Scholar

Evrim Acar

Google Scholar ID: eQKaErAAAAAJ

Simula Research Lab

Data miningtensor decompositionsmultimodal data mining

Citations & Impact

All-time

Citations

2,720

H-index

i10-index

Publications

Co-authors

list available

Contact

Publications

1 items

2025

Cited

Resume (English only)

Academic Achievements

Publications:
1. "Tensor and Coupled Decompositions for Interpretable Pattern Discovery in Multiset and Multimodal Functional Neuroimaging Data", IEEE Signal Processing Magazine
2. "PARAFAC2-based Coupled Matrix and Tensor Factorizations with Constraints", IEEE Journal of Selected Topics in Signal Processing
3. "Longitudinal metabolomics data analysis informed by mechanistic models", Metabolites
4. "tPARAFAC2: Tracking evolving patterns in (incomplete) temporal data", Data Mining and Knowledge Discovery
5. "dCMF: Learning interpretable evolving patterns from temporal multiway data", 33rd European Signal Processing Conference (EUSIPCO)
6. "(Coupled) Tensor Factorizations – as a tool to develop knowledge-guided data-driven methods for extracting insights from complex data", 18th Annual IBEC (Institute for Bioengineering of Catalonia) Symposium, Barcelona, Spain
7. "Coupled Matrix/Tensor Factorizations – as a tool to develop knowledge-guided data-driven methods for extracting insights from complex data", 23rd IEEE Statistical Signal Processing Workshop (SSP 2025), Edinburgh, UK
8. "Coupled Matrix/Tensor Factorizations – as a tool to develop knowledge-guided data-driven methods for extracting insights from complex data", TRICAP: Three-way methods In Chemistry And Psychology
9. "dCMF: Learning interpretable evolving patterns from temporal multiway data", Particles, Fluids and Patterns: Analytical and Computational Challenges - Intensive Trimester

Research Experience

Position: Chief Research Scientist/Research Professor, Head of Department; Department: Data Science and Knowledge Discovery; Organization: Simula Metropolitan.

Background

Research Interests: Data Mining, Matrix/Tensor Factorizations, Data Fusion/Multi-modal Data Mining. Professional Field: Data Science and Knowledge Discovery.

Co-authors

49 total