Scholar
Abdullah Makkeh
Google Scholar ID: i7Sn0mMAAAAJ
Postdoc, University of Göttingen
Citations & Impact
Citations
395
H-index
10
i10-index
10
Publications
20
Co-authors
10
list available
Publications
Resume (English only)
Academic Achievements
- Selected publication: 'A general framework for interpretable neural learning based on local information-theoretic goal functions.' Abdullah Makkeh, Marcel Graetz, Andreas C. Schneider, David A. Ehrlich, Viola Priesemann, and Michael Wibral. PNAS (2025).
Research Experience
- Since October 2019, Senior Scientist at the University of Göttingen, Department of Data-driven Analysis of Biological Networks, headed by Michael Wibral; Since November 2019, Guest Scientist at the Max Planck Institute for Dynamics and Self-Organization, Complex Systems Theory group, headed by Viola Priesemann; Sep 2018 -- Sep 2019: Postdoc at the University of Tartu, Institute of Computer Science, in the groups: Theoretical Computer Science (headed by Dirk Oliver Theis) and Computational Neuroscience (headed by Raul Vicente); Jan 2019 -- Dec 2019: Part-time computational applied mathematician at Ketita labs, developing quantum technology.
Education
- PhD (Informatics) University of Tartu (2018, Supervisor: Dirk Oliver Theis); MSc (Mathematics) Lebanese University (2013, Supervisor: Bassam Mourad); BSc (Mathematics) Lebanese University (2011).
Background
- Interested in information theory, mathematical optimization, data analysis and their applications in studying complex systems. The main focus currently is on how the global computation of entities (especially neural networks and RL interactions) emerges from their local components in AI systems.
Miscellany
- Experienced developer, check his GitHub profile.
