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CV 
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Prof.univ.dr.habil. Gheorghe Cosmin SILAGHI
Strada Teodor Mihali, Nr. 58-60
Campus FSEGA
400591, Cluj-Napoca, Romania
Tel: +40 264 418 652/3/4/5
Fax: +40 264 412 570
Int: 5856
Birou: 431
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| Interes de cercetare predare |
Artificial Intelligence including:
- natural language processing
- deep learning and machine learning
- large language models
- knowledge graphs
- multi-agent systems and mechanism design
- agent-based simulation
Distributed computing including
- heterogeneous computing systems
- resource management and service level agreements
- automated negotiation
- mechanisms for dependability
- reputation models
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| Publicatii (top 5) |
Gheorghe Cosmin Silaghi, Filipe Araujo, Luis Moura Silva, Patricio Domingues, Alvaro E. Arenas, Defeating colluding nodes in desktop grid computing platforms, Journal of Grid Computing, Volume 7, Issue 4, Pages 555 - 573, 2009, Springer,
https://doi.org/10.1007/s10723-009-9124-5
Gheorghe Cosmin Silaghi, Liviu Dan Şerban, Cristian Marius Litan,
A time-constrained SLA negotiation strategy in competitive computational grids,
Future Generation Computer Systems, Volume 28, Issue 8, 2012, Pages 1303-1315, Elsevier
https://doi.org/10.1016/j.future.2011.11.002.
Vasile Ionut Remus Iga, Gheorghe Cosmin Silaghi, Assessing LLMs Suitability for Knowledge Graph Construction, Neurosymbolic Artificial Intelligence, accepted, 2025, Sage,
https://neurosymbolic-ai-journal.com/paper/assessing-llms-suitability-knowledge-graph-construction-1
Mircea Moca, Cristian Litan, Gheorghe Cosmin Silaghi, Gilles Fedak,
Multi-criteria and satisfaction oriented scheduling for hybrid distributed computing infrastructures,
Future Generation Computer Systems,
Volume 55, 2016, Pages 428-443, Elsevier
https://doi.org/10.1016/j.future.2015.03.022.
Alvaro E. Arenas, Benjamin Aziz, Gheorghe Cosmin Silaghi, Reputation management in collaborative computing systems, Security and Communication Networks, volume 3, Pages. 546-564, 2010, Wiley
https://doi.org/10.1002/sec.146
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Discipline predate nivel licenta:
- Algoritmi si structuri de date (an II IE)
- Inteligenta artificiala (an III IE)
- Fundamente de Big data (tehnici fundamentale de Machine Learning, an III IE).
Teme preferate pentru lucrarea de licenta:
- inteligenta artificiala incluzand: large language models si grafuri de cunostiinte
- folosirea tehnicilor de machine learning pentru studierea unor probleme specifice de afaceri
Displine predate la nivel master (masterul Business Modeling and Distributed Computing):
- deep learning
- natural language processing
- research methodologies and academic writing techniques
Discipline predate la nivelul scolii doctorale
- Research methodologies and academic integrity |
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