@article {43012,
	title = {Analyzing postprandial metabolomics data using multiway models: A simulation study},
	journal = {bioRxiv},
	year = {2023},
	publisher = {bioRxiv},
	doi = {https://doi.org/10.1101/2022.12.19.521154 },
	url = {https://www.biorxiv.org/content/10.1101/2022.12.19.521154v2},
	author = {Li, Lu and Yan, Shi and Barbara M. {Bakker} and Hoefsloot, Huub and Chawes, Bo and Horner, David and Morten A. {Rasmussen} and Age K. {Smilde} and Evrim Acar {Ataman}}
}
@article {43402,
	title = {Characterizing human postprandial metabolic response using multiway data analysis},
	journal = {bioRxiv},
	year = {2023},
	publisher = {biorxiv},
	doi = {10.1101/2023.08.31.555521 },
	author = {Yan, Shi and Li, Lu and Horner, David and Ebrahimi, Parvaneh and Chawes, Bo and Lars O. {Dragsted} and Morten A. {Rasmussen} and Age K. {Smilde} and Evrim Acar {Ataman}}
}
@misc {43330,
	title = {Constrained Multimodal Data Mining using Coupled Matrix and Tensor Factorizations},
	howpublished = {Acceleration and Extrapolation Methods, ICERM, Brown University},
	year = {2023},
	url = {https://icerm.brown.edu/topical_workshops/tw-23-aem/},
	author = {Evrim Acar {Ataman}}
}
@misc {43108,
	title = {Extracting Insights from Complex Data: Constrained Multimodal Data Mining using Coupled Matrix and Tensor Factorizations },
	howpublished = {IPAM Workshop on Explainable AI for the Sciences: Towards Novel Insights},
	year = {2023},
	url = {http://www.ipam.ucla.edu/abstract/?tid=18155\&pcode=XAI2023},
	author = {Evrim Acar {Ataman}}
}
@inproceedings {43049,
	title = {PARAFAC2-based coupled Matrix and Tensor Factorizations},
	journal = {IEEE International Conference on Acoustics, Speech and Signal Processing  (ICASSP)},
	year = {2023},
	pages = {1-5},
	publisher = { IEEE},
	doi = {10.1109/ICASSP49357.2023.10094562},
	author = {Schenker, Carla and Wang, Xiulin and Evrim Acar {Ataman}}
}
@inproceedings {43328,
	title = {A Time-aware Tensor Decomposition for Tracking Evolving Patterns},
	journal = {MLSP{\textquoteright}23:  IEEE International Workshop on Machine Learning for Signal Processing},
	year = {2023},
	publisher = { IEEE},
	author = {Chatzis, Christos and Pfeffer, Max and Lind, Pedro and Evrim Acar {Ataman}}
}
@article {42716,
	title = {Unsupervised EHR-based Phenotyping via Matrix and Tensor Decompositions},
	journal = {WIREs Data Mining and Knowledge Discovery},
	volume = {13},
	number = {e1494},
	year = {2023},
	publisher = { Wiley},
	doi = {https://doi.org/10.1002/widm.1494},
	author = {Becker, Florian and Age K. {Smilde} and Evrim Acar {Ataman}}
}
@misc {42752,
	title = {Analyzing postprandial metabolomics data using multiway models: A simulation study},
	howpublished = {Norwegian Bioinformatics Days, Sundvolden, Norway},
	year = {2022},
	author = {Li, Lu and Yan, Shi and Barbara M. {Bakker} and Hoefsloot, Huub and Horner, David and Chawes, Bo and Morten A. {Rasmussen} and Age K. {Smilde} and Evrim Acar {Ataman}}
}
@misc {42657,
	title = {Analyzing postprandial metabolomics data using multiway models: A simulation study},
	howpublished = {NuGOweek 2022 in Spain},
	year = {2022},
	author = {Li, Lu and Yan, Shi and Barbara M. {Bakker} and Hoefsloot, Huub and Chawes, Bo and Morten A. {Rasmussen} and Age K. {Smilde} and Evrim Acar {Ataman}}
}
@misc {42691,
	title = {Analyzing postprandial metabolomics data using multiway models: A simulation study},
	howpublished = {Nordic Metabolomics 2022, Copenhagen, Denmark},
	year = {2022},
	author = {Li, Lu and Yan, Shi and Barbara M. {Bakker} and Hoefsloot, Huub and Horner, David and Chawes, Bo and Morten A. {Rasmussen} and Age K. {Smilde} and Evrim Acar {Ataman}}
}
@misc {43036,
	title = {An AO-ADMM approach to constrained PARAFAC2},
	howpublished = {Nordic AI Meet},
	year = {2022},
	author = {Roald, Marie and Schenker, Carla and Vince D. {Calhoun} and Adali, T{\"u}lay and Bro, Rasmus and Jeremy E. {Cohen} and Evrim Acar {Ataman}}
}
@article {28152,
	title = {An AO-ADMM approach to constraining PARAFAC2 on all modes},
	journal = {SIAM Journal on Mathematics of Data Science},
	volume = {4},
	number = {3},
	year = {2022},
	pages = {1191-1222},
	publisher = {SIAM},
	address = {SIAM Journal on Mathematics of Data Science},
	abstract = {Analyzing multi-way measurements with variations across one mode of the dataset is a challenge in various fields including data mining, neuroscience and chemometrics. For example, measurements may evolve over time or have unaligned time profiles. The PARAFAC2 model has been successfully used to analyze such data by allowing the underlying factor matrices in one mode (i.e., the evolving mode) to change across slices. The traditional approach to fit a PARAFAC2 model is to use an alternating least squares-based algorithm, which handles the constant cross-product constraint of the PARAFAC2 model by implicitly estimating the evolving factor matrices. This approach makes imposing regularization on these factor matrices challenging. There is currently no algorithm to flexibly impose such regularization with general penalty functions and hard constraints. In order to address this challenge and to avoid the implicit estimation, in this paper, we propose an algorithm for fitting PARAFAC2 based on alternating optimization with the alternating direction method of multipliers (AO-ADMM). With numerical experiments on simulated data, we show that the proposed PARAFAC2 AO-ADMM approach allows for flexible constraints, recovers the underlying patterns accurately, and is computationally efficient compared to the state-of-the-art. We also apply our model to a real-world chromatography dataset, and show that constraining the evolving mode improves the interpretability of the extracted patterns.},
	doi = {https://doi.org/10.1137/21M1450033},
	author = {Roald, Marie and Schenker, Carla and Vince D. {Calhoun} and Adali, T{\"u}lay and Bro, Rasmus and Jeremy E. {Cohen} and Evrim Acar {Ataman}}
}
@misc {42755,
	title = {Characterizing postprandial metabolic response using multi-way data analysis},
	year = {2022},
	address = {Norwegian Bioinformatics Days},
	abstract = {Analysis of time-resolved postprandial metabolomics data can enhance our knowledge about the human metabolism by providing a better understanding of regulation of subgroups of metabolites (e.g., lipids) and variations in postprandial responses of subgroups of people, with the potential to ultimately advance precision medicine. However, characterizing postprandial metabolomics response and understanding group differences is a challenging task since it requires the analysis of large-scale metabolomics data from a large set of individuals containing measurements of a wide set of metabolites at multiple time points. Such data is in the form of a three-way array: subjects by metabolites by time points. The state-of-the-art analysis methods mainly focus on clustering temporal profiles relying on summaries of the data across subjects or univariate analysis techniques studying one metabolite at a time, and\&nbsp;fail to associate subgroups of subjects and subsets of metabolites with the dynamic time profile simultaneously.\&nbsp;In this study, we use NMR (nuclear magnetic resonance) spectroscopy measurements of plasma samples (of over three hundred individuals from the COPSAC2000 cohort) collected at multiple time points during a challenge test.\&nbsp;\&nbsp;We use a multi-way analysis technique called the CANDECOMP/PARAFAC (CP) model to extract interpretable patterns from the time-resolved data. We compare the analysis of postprandial data, fasting state-corrected data and only fasting state data, and demonstrate the differences between different analysis approaches.Our results show that the CP model reveals biologically meaningful patterns capturing how certain metabolite groups and their temporal profiles relate to various meta variables, in particular, BMI (body mass index), confirming already known biological knowledge as well as revealing new biological insights.},
	keywords = {CANDECOMP/PARAFAC, Dynamic metabolomics data, large-scale dataset, Tensor factorization},
	author = {Yan, Shi and Li, Lu and Horner, David and Chawes, Bo and Morten A. {Rasmussen} and Age K. {Smilde} and Evrim Acar {Ataman}}
}
@misc {42834,
	title = {Characterizing postprandial metabolomics response using multi-way data analysis},
	howpublished = {Annual NORBIS Conference},
	year = {2022},
	abstract = {Analysis of time-resolved postprandial metabolomics data can enhance our knowledge about the human metabolism by providing a better understanding of regulation of subgroups of metabolites (e.g., lipids) and variations in postprandial responses of subgroups of people, with the potential to ultimately advance precision medicine. However, characterizing postprandial metabolomics response and understanding group differences is a challenging task since it requires the analysis of large-scale metabolomics data from a large set of individuals containing measurements of a wide set of metabolites at multiple time points. Such data is in the form of a three-way array: subjects by metabolites by time points. The state-of-the-art analysis methods mainly focus on clustering temporal profiles relying on summaries of the data across subjects or univariate analysis techniques studying one metabolite at a time, and fail to associate subgroups of subjects and subsets of metabolites with the dynamic time profile simultaneously.\&nbsp;In this study, we use NMR (nuclear magnetic resonance) spectroscopy measurements of plasma samples (of over three hundred individuals from the COPSAC2000 cohort) collected at multiple time points during a challenge test.\&nbsp; We use a multi-way analysis technique called the CANDECOMP/PARAFAC (CP) model to extract interpretable patterns from the time-resolved data. We compare the analysis of postprandial data, fasting state-corrected data and only fasting state data, and demonstrate the differences between different analysis approaches.\&nbsp;Our results show that the CP model reveals biologically meaningful patterns capturing how certain metabolite groups and their temporal profiles relate to various meta variables, in particular, BMI (body mass index), confirming already known biological knowledge as well as revealing new biological insights.},
	author = {Yan, Shi and Li, Lu and Horner, David and Chawes, Bo and Morten A. {Rasmussen} and Age K. {Smilde} and Evrim Acar {Ataman}}
}
@misc {42912,
	title = {Constrained Multimodal Data Mining},
	howpublished = {BigInsight Seminar, University of Oslo, Norway},
	year = {2022},
	url = {https://www.biginsight.no/events},
	author = {Evrim Acar {Ataman}}
}
@misc {42754,
	title = {Constrained Multi-Modal Data Mining Using Coupled Matrix and Tensor Factorizations},
	howpublished = {SIAM Conference on Mathematics of Data Science},
	year = {2022},
	type = {Minisymposium talk},
	url = {https://meetings.siam.org/sess/dsp_programsess.cfm?SESSIONCODE=74454},
	author = {Schenker, Carla and Roald, Marie and Wang, Xiulin and Jeremy E. {Cohen} and Evrim Acar {Ataman}}
}
@article {28151,
	title = {Exploring Dynamic Metabolomics Data With Multiway Data Analysis: a Simulation Study},
	journal = {BMC Bioinformatics},
	volume = {23},
	number = {Article 31},
	year = {2022},
	month = {2022},
	publisher = {Springer },
	abstract = {Background: Analysis of dynamic metabolomics data holds the promise to improve our understanding of underlying mechanisms in metabolism. For example, it may detect changes in metabolism due to the onset of a disease. Dynamic or time-resolved metabolomics data can be arranged as a three-way array with entries organized according to a subjects mode, a metabolites mode and a time mode. While such time-evolving multiway data sets are increasingly collected, revealing the underlying mechanisms and their dynamics from such data remains challenging. For such data, one of the complexities is the presence of a superposition of several sources of variation: induced variation (due to experimental conditions or inborn errors), individual variation, and measurement error. Multiway data analysis (also known as tensor factorizations) has been successfully used in data mining to find the underlying patterns in multiway data. In this paper, we study the use of multiway data analysis to reveal the underlying patterns and dynamics in time-resolved metabolomics data.Results: We focus on simulated data arising from different dynamic models of increasing complexity, i.e., a simple linear system, a yeast glycolysis model, and a human cholesterol model. We generate data with induced variation as well as individual variation. Systematic experiments are performed to demonstrate the advantages and limitations of multiway data analysis in analyzing such dynamic metabolomics data and their capacity to disentangle the different sources of variations. We choose to use simulations since we want to understand the capability of multiway data analysis methods which is facilitated by knowing the ground truth.Conclusion: Our numerical experiments demonstrate that despite the increasing complexity of the studied dynamic metabolic models, tensor factorization methods CANDECOMP/PARAFAC(CP) and Parallel Profiles with Linear Dependences (Paralind) can disentangle the sources of variations and thereby reveal the underlying mechanisms and their dynamics.},
	doi = {https://doi.org/10.1186/s12859-021-04550-5},
	author = {Li, Lu and Hoefsloot, Huub and Albert A. {de Graaf} and Evrim Acar {Ataman} and Age K. {Smilde}}
}
@misc {42958,
	title = {Extracting Insights from Complex Data: Data Mining using Tensor Factorizations},
	howpublished = {SILS (Swammerdam Institute for Life Sciences) Data Science Symposium, University of Amsterdam, Netherlands},
	year = {2022},
	author = {Evrim Acar {Ataman}}
}
@misc {42557,
	title = {A Flexible Framework for Coupled Matrix/Tensor Factorizations},
	howpublished = {TRICAP: Three-way methods In Chemistry And Psychology},
	year = {2022},
	author = {Evrim Acar {Ataman}}
}
@misc {43037,
	title = {Fully Constrained PARAFAC2 with AO-ADMM},
	howpublished = {SIAM Conference on Parallel Processing for Scientific Computing},
	year = {2022},
	author = {Roald, Marie and Schenker, Carla and Bro, Rasmus and Jeremy E. {Cohen} and Evrim Acar {Ataman}}
}
@article {43009,
	title = {Modeling Variation in Mobile Download Speed in Presence of Missing Samples},
	journal = {IEEE Transactions on Mobile Computing},
	year = {2022},
	pages = {1 - 16},
	publisher = { IEEE},
	issn = {1536-1233},
	doi = {10.1109/TMC.2022.3231928},
	url = {https://ieeexplore.ieee.org/document/9999262/http://xplorestaging.ieee.org/ielx7/7755/4358975/09999262.pdf?arnumber=9999262},
	author = {Fida, Mah-rukh and Roald, Marie and Evrim Acar {Ataman} and Elmokashfi, Ahmed}
}
@inproceedings {28466,
	title = {Multi-task FMRI Data Fusion using IVA and PARAFAC2},
	journal = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
	year = {2022},
	pages = {1466-1470},
	publisher = {IEEE},
	doi = {10.1109/ICASSP43922.2022.9747662},
	author = {Lehmann, Isabell and Evrim Acar {Ataman} and Hasija, Tanuj and Akhonda, M.A.B.S. and Vince D. {Calhoun} and Peter J. {Schreier} and Adali, T{\"u}lay}
}
@inproceedings {42523,
	title = {Phenotyping of cervical cancer risk groups via generalized low-rank models using medical questionnaires},
	journal = {Norwegian AI Symposium: Nordic Artificial Intelligence Research and Development},
	year = {2022},
	pages = {94--110},
	publisher = {Springer},
	doi = {https://doi.org/10.1007/978-3-031-17030-0_8},
	author = {Becker, Florian and Nyg{\r a}rd, Mari and Nyg{\r a}rd, Jan and Age K. {Smilde} and Evrim Acar {Ataman}}
}
@article {42450,
	title = {Reproducibility in Matrix and Tensor Decompositions: Focus on Model Match, Interpretability, and Uniqueness},
	journal = {IEEE Signal Processing Magazine },
	volume = {39},
	year = {2022},
	month = {06/2022},
	pages = {8-24},
	publisher = { IEEE},
	doi = {10.1109/MSP.2022.3163870},
	author = {Adali, T. and Kantar, Furkan and Mohammad Abu Bak Si {Akhonda} and Strother, Stephen and Vince D. {Calhoun} and Evrim Acar {Ataman}}
}
@misc {42656,
	title = {Revealing dynamic changes in metabolism through the analysis of postprandial metabolomics data: A simulation study},
	year = {2022},
	address = {Metabolomics 2022, Valencia, Spain},
	author = {Li, Lu and Yan, Shi and Barbara M. {Bakker} and Hoefsloot, Huub and Chawes, Bo and Morten A. {Rasmussen} and Age K. {Smilde} and Evrim Acar {Ataman}}
}
@article {28480,
	title = {Tracing Evolving Networks using Tensor Factorizations vs. ICA-based Approaches},
	journal = {Frontiers in Neuroscience},
	volume = {16},
	year = {2022},
	month = {04/2022},
	publisher = {Frontiers},
	doi = {10.3389/fnins.2022.861402},
	url = {https://www.frontiersin.org/article/10.3389/fnins.2022.861402 },
	author = {Evrim Acar {Ataman} and Roald, Marie and Khondoker M. {Hossain} and Vince D. {Calhoun} and Adali, T{\"u}lay}
}
@misc {42486,
	title = {Exploring dynamic metabolomics data with multiway data analysis: A simulation study},
	howpublished = {Virtual conference},
	year = {2021},
	publisher = {SIAM Conference on Applications of Dynamical Systems},
	abstract = {Analysis of dynamic metabolomics data sets holds the promise to improve our understanding of the underlying mechanisms in human metabolism. That is crucial to detect the changes in the metabolism that can potentially lead to diseases. Dynamic metabolomics data has more than two axes of variation, i.e., samples, metabolites and time. While such time-evolving multi-way data sets are collected more and more in recent years, revealing the underlying mechanisms and their dynamics from such data remains challenging.\&nbsp;\&nbsp;This talk will focus on a systematic study demonstrating the advantages and limitations of multi-way data analysis (also known as tensor factorizations) in terms of analyzing dynamic metabolomics data. We study different dynamic models of increasing complexity, i.e., a simple linear system, a yeast glycolysis model, a human cholesterol model, and generate data with different types of variation. Our numerical experiments demonstrate that despite the increasing complexity of the studied models, tensor factorization methods CANDECOMP/PARAFAC(CP) and PARAllel\&nbsp;Profiles\&nbsp;with\&nbsp;LINear\&nbsp;Dependences\&nbsp;(PARALIND) can reveal the underlying mechanisms and their dynamics.},
	author = {Li, Lu and Hoefsloot, Huub and Albert A. {de Graaf} and Evrim Acar {Ataman} and Age K. {Smilde}}
}
@misc {27904,
	title = {A Flexible Optimization Framework for Regularized Linearly Coupled Matrix-Tensor Factorizations based on the Alternating Direction Method of Multipliers},
	howpublished = {Europt21, 18th Workshop on Advances in Continuous Optimization},
	year = {2021},
	author = {Schenker, Carla and Jeremy E. {Cohen} and Evrim Acar {Ataman}}
}
@misc {28150,
	title = {A Flexible Optimization Framework for Regularized Matrix-Tensor Factorizations with Linear Couplings},
	howpublished = {Asilomar Conference on Signals,  Systems, and Computers},
	year = {2021},
	type = {Invited Session Talk},
	abstract = {An effective way of jointly analyzing data from multiple sources is through coupled matrix and tensor factorizations (CMTF). Different characteristics of datasets from multiple sources require to employ various regularizations, constraints, loss functions and different types of coupling structures between datasets. While existing algorithmic approaches for CMTF can incorporate constraints, linear couplings and different loss functions, none of them has been shown to achieve the flexibility to incorporate all. We propose a flexible algorithmic framework for coupled matrix and tensor factorizations, which utilizes Alternating Optimization (AO) and the Alternating Direction Method of Multipliers (ADMM). The framework facilitates the use of a variety of constraints, loss functions and couplings with linear transformations. Numerical experiments on simulated datasets and real data from chemometrics and hyperspectral super-resolution demonstrate that the proposed approach is accurate, flexible and computationally efficient with comparable or better performance than available CMTF algorithms.While we focus on CANDECOMP/PARAFAC (CP) {\textendash}based CMTF models, we will also briefly discuss the use of an AO-ADMM based algorithmic approach for fitting a PARAFAC2 model.\&nbsp; We demonstrate that the proposed algorithmic approach enables imposing constraints in all modes, which has been a challenge using the traditional alternating least squares-based algorithm used for PARAFAC2.},
	author = {Schenker, Carla and Roald, Marie and Jeremy E. {Cohen} and Evrim Acar {Ataman}}
}
@article {27572,
	title = {A Flexible Optimization Framework for Regularized Matrix-Tensor Factorizations with Linear Couplings},
	journal = {IEEE Journal of Selected Topics in Signal Processing 	},
	volume = {15},
	year = {2021},
	pages = {506 - 521},
	publisher = {IEEE},
	doi = {10.1109/JSTSP.2020.3045848},
	author = {Schenker, Carla and Jeremy E. {Cohen} and Evrim Acar {Ataman}}
}
@misc {27897,
	title = {From Data Mining using Tensor Factorizations to Multimodal Data Mining using Coupled Matrix/Tensor Factorizations},
	howpublished = {Nordic Probabilistic AI School (virtual)},
	year = {2021},
	url = {https://probabilistic.ai/},
	author = {Evrim Acar {Ataman}}
}
@misc {28185,
	title = {Generalized Low-Rank Models for Phenotyping Cervical Cancer Risk Groups using Medical Questionnaires},
	howpublished = {Stavanger, Norway},
	year = {2021},
	author = {Becker, Florian},
	editor = {Nyg{\r a}rd, Mari and Nyg{\r a}rd, Jan and Age K. {Smilde} and Evrim Acar {Ataman}}
}
@misc {27903,
	title = {An Optimization Framework for Regularized Linearly Coupled Matrix-Tensor Factorization},
	year = {2021},
	address = {2020 28th European Signal Processing Conference (EUSIPCO), Amsterdam, Netherlands},
	author = {Schenker, Carla and Jeremy E. {Cohen} and Evrim Acar {Ataman}}
}
@misc {27902,
	title = {An Optimization Framework for Regularized Linearly Coupled Matrix-Tensor Factorization},
	howpublished = {SIAM Conference on Applied Linear Algebra (LA21)},
	year = {2021},
	type = {Minisymposium},
	author = {Schenker, Carla and Cohen, Jeremy and Evrim Acar {Ataman}}
}
@inproceedings {28153,
	title = {PARAFAC2 AO-ADMM: Constraints in all modes},
	journal = {2021 29th European Signal Processing Conference (EUSIPCO)},
	year = {2021},
	pages = {1040-1044},
	publisher = {IEEE},
	author = {Roald, Marie and Schenker, Carla and Jeremy E. {Cohen} and Evrim Acar {Ataman}}
}
@misc {27905,
	title = {Tracing Dynamic Networks through Constrained Parafac2 Decomposition},
	howpublished = {SIAM Conference on Applied Linear Algebra (LA21), Virtual Conference},
	year = {2021},
	publisher = {SIAM},
	abstract = {Time-evolving data analysis is crucial for understanding complex systems such as the brain. Methods that assume static networks have successfully recovered spatial networks of connectivity from neuroimaging data. Still, discovering both underlying networks and their evolution is a challenging task.To capture temporal evolution of connectivity networks, we arrange dynamic data as a tensor and use a tensor factorization method called PARAFAC2. PARAFAC2 deciphers the hidden structure of dynamic networks and yields unique and interpretable components. Preliminary results using PARAFAC2 in neuroimaging data analysis are promising. However, the constant cross-product constraint on the time-evolving mode hinders the use of additional constraints or regularization (e.g. spatial smoothness) on this mode. Currently, the only way to regularize the time-evolving mode of a PARAFAC2 model is with a flexible coupling approach, which finds the solution through regularized least-squares subproblems. Instead, we use an alternating direction method of multipliers (ADMM) based approach to widen the possible regularization penalties to any proximable function.Our numerical experiments demonstrate that the proposed ADMM-based algorithmic approach for PARAFAC2 can accurately recover the underlying evolving components, is flexible in terms of imposing constraints and also computationally efficient.},
	author = {Roald, Marie and Schenker, Carla and Jeremy E. {Cohen} and Evrim Acar {Ataman}}
}
@article {27392,
	title = {Cross-product penalized component analysis (X-CAN)},
	journal = {Chemometrics and Intelligent Laboratory Systems},
	volume = {203},
	year = {2020},
	month = {06/2020},
	pages = {104038},
	publisher = {Elsevier},
	issn = {01697439},
	doi = {10.1016/j.chemolab.2020.104038},
	author = {Camacho, Jose and Evrim Acar {Ataman} and Morten A. {Rasmussen} and Bro, Rasmus}
}
@misc {27573,
	title = {Multi Modal Data Mining using Coupled Matrix/Tensor Factorizations},
	howpublished = {Tufts University - TRIPODS Seminar (virtual)},
	year = {2020},
	url = {https://tripods.tufts.edu/about/events/multi-modal-data-mining-using-coupled-matrixtensor-factorizations},
	author = {Evrim Acar {Ataman}}
}
@inbook {27296,
	title = {Multilinear Models, Iterative Methods},
	booktitle = {Comprehensive Chemometrics (Second Edition)},
	series = {Chemical and Biochemical Data Analysis},
	year = {2020},
	pages = {267-304},
	publisher = {Elsevier},
	organization = {Elsevier},
	abstract = {In this section, multilinear models for multi-way arrays requiring iterative fitting algorithms are outlined. Among them: the PARAFAC (PARAllel FACtor analysis) model and one of its variants (the PARAFAC2 model); Tucker models in which one or more modes are reduced (viz., the N-way Tucker-N and Tucker-m models); hybrid models having intermediate properties between PARAFAC and Tucker ones; and coupled matrix and tensor decompositions (CMTF) which simultaneously decomposes multiple tensors. Five examples are included as to illustrate some practical aspects concerning the use of these models on analytical data.},
	keywords = {-way Tucker models, CANDECOMP, Curve resolution, Exploratory analysis, Least squares, Linked mode PARAFAC, Multi-way analysis, Multi-way array, Multilinear model, PARAFAC, PARAFAC2, PARALIND, Restricted Tucker models, Tensor decomposition, Tensor-matrix factorization},
	isbn = {978-0-12-409547-2},
	doi = {https://doi.org/10.1016/B978-0-12-409547-2.14609-8},
	url = {http://www.sciencedirect.com/science/article/pii/B9780124095472146098},
	author = {Tomasi, Giorgio and Evrim Acar {Ataman} and Bro, Rasmus},
	editor = {Brown, Steven and Tauler, Roma and Walczak, Beata}
}
@article {27435,
	title = {Multi-Linear Population Analysis (MLPA) of LFP Data Using Tensor Decompositions},
	journal = {Frontiers in Applied Mathematics and Statistics},
	volume = {6},
	number = {41},
	year = {2020},
	month = {Aug-09-2020},
	publisher = {Frontiers},
	keywords = {CANDECOMP/PARAFAC, independent component analysis (ICA), local field potential (LFP), Neuroscience, population rate model, principal component analysis (PCA), tensor decompositions},
	doi = {10.3389/fams.2020.00041},
	url = {https://www.frontiersin.org/article/10.3389/fams.2020.00041/fullhttps://www.frontiersin.org/article/10.3389/fams.2020.00041/full},
	author = {Geddes, Justen and Gaute T. {Einevoll} and Evrim Acar {Ataman} and Alexander J. {Stasik}}
}
@inproceedings {27489,
	title = {An Optimization Framework for RegularizedLinearly Coupled Matrix-Tensor Factorization},
	journal = {European Signal Processing Conference (EUSIPCO)},
	year = {2020},
	pages = {985-989},
	publisher = {IEEE},
	url = {https://www.eurasip.org/Proceedings/Eusipco/Eusipco2020/pdfs/0000985.pdf},
	author = {Schenker, Carla and Jeremy E. {Cohen} and Evrim Acar {Ataman}}
}
@inproceedings {27295,
	title = {Tracing Network Evolution Using The Parafac2 Model},
	journal = {2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
	year = {2020},
	publisher = {IEEE},
	address = {Barcelona, Spain},
	doi = {10.1109/ICASSP40776.202010.1109/ICASSP40776.2020.9053902},
	url = {https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9040208https://ieeexplore.ieee.org/document/9053902/http://xplorestaging.ieee.org/ielx7/9040208/9052899/09053902.pdf?arnumber=9053902},
	author = {Roald, Marie and Bhinge, Suchita and Jia, Chunying and Calhoun, Vince and Adali, T{\"u}lay and Evrim Acar {Ataman}}
}
@misc {26648,
	title = {Biomarker Discovery through Structure-Revealing Fusion of Multi-Modal Neuroimaging Data},
	howpublished = {IEEE EMBC (Engineering in Medicine and Biology Conference), Berlin, Germany},
	year = {2019},
	type = {Invited Session Paper},
	author = {Evrim Acar {Ataman} and Schenker, Carla and Levin-Schwartz, Yuri and Vince D. {Calhoun} and Adali, T{\"u}lay}
}
@article {AcGuKh18,
	title = {Biomarkers of individual foods, and separation of diets using untargeted LC-MS based plasma metabolomics in a randomized controlled trial},
	journal = {Molecular Nutrition \& Food Research},
	volume = {63},
	number = {1800215},
	year = {2019},
	pages = {1-10},
	publisher = { Wiley},
	doi = {10.1002/mnfr.201800215},
	author = {Evrim Acar {Ataman} and Gurdeniz, Gozde and Khakimov, Bekzod and Savorani, Francesco and Sanne Kellebjerg {Korndal} and Thomas Meinert {Larsen} and S{\o}ren Balling {Engelsen} and Astrup, Arne and Lars O. {Dragsted}}
}
@inproceedings {26945,
	title = {Multiway Reliability Analysis of Mobile Broadband Networks},
	journal = {the Internet Measurement ConferenceProceedings of the Internet Measurement Conference on   - IMC {\textquoteright}19},
	year = {2019},
	pages = {358-364},
	publisher = {ACM Press},
	address = {New York, NY, USA},
	isbn = {9781450369480},
	doi = {10.1145/335536910.1145/3355369.3355591},
	url = {http://dl.acm.org/citation.cfm?doid=3355369http://dl.acm.org/citation.cfm?doid=3355369.3355591http://dl.acm.org/ft_gateway.cfm?id=3355591\&ftid=2091201\&dwn=1},
	author = {Fida, Mah-rukh and Evrim Acar {Ataman} and Elmokashfi, Ahmed}
}
@misc {26647,
	title = {Unraveling Biomarkers through Multi-Modal Data Fusion},
	howpublished = {IEEE ISMICT (International Symposium on Medical Information and Communication Technology), Oslo, Norway},
	year = {2019},
	type = {Invited},
	author = {Evrim Acar {Ataman}}
}
@article {26539,
	title = {Unraveling Diagnostic Biomarkers of Schizophrenia Through Structure-Revealing Fusion of Multi-Modal Neuroimaging Data},
	journal = {Frontiers in Neuroscience},
	volume = {13},
	year = {2019},
	month = {Mar-05-2019},
	publisher = {Frontiers},
	doi = {10.3389/fnins.2019.00416},
	author = {Evrim Acar {Ataman} and Schenker, Carla and Levin-Schwartz, Yuri and Vince D. {Calhoun} and Adali, T{\"u}lay}
}
@misc {26649,
	title = {Unraveling Interpretable Patterns  through Data Fusion based on Coupled Matrix and Tensor Factorizations},
	howpublished = {KDD Workshop on Tensor Methods for Emerging Data Science Challenges, Anchorage, Alaska, USA},
	year = {2019},
	type = {Keynote},
	author = {Evrim Acar {Ataman}}
}
@misc {26944,
	title = {Unraveling Interpretable Patterns  through Data Fusion based on Coupled Matrix and Tensor Factorizations},
	howpublished = {AI and Tensor Factorizations for Physical, Chemical, and Biological Systems, Santa Fe, NM, USA},
	year = {2019},
	type = {Invited},
	author = {Evrim Acar {Ataman}}
}
@misc {26185,
	title = {Data Fusion based on Coupled Matrix and Tensor Factorizations},
	howpublished = {5th Conference on Constraint-Based Reconstruction and Analysis (COBRA 2018), Seattle, USA},
	year = {2018},
	url = {https://www.aiche.org/sbe/conferences/conference-on-constraint-based-reconstruction-and-analysis-cobra/2018},
	author = {Evrim Acar {Ataman}}
}
@article {26218,
	title = {The molecular fingerprint of fluorescent natural organic matter offers insight into biogeochemical sources and diagenetic state},
	journal = {Analytical Chemistry},
	volume = {90},
	year = {2018},
	pages = {14188{\textendash}14197},
	publisher = {ACS},
	issn = {0003-2700},
	doi = {10.1021/acs.analchem.8b02863},
	url = {http://pubs.acs.org/doi/10.1021/acs.analchem.8b02863http://pubs.acs.org/doi/pdf/10.1021/acs.analchem.8b02863},
	author = {W{\"u}nsch, Urban and Evrim Acar {Ataman} and Boris Peter {Koch} and Murphy, Kathleen and Schmitt-Kopplin, Philippe and Colin Andrew {Stedmon}}
}
@misc {26187,
	title = {Structure-Revealing Data Fusion Models based on Coupled Matrix and Tensor Factorizations and Their Applications},
	howpublished = {Three-way Methods in Chemistry and Psychology (TRICAP), New Mexico, USA},
	year = {2018},
	url = {https://www.sandia.gov/tricap2018/Home.html},
	author = {Evrim Acar {Ataman}}
}
@misc {42558,
	title = {Structure-Revealing Data Fusion Models based on Coupled Matrix and Tensor Factorizations and Their Applications},
	howpublished = {TRICAP: Three-way methods In Chemistry And Psychology},
	year = {2018},
	author = {Evrim Acar {Ataman}}
}
@misc {26186,
	title = {Tutorial on Tensor Factorizations, Data Fusion \& Applications},
	howpublished = {14th International Conference on Latent Variable Analysis and Signal Separation, Guildford, UK},
	year = {2018},
	type = {Tutorial},
	url = {http://cvssp.org/events/lva-ica-2018/tutorials/},
	author = {Evrim Acar {Ataman}}
}
@inproceedings {2017_EUSIPCO,
	title = {ACMTF for Fusion of Multi-Modal Neuroimaging Data and Identification of Biomarkers},
	journal = {EUSIPCO 2017: Proceedings of the 25th European Signal Processing Conference},
	year = {2017},
	publisher = {IEEE},
	doi = {10.23919/EUSIPCO.2017.8081286},
	author = {Evrim Acar {Ataman} and Levin-Schwartz, Y. and Calhoun, V.D. and Adali, T.}
}
@article {26222,
	title = {Common and distinct components in data fusion},
	journal = {Journal of Chemometrics},
	volume = {31},
	year = {2017},
	month = {Jan-07-2017},
	pages = {e2900},
	publisher = { Wiley},
	doi = {10.1002/cem.v31.710.1002/cem.2900},
	url = {http://doi.wiley.com/10.1002/cem.v31.7http://doi.wiley.com/10.1002/cem.2900https://onlinelibrary.wiley.com/doi/full/10.1002/cem.2900https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002\%2Fcem.2900},
	author = {Age K. {Smilde} and M{\r a}ge, Ingrid and Naes, Tormod and Hankemeier, Thomas and Mirjam Anne {Lips} and Henk A. L. {Kiers} and Evrim Acar {Ataman} and Bro, Rasmus}
}
@article {26221,
	title = {Forecasting Chronic Diseases Using Data Fusion},
	journal = {Journal of Proteome Research},
	volume = {16},
	year = {2017},
	month = {Jul-07-2017},
	pages = {2435 - 2444},
	publisher = {ACS},
	issn = {1535-3893},
	doi = {10.1021/acs.jproteome.7b00039},
	url = {http://pubs.acs.org/doi/10.1021/acs.jproteome.7b00039http://pubs.acs.org/doi/pdf/10.1021/acs.jproteome.7b00039},
	author = {Evrim Acar {Ataman} and G{\"u}rdeniz, G{\"o}zde and Savorani, Francesco and Hansen, Louise and Olsen, Anja and Tj{\o}nneland, Anne and Lars Ove {Dragsted} and Bro, Rasmus}
}
@inproceedings {2017_ISCAS,
	title = {Tensor-Based Fusion of EEG and FMRI to Understand Neurological Changes in Schizophrenia},
	journal = {ISCAS 2017: Proceedings of IEEE International Symposium on Circuits and Systems},
	year = {2017},
	pages = {314-317},
	publisher = {IEEE},
	doi = { 10.1109/ISCAS.2017.8050303},
	author = {Evrim Acar {Ataman} and Levin-Schwartz, Y. and V. D. {Calhoun} and Adal?, T.}
}
