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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.
By Arda Antikacioglu, Tanvi Bajpai, and R. RaviSIAM Journal on Mathematics of Data Science, Volume 1, Issue 4, Page 759-779, January 2019. Recommender systems often operate on item catalogs clustered by genres and user bases that...
By Leslie Greengard and Shidong JiangSIAM Review, Volume 61, Issue 4, Page 733-755, January 2019. We present a new integral representation for the unsteady, incompressible Stokes or Navier--Stokes equations, based on a linear combination...
By Jon Cockayne, Chris J. Oates, T. J. Sullivan, and Mark GirolamiSIAM Review, Volume 61, Issue 4, Page 756-789, January 2019. Over forty years ago average-case error was proposed in the applied mathematics literature as an alternative criterion with which...
By Gábor PatakiSIAM Review, Volume 61, Issue 4, Page 839-859, January 2019. Semidefinite programs (SDPs)---some of the most useful and versatile optimization problems of the last few decades---are often...
By Catherine F. Higham and Desmond J. HighamSIAM Review, Volume 61, Issue 4, Page 860-891, January 2019. Multilayered artificial neural networks are becoming a pervasive tool in a host of application fields. At the heart of...
By Guo Luo and Thomas Y. HouSIAM Review, Volume 61, Issue 4, Page 793-835, January 2019. Whether the three-dimensional incompressible Euler equations can develop a singularity in finite time from smooth initial data is one...
By Olga Y. KushelSIAM Review, Volume 61, Issue 4, Page 643-729, January 2019. Multiplicative and additive $D$-stability, diagonal stability, Schur $D$-stability, and $H$-stability are classical concepts which arise in studying linear dynamical...
By Darinka DentchevaSIAM Review, Volume 61, Issue 4, Page 837-838, January 2019. The Education section of SIAM Review presents two papers in this issue. In the first paper Gábor Pataki ...
By Misha E. KilmerSIAM Review, Volume 61, Issue 4, Page 731-731, January 2019. Numerical solution of the Navier--Stokes equations in both two and three dimensions is the subject of the first of...
By Volker H. SchulzSIAM Review, Volume 61, Issue 4, Page 895-906, January 2019. The book reviews section serves to inform readers about current important books from a personal point of view. However,...
By The EditorsSIAM Review, Volume 61, Issue 4, Page 791-791, January 2019. The SIGEST article in this issue is “Formation of Finite-Time Singularities in the 3D Axisymmetric Euler Equations: A Numerics...
By J. M. Sanz-SernaSIAM Review, Volume 61, Issue 4, Page 641-641, January 2019. It is a safe bet that most readers use matrices in their work. It is often of interest to...
By Hans De Sterck , Gitta Kutyniok , James Nagy , and Eitan Tadmor The excitement and activity surrounding the field of machine learning was clearly evident at the 9th International ...
Have you ever tried to explain why applied math matters to your non-specialist friends and colleagues? How does it impact their everyday life? How is it shaping the future of technology? SIAM has ...
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By Pierre Baldi and Roman VershyninSIAM Journal on Mathematics of Data Science, Volume 1, Issue 4, Page 699-729, January 2019. A simple way to generate a Boolean function is to take the sign of...
By A. Roxana Pamfil, Sam D. Howison, Renaud Lambiotte, and Mason A. PorterSIAM Journal on Mathematics of Data Science, Volume 1, Issue 4, Page 667-698, January 2019. Characterizing large-scale organization in networks, including multilayer networks, is one of the most prominent...
By Nhat Ho and XuanLong NguyenSIAM Journal on Mathematics of Data Science, Volume 1, Issue 4, Page 730-758, January 2019. Singularities of a statistical model are the elements of the model's parameter space which...
By Reginald E. Bryant and Sekou L. Remy The 2019 SIAM International Conference on Data Mining (SDM19), which took place earlier this year in Calgary, Canada, introduced a ...
By Michael W. Mahoney Part I of this article , which published in the September issue of SIAM News, described the National Science Foundation’s (NSF) Transdisciplinary Research in ...
By Sinan G. Aksoy, Kathleen Nowak, and Stephen J. YoungSIAM Journal on Mathematics of Data Science, Volume 1, Issue 4, Page 647-666, January 2019. Graph similarity metrics serve far-ranging purposes across many domains in data science. As graph...
By Paul Anderson, Timothy Chartier, and Amy LangvilleSIAM Journal on Mathematics of Data Science, Volume 1, Issue 3, Page 643-646, January 2019. This erratum contains corrected figures to replace Figures 5, 7, and 9 in [P....
By Jaeho Lee and Maxim RaginskySIAM Journal on Mathematics of Data Science, Volume 1, Issue 3, Page 617-642, January 2019. This paper generalizes the Maurer--Pontil framework of finite-dimensional lossy coding schemes to the setting...
By Tal Shnitzer, Mirela Ben-Chen, Leonidas Guibas, Ronen Talmon, and Hau-Tieng WuSIAM Journal on Mathematics of Data Science, Volume 1, Issue 3, Page 588-616, January 2019. Finding appropriate low-dimensional representations of high-dimensional multimodal data can be challenging, since each modality...
By Christopher Aicher, Yi-An Ma, Nicholas J. Foti, and Emily B. FoxSIAM Journal on Mathematics of Data Science, Volume 1, Issue 3, Page 555-587, January 2019. State space models (SSMs) are a flexible approach to modeling complex time series. However,...
By Jared Tanner, Andrew Thompson, and Simon VarySIAM Journal on Mathematics of Data Science, Volume 1, Issue 3, Page 537-554, January 2019. Robust principal component analysis (RPCA) [J. Candès et al., J. ACM, 58 (2011), pp....
By Jeremy E. Cohen and Nicolas GillisSIAM 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 SingerSIAM 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 DaubechiesSIAM 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 LandsbergSIAM 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 TeboulleSIAM 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 YangSIAM 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. GleichSIAM 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 ZwartSIAM 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 HuybrechsSIAM 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. TeckentrupSIAM 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 NorburySIAM 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 EditorsSIAM 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. SchulzSIAM 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. KilmerSIAM 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-SernaSIAM 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 DentchevaSIAM 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 SteinerbergerSIAM 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. PorterSIAM 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 SaundersonSIAM 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 SteinerbergerSIAM 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. BensonSIAM 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 ErmentroutSIAM 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 TownsendSIAM 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 WheatonSIAM 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. BarasSIAM 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. NeidingerSIAM 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. WolfeSIAM 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. KilmerSIAM 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 DentchevaSIAM 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-SernaSIAM 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. SchulzSIAM 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 EditorsSIAM 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. HighamSIAM 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. JordanSIAM 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 DaubechiesSIAM 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 DaubechiesSIAM 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 EladSIAM 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 DuSIAM 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 PetersenSIAM 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 CohenSIAM 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 LangvilleSIAM 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 TrillosSIAM 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 TownsendSIAM 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. KoldaSIAM 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 ZhangSIAM 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 SandersSIAM 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 MeinsmaSIAM 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 PfisterSIAM 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. GleesonSIAM 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. TrefethenSIAM 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 DentchevaSIAM 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. KilmerSIAM 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. SchulzSIAM 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-SernaSIAM 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 EditorsSIAM 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...
SIAM is pleased to announce the Program Committee and Senior Program Committee for the SIAM International Conference on Data Mining (SDM20). As part of the submission procedure , SDM20 authors ...
SIAM is proud to introduce its first ever Conference on Mathematics of Data Science (MDS20). This conference will provide a forum to present work that advances mathematical, statistical, and ...
Data science is everywhere, but how does it actually work ? SIAM’s newest journal, the SIAM Journal on Mathematics of Data Science ( SIMODS ), aims to understand the deep inner workings of ...
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 ...
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 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.
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.
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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 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.
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.
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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Internships allow you to network and forge connections for future job possibilities, while also exploring possible areas of interest. Look at this list of companies who offer valuable opportunities.
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.