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Personal profile

Education/Academic qualification

Industrial Engineering, PhD, Georgia Institute of Technology

… → 1999

Applied Mathematics, BS, University of Ljubljana

… → 1994

Research interests

  • Deep learning
  • Finance
  • Healthcare
  • Machine learning and artificial intelligence - text analytics
  • Optimization
  • Transportation

Fingerprint Dive into the research topics where Diego Klabjan is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

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Grants 2007 2022

Agile manufacturing systems
Industrial plants
Availability
Silicon
Steady flow
Knowledge management
Planning
Industry
Deep learning
training program
applied science
engineering science
school
science
Scheduling
Planning
Cellular manufacturing
Application programs
Education

Research Output 1998 2019

A Team Based Player Versus Player Recommender Systems Framework for Player Improvement

Joshi, R., Gupta, V., Li, X., Cui, Y., Wang, Z., Ravari, Y. N., Klabjan, D., Sifa, R., Parsaeian, A., Drachen, A. & Demediuk, S., Jan 29 2019, Proceedings of the Australasian Computer Science Week Multiconference, ACSW 2019. Association for Computing Machinery, a45. (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Recommender systems
Cluster analysis
2 Citations (Scopus)

Online adaptive machine learning based algorithm for implied volatility surface modeling

Zeng, Y. & Klabjan, D., Jan 1 2019, In : Knowledge-Based Systems. 163, p. 376-391 16 p.

Research output: Contribution to journalArticle

Learning systems
Computer hardware
Sensitivity analysis
Program processors
Field programmable gate arrays (FPGA)
2 Citations (Scopus)

Predicting ICU readmission using grouped physiological and medication trends

Xue, Y., Klabjan, D. & Luo, Y., Apr 1 2019, In : Artificial Intelligence In Medicine. 95, p. 27-37 11 p.

Research output: Contribution to journalArticle

Intensive care units
Intensive Care Units
Logistic Models
Statistics
Aptitude

The trails of Just Cause 2: Spatio-temporal player profiling in open-world games

Aung, M., Demediuk, S., Sun, Y., Tu, Y., Ang, Y., Nekkanti, S., Raghav, S., Klabjan, D., Sifa, R. & Drachen, A., Aug 26 2019, Proceedings of the 14th International Conference on the Foundations of Digital Games, FDG 2019. Khosmood, F., Pirker, J., Apperley, T. & Deterding, S. (eds.). Association for Computing Machinery, 41. (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Open Access
Cluster analysis

Competitive multi-agent inverse reinforcement learning with sub-optimal demonstrations

Wang, X. & Klabjan, D., Jan 1 2018, 35th International Conference on Machine Learning, ICML 2018. Krause, A. & Dy, J. (eds.). International Machine Learning Society (IMLS), Vol. 11. p. 8148-8175 28 p.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Reinforcement learning
Demonstrations
Learning algorithms
Experiments