Evrim Acar Ataman

C. Schenker, X. Wang, D. Horner, M. A. Rasmussen and E. A. Ataman

PARAFAC2-based Coupled Matrix and Tensor Factorizations with Constraints

IEEE Journal of Selected Topics in Signal Processing

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

Metabolites

C. Chatzis, C. Schenker, M. Pfeffer and E. A. Ataman

tPARAFAC2: Tracking evolving patterns in (incomplete) temporal data

Data Mining and Knowledge Discovery

C. Chatzis, C. Schenker, J. E. Cohen and E. A. Ataman

dCMF: Learning interpretable evolving patterns from temporal multiway data

33rd European Signal Processing Conference (EUSIPCO)

E. A. Ataman

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

E. A. Ataman

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

C. Chatzis, C. Schenker, J. E. Cohen and E. A. Ataman

dCMF: Learning interpretable evolving patterns from temporal multiway data

Particles, Fluids and Patterns: Analytical and Computational Challenges - Intensive Trimester

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

B. Erdős, M. L. Widdowson, F. Wesel, J. Thorsen, J. Stokholm, A. K. Smilde, M. A. Rasmussen and E. A. Ataman

Extracting insight from longitudinal gut microbiome data through multiway data analysis

2nd Copenhagen Gut Microbiome Symposium, Copenhagen, Denmark

E. A. Ataman

Extracting Insights from Complex Data: (Coupled) Tensor Factorizations & Applications

ECDA'24: European Conference on Data Analysis

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

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)

F. Becker, A. K. Smilde and E. A. Ataman

Unsupervised EHR-based Phenotyping via Matrix and Tensor Decompositions

WIREs Data Mining and Knowledge Discovery

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)

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)

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

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

Constrained Multimodal Data Mining using Coupled Matrix and Tensor Factorizations

Acceleration and Extrapolation Methods, ICERM, Brown University, Rhode Island, US

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

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

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

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)

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

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

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

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

E. A. Ataman

A Flexible Framework for Coupled Matrix/Tensor Factorizations

TRICAP: Three-way methods In Chemistry And Psychology

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)

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

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