Majid is fascinated by the science that takes data from the real world and transforms them into useful insights by discovering business process models or training descriptive and predictive models. Majid earned his Ph.D. in process and data science at RWTH Aachen University in Germany, under the supervision of Prof. Wil van der Aalst, the godfather of process mining. He is currently working as a Senior Process and Data Scientist at SAP SE. He is a former SAP Technical Consultant and was involved in several SAP ABAP development, SAP Fiori, and SAP PI/PO projects.
Skills
Python
Statistics
Photography
Experience
CEO
GenCoin
Responsibilities include:
- Analysing
- Modelling
- Deploying
Professor of Semiconductor Physics
University X
Taught electronic engineering and researched semiconductor physics.
Accomplishments
Neural Networks and Deep Learning
Formulated informed blockchain models, hypotheses, and use cases.
See certificate
Object-Oriented Programming in R
Recent Posts
Projects
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The goal of this project is to provide AI-based tools and analysis for students and study program designers to increase the quality of teaching and learning. The AI-based study buddy tool provides goal-oriented help for students such as setting a goal to achieve a high grade in a course and getting recommendations for that goal. The AI-based buddy analytics tool provides dashboards for study program designers and supports them to (re)design study programs.
In this project, the goal is to support and comprehend process mining results such as process models with predictive machine learning techniques. An example is to predict deviations from the reference model as early as possible. All the machine learning techniques are supported by explainable AI (XAI) to add transparency to decisions made by black-box models.
The extraction, transformation, and loading of event logs from information systems is the first and the most expensive step in process mining. In particular, extracting event data from popular ERP systems such as SAP poses major challenges, given the size and the structure of the data. The goal of this project is to first extract object-centric event data from SAP ERP systems, and then discover and analyze well-known and unknown processes from such systems.
RPM (Responsible Process Mining) aims to deal with FACT (Fairness, Accuracy, Confidentiality, and Transparency) challenges in process mining. This project is focused on privacy and confidentiality aspects, where process mining algorithms are applied with respect to the private (sensitive) information of individuals (organization).
In this project, I was working as SAP technical consultant, where I was involved in developing SAP applications (transactions) using SAP ABAP programming. I was also an SAP Fiori developer and PI/PO (Process Integration/Process Orchestration) architect.
An online community is a virtual community where people can express their opinions and their knowledge freely. There is a great deal of information in online communities. However, there is no way to determine its authenticity. Thus, the knowledge which has been shared in online communities is not reliable. The goal of this project is to determine the expertise level of users and find experts in online communities in order to evaluate the accuracy of posted comments.
Recent Publications
Quickly discover relevant content by filtering publications.
(2023).
A generic approach to extract object-centric event data from databases supporting SAP ERP.
Journal of Intelligent Information Systems.
(2023).
An Abstraction-Based Approach for Privacy-Aware Federated Process Mining.
IEEE Access.
(2023).
Analyzing Inter-Connected Processes: Using Object-Centric Process Mining to Analyze Procurement Processes.
International Journal of Data Science and Analytics.
(2023).
TraVaG: Differentially Private Trace Variant Generation Using GANs.
Research Challenges in Information Science: Information Science and the Connected World - 17th International Conference, RCIS 2023, Corfu, Greece, May 23-26, 2023, Proceedings.
(2023).
TraVaS: Differentially Private Trace Variant Selection for Process Mining.
Process Mining Workshops.
Contact
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