This research area covers mathematics and computational methods in data science that includes machine learning and data mining, intertwined with other key areas such as statistics, computer science, network science, and signal processing - all within the context of data science as well as applications of mathematical methods in data/information processing to science and engineering problems.

Upcoming Related Conferences

Upcoming Related Conferences

SIAM Conference on Mathematics of Data Science (MDS20)

May 5 - 7, 2020 Cincinnati, Ohio, U.S. More Information

Research Area Announcements

Data Science Book Series

Did you know – SIAM recently launched a new Data Science Book Series! Data science is a forward-looking field and is seeing tremendous energy and growth. Have book ideas? We want to hear about them.

The prize was established in 1986 in memory of Richard C. DiPrima, who served SIAM for many years and in 1979–1980 as SIAM President. It aims to recognize an early career researcher in applied mathematics and is based on the doctoral dissertation.

This joint prize was established in 2002 to honor Sonia Kovalevsky and her work on the theory of differential equations. It is awarded to anyone in the scientific or engineering community whose work highlights the achievements of women in applied and computational mathematics. Nominations can be submitted via the AWM website.

Data science is everywhere, but how does it actually work ? SIAM’s newest journal, SIAM Journal on Mathematics of Data Science (SIMODS), aims to understand the deep inner workings of machine ...

By Jeremy E. Cohen and Nicolas Gillis SIAM Journal on Mathematics of Data Science, Volume 1, Issue 3, Page 518-536, January 2019. Sparse component analysis (SCA), also known as complete dictionary learning, is...

By Amelia Perry, Jonathan Weed, Afonso S. Bandeira, Philippe Rigollet, and Amit Singer SIAM Journal on Mathematics of Data Science, Volume 1, Issue 3, Page 497-517, January 2019. The growing role of data-driven approaches to scientific discovery has unveiled a large class...

Sponsored by the SIAM Activity Group on Data Mining and Analytics .
This conference is held in cooperation with the American Statistical Association .
This meeting is co-located with ...

SIAM is proud to introduce the first Conference on Mathematics of Data Science (MDS). The meeting, which will be held May 5-7, 2020, in Cincinnati, Ohio, intends to provide a forum for attendees ...

By Michael W. Mahoney
The Transdisciplinary Research in Principles of Data Science (TRIPODS) program is an effort funded by the National Science Foundation (NSF) to establish the foundations ...

By Wei Zhu, Qiang Qiu, Bao Wang, Jianfeng Lu, Guillermo Sapiro, and Ingrid Daubechies SIAM Journal on Mathematics of Data Science, Volume 1, Issue 3, Page 476-496, January 2019. Deep neural networks (DNNs) typically have enough capacity to fit random data by brute...

By François Malgouyres and Joseph Landsberg SIAM Journal on Mathematics of Data Science, Volume 1, Issue 3, Page 446-475, January 2019. We study a deep linear network endowed with the following structure: A matrix $X$...

By D. Russell Luke, Shoham Sabach, and Marc Teboulle SIAM Journal on Mathematics of Data Science, Volume 1, Issue 3, Page 408-445, January 2019. We present a unified treatment of the abstract problem of finding the best approximation...

By Jie Shen, Jie Xu, and Jiang Yang SIAM Review, Volume 61, Issue 3, Page 474-506, January 2019. We propose a new numerical technique to deal with nonlinear terms in gradient flows. By introducing...

By Arjun S. Ramani, Nicole Eikmeier, and David F. Gleich SIAM Review, Volume 61, Issue 3, Page 549-595, January 2019. Common models for random graphs, such as Erdös--Rényi and Kronecker graphs, correspond to generating random adjacency matrices where each...

By Johan S. H. van Leeuwaarden, Britt W. J. Mathijsen, and Bert Zwart SIAM Review, Volume 61, Issue 3, Page 403-440, January 2019. Multiserver queueing systems describe situations in which users require service from multiple parallel servers. Examples include check-in lines...

By Ben Adcock and Daan Huybrechs SIAM Review, Volume 61, Issue 3, Page 443-473, January 2019. Functions of one or more variables are usually approximated with a basis: a complete, linearly independent system of functions...

By T. J. Dodwell, C. Ketelsen, R. Scheichl, and A. L. Teckentrup SIAM Review, Volume 61, Issue 3, Page 509-545, January 2019. In this paper we address the problem of the prohibitively large computational cost of existing Markov chain Monte Carlo...

By Roger Cropp and John Norbury SIAM Review, Volume 61, Issue 3, Page 596-622, January 2019. Obligate mutualist interactions appear to be ubiquitous in nature but cannot be described by the simple models that have...

By The Editors SIAM Review, Volume 61, Issue 3, Page 507-507, January 2019. In this section we present “Multilevel Markov Chain Monte Carlo,” by T. J. Dodwell, C. Ketelsen, R. Scheichl, and...

By Jeremy E. Cohen and Nicolas Gillis SIAM Journal on Mathematics of Data Science, Volume 1, Issue 3, Page 518-536, January 2019. Sparse component analysis (SCA), also known as complete dictionary learning, is...

By Amelia Perry, Jonathan Weed, Afonso S. Bandeira, Philippe Rigollet, and Amit Singer SIAM Journal on Mathematics of Data Science, Volume 1, Issue 3, Page 497-517, January 2019. The growing role of data-driven approaches to scientific discovery has unveiled a large class...

By Wei Zhu, Qiang Qiu, Bao Wang, Jianfeng Lu, Guillermo Sapiro, and Ingrid Daubechies SIAM Journal on Mathematics of Data Science, Volume 1, Issue 3, Page 476-496, January 2019. Deep neural networks (DNNs) typically have enough capacity to fit random data by brute...

By François Malgouyres and Joseph Landsberg SIAM Journal on Mathematics of Data Science, Volume 1, Issue 3, Page 446-475, January 2019. We study a deep linear network endowed with the following structure: A matrix $X$...

By D. Russell Luke, Shoham Sabach, and Marc Teboulle SIAM Journal on Mathematics of Data Science, Volume 1, Issue 3, Page 408-445, January 2019. We present a unified treatment of the abstract problem of finding the best approximation...

By Jie Shen, Jie Xu, and Jiang Yang SIAM Review, Volume 61, Issue 3, Page 474-506, January 2019. We propose a new numerical technique to deal with nonlinear terms in gradient flows. By introducing...

By Arjun S. Ramani, Nicole Eikmeier, and David F. Gleich SIAM Review, Volume 61, Issue 3, Page 549-595, January 2019. Common models for random graphs, such as Erdös--Rényi and Kronecker graphs, correspond to generating random adjacency matrices where each...

By Johan S. H. van Leeuwaarden, Britt W. J. Mathijsen, and Bert Zwart SIAM Review, Volume 61, Issue 3, Page 403-440, January 2019. Multiserver queueing systems describe situations in which users require service from multiple parallel servers. Examples include check-in lines...

By Ben Adcock and Daan Huybrechs SIAM Review, Volume 61, Issue 3, Page 443-473, January 2019. Functions of one or more variables are usually approximated with a basis: a complete, linearly independent system of functions...

By T. J. Dodwell, C. Ketelsen, R. Scheichl, and A. L. Teckentrup SIAM Review, Volume 61, Issue 3, Page 509-545, January 2019. In this paper we address the problem of the prohibitively large computational cost of existing Markov chain Monte Carlo...

By Roger Cropp and John Norbury SIAM Review, Volume 61, Issue 3, Page 596-622, January 2019. Obligate mutualist interactions appear to be ubiquitous in nature but cannot be described by the simple models that have...

By The Editors SIAM Review, Volume 61, Issue 3, Page 507-507, January 2019. In this section we present “Multilevel Markov Chain Monte Carlo,” by T. J. Dodwell, C. Ketelsen, R. Scheichl, and...

By Volker H. Schulz SIAM Review, Volume 61, Issue 3, Page 625-640, January 2019. This time, the Book Reviews section has a large portion of books devoted to system dynamics and applications. The...

By Misha E. Kilmer SIAM Review, Volume 61, Issue 3, Page 441-441, January 2019. The issue of accurate function approximation is an important topic in numerical analysis. The first Research Spotlights article,...

By J. M. Sanz-Serna SIAM Review, Volume 61, Issue 3, Page 401-401, January 2019. Johan S. H. van Leeuwaarden, Britt W. J. Mathijsen, and Bert Zwart are the authors of the Survey and...

By Darinka Dentcheva SIAM Review, Volume 61, Issue 3, Page 547-548, January 2019. This issue of SIAM Review presents two papers in the Education section. The first is “Coin-Flipping, Ball-Dropping, and Grass-Hopping...

By Eric C. Chi and Stefan Steinerberger SIAM Journal on Mathematics of Data Science, Volume 1, Issue 3, Page 383-407, January 2019. Hierarchical clustering is a fundamental unsupervised learning task, whose aim is to organize a...

By Baichuan Yuan, Hao Li, Andrea L. Bertozzi, P. Jeffrey Brantingham, and Mason A. Porter SIAM Journal on Mathematics of Data Science, Volume 1, Issue 2, Page 356-382, January 2019. There is often latent network structure in spatial and temporal data, and the tools...

By Nate Veldt, David F. Gleich, Anthony Wirth, and James Saunderson SIAM Journal on Mathematics of Data Science, Volume 1, Issue 2, Page 333-355, January 2019. We outline a new approach for solving linear programming relaxations of NP-hard graph clustering...

By George C. Linderman and Stefan Steinerberger SIAM Journal on Mathematics of Data Science, Volume 1, Issue 2, Page 313-332, January 2019. t-distributed stochastic neighborhood embedding (t-SNE), a clustering and visualization method proposed by van der...

By Austin R. Benson SIAM Journal on Mathematics of Data Science, Volume 1, Issue 2, Page 293-312, January 2019. Eigenvector centrality is a standard network analysis tool for determining the importance of (or...

By Dan Wilson and Bard Ermentrout SIAM Review, Volume 61, Issue 2, Page 277-315, January 2019. While phase reduction is a tremendously useful tool for understanding the dynamics of weakly perturbed limit cycle oscillators,...

By Bernhard Beckermann and Alex Townsend SIAM Review, Volume 61, Issue 2, Page 319-344, January 2019. Matrices with displacement structure, such as Pick, Vandermonde, and Hankel matrices, appear in a diverse range of applications. In...

By Samuel Awoniyi and Ira Wheaton SIAM Review, Volume 61, Issue 2, Page 347-360, January 2019. This education article presents a case for first courses on Markov chain modeling to include the topic “sojourn time...

By Guodong Shi, Claudio Altafini, and John S. Baras SIAM Review, Volume 61, Issue 2, Page 229-257, January 2019. A signed network is a network in which each link is associated with a positive or negative sign....

By Richard D. Neidinger SIAM Review, Volume 61, Issue 2, Page 361-381, January 2019. Techniques of univariate Newton interpolating polynomials are extended to multivariate data points by different generalizations and practical algorithms. ...

By Beate Ehrhardt and Patrick J. Wolfe SIAM Review, Volume 61, Issue 2, Page 261-276, January 2019. We characterize the large-sample properties of network modularity in the presence of covariates, under a natural and flexible null...

By Misha E. Kilmer SIAM Review, Volume 61, Issue 2, Page 259-259, January 2019. The paper “Network Modularity in the Presence of Covariates," by Beate Ehrhardt and Patrick J. Wolfe, is the first...

By Darinka Dentcheva SIAM Review, Volume 61, Issue 2, Page 345-346, January 2019. This issue of SIAM Review contains two papers in the Education section. The first paper is “Case for First...

By J. M. Sanz-Serna SIAM Review, Volume 61, Issue 2, Page 227-227, January 2019. ``Dynamics over Signed Networks,” by Guodong Shi, Claudio Altafini, and John S. Baras, is the Survey and Review article...

By Volker H. Schulz SIAM Review, Volume 61, Issue 2, Page 385-400, January 2019. Sometimes, people confuse artificial neural networks (ANNs) with real biological brains, at least metaphorically. The section starts with a...

By The Editors SIAM Review, Volume 61, Issue 2, Page 317-317, January 2019. The SIGEST article in this issue is “Bounds on the Singular Values of Matrices with Displacement Structure,” by Bernhard...

By Francesco Tudisco and Desmond J. Higham SIAM Journal on Mathematics of Data Science, Volume 1, Issue 2, Page 269-292, January 2019. We derive and analyze a new iterative algorithm for detecting network core--periphery structure. Using...

By Ahmed El Alaoui, Aaditya Ramdas, Florent Krzakala, Lenka Zdeborová, and Michael I. Jordan SIAM Journal on Mathematics of Data Science, Volume 1, Issue 1, Page 161-188, January 2019. Consider a population consisting of $n$ individuals, each of whom has one of $d$...

By Tingran Gao, Shahar Z. Kovalsky, Doug M. Boyer, and Ingrid Daubechies SIAM Journal on Mathematics of Data Science, Volume 1, Issue 1, Page 237-267, January 2019. We demonstrate applications of the Gaussian process-based landmarking algorithm proposed in [T. Gao, S....

By Tingran Gao, Shahar Z. Kovalsky, and Ingrid Daubechies SIAM Journal on Mathematics of Data Science, Volume 1, Issue 1, Page 208-236, January 2019. As a means of improving analysis of biological shapes, we propose an algorithm for...

By Aviad Aberdam, Jeremias Sulam, and Michael Elad SIAM Journal on Mathematics of Data Science, Volume 1, Issue 1, Page 46-77, January 2019. The recently proposed multi-layer sparse model has raised insightful connections between sparse representations and...

By Hadrien Montanelli and Qiang Du SIAM Journal on Mathematics of Data Science, Volume 1, Issue 1, Page 78-92, January 2019. We prove a theorem concerning the approximation of multivariate functions by deep ReLU networks....

By Helmut Bölcskei, Philipp Grohs, Gitta Kutyniok, and Philipp Petersen SIAM Journal on Mathematics of Data Science, Volume 1, Issue 1, Page 8-45, January 2019. We derive fundamental lower bounds on the connectivity and the memory requirements of deep...

By Benjamin Arras, Markus Bachmayr, and Albert Cohen SIAM Journal on Mathematics of Data Science, Volume 1, Issue 1, Page 189-207, January 2019. We consider the problem of approximating an unknown function $u\in L^2(D,\rho)$ from its evaluations...

By Paul Anderson, Timothy Chartier, and Amy Langville SIAM Journal on Mathematics of Data Science, Volume 1, Issue 1, Page 121-143, January 2019. This paper poses and solves a new problem, the rankability problem, which refers to...

By Nicolas Garcia Trillos SIAM Journal on Mathematics of Data Science, Volume 1, Issue 1, Page 93-120, January 2019. This paper studies the large sample asymptotics of data analysis procedures based on the...

By Madeleine Udell and Alex Townsend SIAM Journal on Mathematics of Data Science, Volume 1, Issue 1, Page 144-160, January 2019. Matrices of (approximate) low rank are pervasive in data science, appearing in movie preferences,...

By Tamara G. Kolda SIAM Journal on Mathematics of Data Science, Volume 1, Issue 1, Page 1-7, January 2019. The ascent of data science is a boon for mathematics. The SIAM Journal on...

By Martin J. Gander and Hui Zhang SIAM Review, Volume 61, Issue 1, Page 3-76, January 2019. Solving time-harmonic wave propagation problems by iterative methods is a difficult task, and over the last two decades an...

By Eddie Nijholt, Bob Rink, and Jan Sanders SIAM Review, Volume 61, Issue 1, Page 121-155, January 2019. Many systems in science and technology are networks: they consist of nodes with connections between them. Examples include electronic...

By Gjerrit Meinsma SIAM Review, Volume 61, Issue 1, Page 159-184, January 2019. A complete theory of dimensional and scaling analysis is presented and its power is demonstrated through a series of...

By Simone Carlo Surace, Anna Kutschireiter, and Jean-Pascal Pfister SIAM Review, Volume 61, Issue 1, Page 79-91, January 2019. Particle filters are a popular and flexible class of numerical algorithms to solve a large class of nonlinear filtering...

By Peter G. Fennell and James P. Gleeson SIAM Review, Volume 61, Issue 1, Page 92-118, January 2019. Multistate dynamical processes on networks, where nodes can occupy one of a multitude of discrete states, are gaining widespread...

By Silviu Filip, Aurya Javeed, and Lloyd N. Trefethen SIAM Review, Volume 61, Issue 1, Page 185-205, January 2019. The usual way in which mathematicians work with randomness is by a rigorous formulation of the idea of Brownian...

By Darinka Dentcheva SIAM Review, Volume 61, Issue 1, Page 157-158, January 2019. In this issue, the Education section of SIAM Review presents two papers. The first is “Dimensional and Scaling Analysis,”...

By Misha E. Kilmer SIAM Review, Volume 61, Issue 1, Page 77-77, January 2019. Short and to the point, “How to Avoid the Curse of Dimensionality: Scalability of Particle Filters with and...

By Volker H. Schulz SIAM Review, Volume 61, Issue 1, Page 209-225, January 2019. This time, the Book Reviews section has a large portion of reviews on books devoted to applications---in particular medical...

By J. M. Sanz-Serna SIAM Review, Volume 61, Issue 1, Page 1-1, January 2019. The Survey and Review article in this issue is “A Class of Iterative Solvers for the Helmholtz Equation: Factorizations,...

By The Editors SIAM Review, Volume 61, Issue 1, Page 119-119, January 2019. Over the last fifteen years, SIAM Review has published a range of articles in the general area of network...

Data science is everywhere, but how does it actually work ? SIAM’s newest journal, SIAM Journal on Mathematics of Data Science (SIMODS), aims to understand the deep inner workings of machine ...

Sponsored by the SIAM Activity Group on Data Mining and Analytics .
This conference is held in cooperation with the American Statistical Association .
This meeting is co-located with ...

SIAM is proud to introduce the first Conference on Mathematics of Data Science (MDS). The meeting, which will be held May 5-7, 2020, in Cincinnati, Ohio, intends to provide a forum for attendees ...

By Michael W. Mahoney
The Transdisciplinary Research in Principles of Data Science (TRIPODS) program is an effort funded by the National Science Foundation (NSF) to establish the foundations ...

By Lina Sorg
BIGMATH is an EU-funded Ph.D. program for early-career mathematicians with the theoretical and practical skills necessary to address the computational challenges that accompany ...

By Lina Sorg
As the capital city of Columbia, Bogotá has made major strides to decrease its crime rate and improve its reputation in recent years. Yet as a large metropolis and the most ...

The Society for Industrial and Applied Mathematics is proud to introduce the First Conference on Mathematics of Data Science. This conference will provide a forum to present work that advances ...

This joint prize was established in 2002 to honor Sonia Kovalevsky and her work on the theory of differential equations. It is awarded to anyone in the scientific or engineering community whose work highlights the achievements of women in applied and computational mathematics. Nominations can be submitted via the AWM website.

The prize was established in 1986 in memory of Richard C. DiPrima, who served SIAM for many years and in 1979–1980 as SIAM President. It aims to recognize an early career researcher in applied mathematics and is based on the doctoral dissertation.

The SIAM Student Paper Prize is awarded annually to the student author(s) of the most outstanding paper(s) accepted by SIAM journals within the three years preceding the nomination deadline. Starting with the 2018 award, the focus of the prize is to recognize outstanding scholarship by students in SIAM journals.

The Pioneer Prize is awarded every four years at the International Council for Industrial and Applied Mathematics (ICIAM) Congress to one individual for pioneering work introducing applied mathematical methods and scientific computing techniques to an industrial problem area or a new scientific field of applications. Nominations can be submitted via the ICIAM website.

This prize was created in 2013 to emphasize George Pólya’s legacy of communicating mathematics effectively. It joins two long-standing Pólya prizes SIAM has awarded in combinatorics and other fields beginning in 1969.

Through the generosity and inspiration of Gerald and Judith Porter, the Mathematical Association of America (MAA), American Mathematical Society (AMS), and SIAM offer this annual lecture at the Joint Mathematics Meetings. The lecture, first awarded in 2010, is given on a mathematical topic accessible to the broader community.

Named in honor of I. E. Block, a co-founder and the first managing director of SIAM, this lecture is open to the public at the SIAM Annual Meeting. It is intended to encourage public appreciation of applied mathematics and computational science by reaching out to the local community.

Established in 1959, the prize honors John von Neumann, a founder of modern computing. The prize is awarded annually for distinguished contributions to applied mathematics and for the effective communication of these ideas to the community.

The JPBM Communications Award is given annually to reward and encourage communicators who, on a sustained basis, bring mathematical ideas and information to non-mathematical audiences. The prize may be awarded in two categories: For Public Outreach and For Expository and Popular Books. Nominations can be submitted via the AMS website.

The prize recognizes students for outstanding solutions to real world math problems. It is awarded to two of the teams judged "Outstanding" in the Mathematical Contest in Modeling (MCM) administered annually by the Consortium for Mathematics and Its Applications (COMAP). Registration is accepted via the COMAP website.

The SIAM Outstanding Paper Prizes bring attention to papers published in SIAM journals. Three awards are made each year to the authors of papers deemed by SIAM journal editors-in-chief worthy of particular attention.

The prize, established in 1985 and originally intended to be awarded periodically, is now awarded annually for contributions to the advancement of applied mathematics on the national or international level.

The purpose of this activity group is to advance the mathematics of data mining, to highlight the importance and benefits of the application of data mining, and to identify and explore the connections between data mining and other applied sciences.

SIAM Journal on Mathematics of Data Science (SIMODS) publishes work that advances mathematical, statistical, and computational methods in the context of data and information sciences. We invite papers that present significant advances in this context, including applications to science, engineering, business, and medicine.

Multiscale Modeling & Simulation
SIAM J. on Applied Algebra and Geometry
SIAM J. on Applied Dynamical Systems
SIAM J. on Applied Mathematics
SIAM J. on Computing
SIAM J. on Control and Optimization
SIAM J. on Discrete Mathematics
SIAM J. on Financial Mathematics
SIAM J. on Imaging Sciences
SIAM J. on Mathematical Analysis
SIAM J. on Matrix Analysis and Applications
SIAM J. on Numerical Analysis
SIAM J. on Optimization
SIAM J. on Scientific Computing
SIAM/ASA J. on Uncertainty Quantification
SIAM Review
Theory of Probability & Its Applications

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Fellowships

Looking for financial support to further your research? Fellowships often provide funding plus experiential learning opportunities to young researchers. Learn more about fellowship opportunities.

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Our community is founded on igniting groundbreaking developments in applied math and computational science. Take a deeper dive into your area of study with one of these opportunities.