssectional Group Data Science and Machine Learnining at the National High Performance Computing Center (NHR4CES).

Thematic Advice

There are a large number of scientfific workflows that are running on HPC clusters. It is good to document these scientific workflows: reporting which commands are executed and in which order, and also detect the bottlenecks that slow down the execution of scientific workflows. he goal of process mining is to extract information about processes from input logs, i.e., execution histories and all of these challenges can be solved with process minning techniques.

Professional Competence

Zahra focus lies on Scientific Workflows and process mining,  developing tools to support users to execute their jobs on the HPC as a scientific workflow, and project data and process mining insights on the input workflow (Simulation, ML, AI, PM, etc). Insights like analyzing performance bottlenecks and finding deviations, finally improving scheduling and planning and suggesting optimal parameters.

In the context of NHR, „CSG Data Science and Machine Learning“, she provides training in process mining techniques. Her goals are to 1) guide users through process mining, 2) provide infrastructure and scientific workflows to make machine learning and process mining on HPC easily accessible while increasing the scalability of process mining techniques (SW4PM), and 3) analyze scientific workflows running on HPC clusters using process mining (PM4SW).