eISSN:
 2639-8001

All Issues

Volume 4, 2022

Volume 3, 2021

Volume 2, 2020

Volume 1, 2019





FoDS Flyer: showing all essential information of the journal.
Foundations of Data Science (FoDS) invites submissions focusing on advances in mathematical, statistical, and computational methods for data science. Results should significantly advance current understanding of data science, by algorithm development, analysis, and/or computational implementation which demonstrates behavior and applicability of the algorithm. Fields covered by the journal include, but are not limited to Bayesian Statistics, High Performance Computing, Inverse Problems, Data Assimilation, Machine Learning, Optimization, Topological Data Analysis, Spatial Statistics, Nonparametric Statistics, Uncertainty Quantification, and Data Centric Engineering. Expository and review articles are welcome. Papers which focus on applications in science and engineering are also encouraged, however the method(s) used should be applicable outside of one specific application domain.

Call for Papers: Special Issue on "Data Science Education Research" of Foundations of Data Science (click to view details)

Call for Papers: Special Issue on "Topological methods in data analysis, machine learning and artificial intelligence" of Foundations of Data Science (click to view details)

Call for Papers: Special Issue on "Sequential Monte Carlo Methods" of Foundations of Data Science (click to view details)

Call for Papers: Special Issue on "Scientific Machine Learning" of Foundations of Data Science (click to view details)

Select all articles

Export/Reference:

Optimization and learning with nonlocal calculus
Sriram Nagaraj
2022, 4(3) : 323-353 doi: 10.3934/fods.2022009 +[Abstract](470) +[HTML](126) +[PDF](768.74KB)
Reconsider phase reconstruction in signals with dynamic periodicity from the modern signal processing perspective
Aymen Alian, Yu-Lun Lo, Kirk Shelley and Hau-Tieng Wu
2022, 4(3) : 355-393 doi: 10.3934/fods.2022010 +[Abstract](440) +[HTML](160) +[PDF](7649.88KB)
Multimodal correlations-based data clustering
Jia Chen and Ioannis D. Schizas
2022, 4(3) : 395-422 doi: 10.3934/fods.2022011 +[Abstract](634) +[HTML](172) +[PDF](874.32KB)
Learning in high-dimensional feature spaces using ANOVA-based fast matrix-vector multiplication
Franziska Nestler, Martin Stoll and Theresa Wagner
2022, 4(3) : 423-440 doi: 10.3934/fods.2022012 +[Abstract](328) +[HTML](77) +[PDF](3113.98KB)
Geometric structure guided model and algorithms for complete deconvolution of gene expression data
Duan Chen, Shaoyu Li and Xue Wang
2022, 4(3) : 441-466 doi: 10.3934/fods.2022013 +[Abstract](262) +[HTML](146) +[PDF](10210.58KB)
The (homological) persistence of gerrymandering
Moon Duchin, Tom Needham and Thomas Weighill
2021doi: 10.3934/fods.2021007 +[Abstract](1996) +[HTML](823) +[PDF](23416.84KB)
Intrinsic disease maps using persistent cohomology
Daniel Amin and Mikael Vejdemo-Johansson
2021doi: 10.3934/fods.2021008 +[Abstract](1452) +[HTML](728) +[PDF](720.82KB)
A density-based approach to feature detection in persistence diagrams for firn data
Austin Lawson, Tyler Hoffman, Yu-Min Chung, Kaitlin Keegan and Sarah Day
2021doi: 10.3934/fods.2021012 +[Abstract](1170) +[HTML](693) +[PDF](5777.15KB)
ToFU: Topology functional units for deep learning
Christopher Oballe, David Boothe, Piotr J. Franaszczuk and Vasileios Maroulas
2021doi: 10.3934/fods.2021021 +[Abstract](1055) +[HTML](591) +[PDF](1001.97KB)
Reconstructing linearly embedded graphs: A first step to stratified space learning
Yossi Bokor, Katharine Turner and Christopher Williams
2021doi: 10.3934/fods.2021026 +[Abstract](946) +[HTML](540) +[PDF](880.46KB)
Euler characteristic surfaces
Gabriele Beltramo, Primoz Skraba, Rayna Andreeva, Rik Sarkar, Ylenia Giarratano and Miguel O. Bernabeu
2021doi: 10.3934/fods.2021027 +[Abstract](1752) +[HTML](475) +[PDF](6801.05KB)
Evaluation of EDISON's data science competency framework through a comparative literature analysis
Karl R. B. Schmitt, Linda Clark, Katherine M. Kinnaird, Ruth E. H. Wertz and Björn Sandstede
2021doi: 10.3934/fods.2021031 +[Abstract](1063) +[HTML](485) +[PDF](915.18KB)
Facilitating API lookup for novices learning data wrangling using thumbnail graphics
Lovisa Sundin, Nourhan Sakr, Juho Leinonen and Quintin Cutts
2021doi: 10.3934/fods.2021032 +[Abstract](690) +[HTML](470) +[PDF](1400.58KB)
Addressing confirmation bias in middle school data science education
Sarai Hedges and Kim Given
2022doi: 10.3934/fods.2021035 +[Abstract](1546) +[HTML](281) +[PDF](253.67KB)
Facilitating team-based data science: Lessons learned from the DSC-WAV project
Chelsey Legacy, Andrew Zieffler, Benjamin S. Baumer, Valerie Barr and Nicholas J. Horton
2022doi: 10.3934/fods.2022003 +[Abstract](848) +[HTML](237) +[PDF](969.8KB)
Applying topological data analysis to local search problems
Erik Carlsson, John Gunnar Carlsson and Shannon Sweitzer
2022doi: 10.3934/fods.2022006 +[Abstract](600) +[HTML](275) +[PDF](850.76KB)
Statistical inference for persistent homology applied to simulated fMRI time series data
Hassan Abdallah, Adam Regalski, Mohammad Behzad Kang, Maria Berishaj, Nkechi Nnadi, Asadur Chowdury, Vaibhav A. Diwadkar and Andrew Salch
2022doi: 10.3934/fods.2022014 +[Abstract](226) +[HTML](68) +[PDF](3545.81KB)
Persistent path Laplacian
Rui Wang and Guo-Wei Wei
2022doi: 10.3934/fods.2022015 +[Abstract](250) +[HTML](115) +[PDF](24207.02KB)
Machine learning-based conditional mean filter: A generalization of the ensemble Kalman filter for nonlinear data assimilation
Truong-Vinh Hoang, Sebastian Krumscheid, Hermann G. Matthies and Raúl Tempone
2022doi: 10.3934/fods.2022016 +[Abstract](195) +[HTML](58) +[PDF](660.83KB)
Support vector machines and Radon's theorem
Henry Adams, Elin Farnell and Brittany Story
2022doi: 10.3934/fods.2022017 +[Abstract](89) +[HTML](29) +[PDF](503.86KB)
Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization
Marc Bocquet, Julien Brajard, Alberto Carrassi and Laurent Bertino
2020, 2(1) : 55-80 doi: 10.3934/fods.2020004 +[Abstract](5388) +[HTML](1544) +[PDF](800.0KB) Cited By(10)
Consistent manifold representation for topological data analysis
Tyrus Berry and Timothy Sauer
2019, 1(1) : 1-38 doi: 10.3934/fods.2019001 +[Abstract](5926) +[HTML](2610) +[PDF](3141.49KB) Cited By(3)
Semi-supervised classification on graphs using explicit diffusion dynamics
Robert L. Peach, Alexis Arnaudon and Mauricio Barahona
2020, 2(1) : 19-33 doi: 10.3934/fods.2020002 +[Abstract](3289) +[HTML](1416) +[PDF](347.25KB) Cited By(3)
Accelerating Metropolis-Hastings algorithms by Delayed Acceptance
Marco Banterle, Clara Grazian, Anthony Lee and Christian P. Robert
2019, 1(2) : 103-128 doi: 10.3934/fods.2019005 +[Abstract](4899) +[HTML](2456) +[PDF](685.26KB) Cited By(2)
Power weighted shortest paths for clustering Euclidean data
Daniel Mckenzie and Steven Damelin
2019, 1(3) : 307-327 doi: 10.3934/fods.2019014 +[Abstract](3102) +[HTML](1527) +[PDF](663.53KB) Cited By(2)
Partitioned integrators for thermodynamic parameterization of neural networks
Benedict Leimkuhler, Charles Matthews and Tiffany Vlaar
2019, 1(4) : 457-489 doi: 10.3934/fods.2019019 +[Abstract](3321) +[HTML](1409) +[PDF](10550.03KB) Cited By(2)
Learning by active nonlinear diffusion
Mauro Maggioni and James M. Murphy
2019, 1(3) : 271-291 doi: 10.3934/fods.2019012 +[Abstract](3946) +[HTML](1564) +[PDF](4001.74KB) Cited By(2)
Levels and trends in the sex ratio at birth and missing female births for 29 states and union territories in India 1990–2016: A Bayesian modeling study
Fengqing Chao and Ajit Kumar Yadav
2019, 1(2) : 177-196 doi: 10.3934/fods.2019008 +[Abstract](4361) +[HTML](1557) +[PDF](2577.91KB) Cited By(2)
Multi-fidelity generative deep learning turbulent flows
Nicholas Geneva and Nicholas Zabaras
2020, 2(4) : 391-428 doi: 10.3934/fods.2020019 +[Abstract](3090) +[HTML](921) +[PDF](14569.45KB) Cited By(1)
On the incorporation of box-constraints for ensemble Kalman inversion
Neil K. Chada, Claudia Schillings and Simon Weissmann
2019, 1(4) : 433-456 doi: 10.3934/fods.2019018 +[Abstract](2402) +[HTML](1361) +[PDF](1289.35KB) Cited By(1)
Issues using logistic regression with class imbalance, with a case study from credit risk modelling
Yazhe Li, Tony Bellotti and Niall Adams
2019, 1(4) : 389-417 doi: 10.3934/fods.2019016 +[Abstract](5555) +[HTML](1644) +[PDF](4084.46KB) PDF Downloads(4076)
Addressing confirmation bias in middle school data science education
Sarai Hedges and Kim Given
2022, 0(0) : 0 doi: 10.3934/fods.2021035 +[Abstract](1546) +[HTML](281) +[PDF](253.67KB) PDF Downloads(2957)
Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization
Marc Bocquet, Julien Brajard, Alberto Carrassi and Laurent Bertino
2020, 2(1) : 55-80 doi: 10.3934/fods.2020004 +[Abstract](5388) +[HTML](1544) +[PDF](800.0KB) PDF Downloads(1281)
Stochastic gradient descent algorithm for stochastic optimization in solving analytic continuation problems
Feng Bao and Thomas Maier
2020, 2(1) : 1-17 doi: 10.3934/fods.2020001 +[Abstract](4080) +[HTML](1552) +[PDF](418.16KB) PDF Downloads(828)
Multi-fidelity generative deep learning turbulent flows
Nicholas Geneva and Nicholas Zabaras
2020, 2(4) : 391-428 doi: 10.3934/fods.2020019 +[Abstract](3090) +[HTML](921) +[PDF](14569.45KB) PDF Downloads(759)
The (homological) persistence of gerrymandering
Moon Duchin, Tom Needham and Thomas Weighill
2021, 0(0) : 0 doi: 10.3934/fods.2021007 +[Abstract](1996) +[HTML](823) +[PDF](23416.84KB) PDF Downloads(672)
A density-based approach to feature detection in persistence diagrams for firn data
Austin Lawson, Tyler Hoffman, Yu-Min Chung, Kaitlin Keegan and Sarah Day
2021, 0(0) : 0 doi: 10.3934/fods.2021012 +[Abstract](1170) +[HTML](693) +[PDF](5777.15KB) PDF Downloads(651)
The rankability of weighted data from pairwise comparisons
Paul E. Anderson, Timothy P. Chartier, Amy N. Langville and Kathryn E. Pedings-Behling
2021, 3(1) : 1-26 doi: 10.3934/fods.2021002 +[Abstract](1632) +[HTML](692) +[PDF](3603.92KB) PDF Downloads(619)
Probabilistic learning on manifolds
Christian Soize and Roger Ghanem
2020, 2(3) : 279-307 doi: 10.3934/fods.2020013 +[Abstract](3042) +[HTML](1415) +[PDF](722.55KB) PDF Downloads(601)
ToFU: Topology functional units for deep learning
Christopher Oballe, David Boothe, Piotr J. Franaszczuk and Vasileios Maroulas
2021, 0(0) : 0 doi: 10.3934/fods.2021021 +[Abstract](1055) +[HTML](591) +[PDF](1001.97KB) PDF Downloads(583)

Editors

Referees

Librarians

Special Issues

Email Alert

[Back to Top]