Evrim Acar Ataman
Chief Research Scientist/Research ProfessorHead of Department
- Department
- Data Science and Knowledge Discovery
- Organisation
- Simula Metropolitan
- Research Interests
- Data Mining Matrix/Tensor Factorizations Data Fusion/ Multi-modal data mining
![Evrim Acar Ataman](/_next/image?url=%2Fimages%2Fperson-placeholder.png&w=384&q=75)
- evrim@simula.no
Publications
2024
Journal Articles
L. Li, H. Hoefsloot, B. M. Bakker, D. Horner, M. A. Rasmussen, A. K. Smilde and E. A. Ataman
Longitudinal metabolomics data analysis informed by mechanistic models
bioRxiv
C. Chatzis, C. Schenker, M. Pfeffer and E. A. Ataman
tPARAFAC2: Tracking evolving patterns in (incomplete) temporal data
arXiv Preprints
L. Li, S. Yan, D. Horner, M. A. Rasmussen, A. K. Smilde and E. A. Ataman
Revealing static and dynamic biomarkers from postprandial metabolomics data through coupled matrix and tensor factorizations
Metabolomics
S. Yan, L. Li, D. Horner, P. Ebrahimi, B. Chawes, L. O. Dragsted, M. A. Rasmussen, A. K. Smilde and E. A. Ataman
Characterizing human postprandial metabolic response using multiway data analysis
Metabolomics
L. Li, S. Yan, B. M. Bakker, H. Hoefsloot, B. Chawes, D. Horner, M. A. Rasmussen, A. K. Smilde and E. A. Ataman
Analyzing postprandial metabolomics data using multiway models: A simulation study
BMC Bioinformatics
M. Fida, M. Roald, E. A. Ataman and A. Elmokashfi
Modeling Variation in Mobile Download Speed in Presence of Missing Samples
IEEE Transactions on Mobile Computing
Talks, keynote
E. A. Ataman
Extracting Insights from Complex Data using (Coupled) Tensor Factorizations
ECMTB'24: European Conference on Mathematical and Theoretical Biology
E. A. Ataman
Coupled Matrix and Tensor Factorizations – Improving our Understanding of Complex Systems Through the Analysis of Temporal and Multimodal Data
SIAM Conference on Applied Linear Algebra
Talks, invited
L. Li, S. Yan, B. M. Bakker, H. Hoefsloot, D. Horner, B. Chawes, L. O. Dragsted, P. Ebrahimi, M. A. Rasmussen, A. K. Smilde and E. A. Ataman
From static to dynamic markers: Analysis of time-resolved metabolomics data using (coupled) tensor decompositions
SIAM Conference on Applied Linear Algebra (LA24)
C. Chatzis, C. Schenker, M. Pfeffer, P. Lind and E. A. Ataman
A Time-aware tensor decomposition for concept evolution
94th Annual meeting of the International Association of Applied Mathematics and Mechanics (GAMM 2024)
Miscellaneous
C. Schenker, X. Wang, D. Horner, M. A. Rasmussen and E. A. Ataman
PARAFAC2-based Coupled Matrix and Tensor Factorizations with Constraints
2023
Journal Articles
F. Becker, A. K. Smilde and E. A. Ataman
Unsupervised EHR-based Phenotyping via Matrix and Tensor Decompositions
WIREs Data Mining and Knowledge Discovery
Proceedings, refereed
C. Chatzis, M. Pfeffer, P. Lind and E. A. Ataman
A Time-aware tensor decomposition for tracking evolving patterns
IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP 2023)
C. Schenker, X. Wang and E. A. Ataman
PARAFAC2-based coupled Matrix and Tensor Factorizations
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Posters
L. Li, S. Yan, B. M. Bakker, H. Hoefsloot, B. Chawes, M. A. Rasmussen, A. K. Smilde and E. A. Ataman
Fusion of static and dynamic metabolomics challenge test data: Capturing metabolic differences between fasting and postprandial states
Nordic AI meet 2023
C. Schenker, X. Wang and E. A. Ataman
PARAFAC2-Based Coupled Matrix and Tensor Factorizations
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Talks, invited
C. Schenker, X. Wang and E. A. Ataman
PARAFAC2-Based Coupled Matrix and Tensor Factorizations with Constraints
10th International Congress on Industrial and Applied Mathematics, ICIAM 2023, Tokyo, Japan
E. A. Ataman
Constrained Multimodal Data Mining using Coupled Matrix and Tensor Factorizations
Acceleration and Extrapolation Methods, ICERM, Brown University, Rhode Island, US
C. Schenker, X. Wang and E. A. Ataman
Fusing Dynamic and Static Data Using Parafac2 Based Coupled Matrix and Tensor Factorizations
SIAM Conference on Computational Science and Engineering
E. A. Ataman
Extracting Insights from Complex Data: Constrained Multimodal Data Mining using Coupled Matrix and Tensor Factorizations
IPAM Workshop on Explainable AI for the Sciences: Towards Novel Insights
Talks, contributed
L. Li, S. Yan, B. M. Bakker, H. Hoefsloot, D. Horner, B. Chawes, M. A. Rasmussen, A. K. Smilde and E. A. Ataman
From static to dynamic - how to analyze postprandial metabolomics data?
Nordic Metabolomics Conference
2022
Journal Articles
E. A. Ataman, M. Roald, K. M. Hossain, V. D. Calhoun and T. Adali
Tracing Evolving Networks using Tensor Factorizations vs. ICA-based Approaches
Frontiers in Neuroscience
T. Adali, F. Kantar, M. A. B. S. Akhonda, S. Strother, V. D. Calhoun and E. A. Ataman
Reproducibility in Matrix and Tensor Decompositions: Focus on Model Match, Interpretability, and Uniqueness
IEEE Signal Processing Magazine
L. Li, H. Hoefsloot, A. A. de Graaf, E. A. Ataman and A. K. Smilde
Exploring Dynamic Metabolomics Data With Multiway Data Analysis: a Simulation Study
BMC Bioinformatics
M. Roald, C. Schenker, V. D. Calhoun, T. Adali, R. Bro, J. E. Cohen and E. A. Ataman
An AO-ADMM approach to constraining PARAFAC2 on all modes
SIAM Journal on Mathematics of Data Science
Proceedings, refereed
F. Becker, M. Nygård, J. Nygård, A. K. Smilde and E. A. Ataman
Phenotyping of cervical cancer risk groups via generalized low-rank models using medical questionnaires
Norwegian AI Symposium: Nordic Artificial Intelligence Research and Development
I. Lehmann, E. A. Ataman, T. Hasija, M. Akhonda, V. D. Calhoun, P. J. Schreier and T. Adali
Multi-task FMRI Data Fusion using IVA and PARAFAC2
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Posters
S. Yan, L. Li, D. Horner, B. Chawes, M. A. Rasmussen, A. K. Smilde and E. A. Ataman
Characterizing postprandial metabolic response using multi-way data analysis
Norwegian Bioinformatics Days 2022
L. Li, S. Yan, B. M. Bakker, H. Hoefsloot, B. Chawes, M. A. Rasmussen, A. K. Smilde and E. A. Ataman
Revealing dynamic changes in metabolism through the analysis of postprandial metabolomics data: A simulation study
Metabolomics 2022
Talks, invited
E. A. Ataman
Extracting Insights from Complex Data: Data Mining using Tensor Factorizations
SILS (Swammerdam Institute for Life Sciences) Data Science Symposium, University of Amsterdam, Netherlands
E. A. Ataman
Constrained Multimodal Data Mining
BigInsight Seminar, University of Oslo, Norway
C. Schenker, M. Roald, X. Wang, J. E. Cohen and E. A. Ataman
Constrained Multi-Modal Data Mining Using Coupled Matrix and Tensor Factorizations
SIAM Conference on Mathematics of Data Science
Talks, contributed
M. Roald, C. Schenker, V. D. Calhoun, T. Adali, R. Bro, J. E. Cohen and E. A. Ataman
An AO-ADMM approach to constrained PARAFAC2
Nordic AI Meet
M. Roald, C. Schenker, R. Bro, J. E. Cohen and E. A. Ataman
Fully Constrained PARAFAC2 with AO-ADMM
SIAM Conference on Parallel Processing for Scientific Computing
S. Yan, L. Li, D. Horner, B. Chawes, M. A. Rasmussen, A. K. Smilde and E. A. Ataman
Characterizing postprandial metabolomics response using multi-way data analysis
Annual NORBIS Conference
L. Li, S. Yan, B. M. Bakker, H. Hoefsloot, D. Horner, B. Chawes, M. A. Rasmussen, A. K. Smilde and E. A. Ataman
Analyzing postprandial metabolomics data using multiway models: A simulation study
Nordic Metabolomics 2022, Copenhagen, Denmark
L. Li, S. Yan, B. M. Bakker, H. Hoefsloot, D. Horner, B. Chawes, M. A. Rasmussen, A. K. Smilde and E. A. Ataman
Analyzing postprandial metabolomics data using multiway models: A simulation study
Norwegian Bioinformatics Days, Sundvolden, Norway
E. A. Ataman
A Flexible Framework for Coupled Matrix/Tensor Factorizations
TRICAP: Three-way methods In Chemistry And Psychology
L. Li, S. Yan, B. M. Bakker, H. Hoefsloot, B. Chawes, M. A. Rasmussen, A. K. Smilde and E. A. Ataman
Analyzing postprandial metabolomics data using multiway models: A simulation study
NuGOweek 2022 in Spain
2021
Journal Articles
C. Schenker, J. E. Cohen and E. A. Ataman
A Flexible Optimization Framework for Regularized Matrix-Tensor Factorizations with Linear Couplings
IEEE Journal of Selected Topics in Signal Processing
Proceedings, refereed
M. Roald, C. Schenker, J. E. Cohen and E. A. Ataman
PARAFAC2 AO-ADMM: Constraints in all modes
2021 29th European Signal Processing Conference (EUSIPCO)
Posters
C. Schenker, J. E. Cohen and E. A. Ataman
An Optimization Framework for Regularized Linearly Coupled Matrix-Tensor Factorization
Talks, invited
C. Schenker, J. E. Cohen and E. A. Ataman
A Flexible Optimization Framework for Regularized Linearly Coupled Matrix-Tensor Factorizations based on the Alternating Direction Method of Multipliers
Europt21, 18th Workshop on Advances in Continuous Optimization
E. A. Ataman
From Data Mining using Tensor Factorizations to Multimodal Data Mining using Coupled Matrix/Tensor Factorizations
Nordic Probabilistic AI School (virtual)
C. Schenker, J. Cohen and E. A. Ataman
An Optimization Framework for Regularized Linearly Coupled Matrix-Tensor Factorization
SIAM Conference on Applied Linear Algebra (LA21)
Talks, contributed
L. Li, H. Hoefsloot, A. A. de Graaf, E. A. Ataman and A. K. Smilde
Exploring dynamic metabolomics data with multiway data analysis: A simulation study
SIAM Conference on Applications of Dynamical Systems
C. Schenker, M. Roald, J. E. Cohen and E. A. Ataman
A Flexible Optimization Framework for Regularized Matrix-Tensor Factorizations with Linear Couplings
Asilomar Conference on Signals, Systems, and Computers
F. Becker
Generalized Low-Rank Models for Phenotyping Cervical Cancer Risk Groups using Medical Questionnaires
Stavanger, Norway
M. Roald, C. Schenker, J. E. Cohen and E. A. Ataman
Tracing Dynamic Networks through Constrained Parafac2 Decomposition
SIAM Conference on Applied Linear Algebra (LA21), Virtual Conference
2018
Talks, contributed
E. A. Ataman
Structure-Revealing Data Fusion Models based on Coupled Matrix and Tensor Factorizations and Their Applications
TRICAP: Three-way methods In Chemistry And Psychology