Conferences  >  Mathematics  >  Numerical Analysis and Computational Mathematics  >  United States

ICERM / Brown University

The aim of this workshop is to discuss many exciting recent developments on the computational side of discrete optimization. The workshop has three main themes. The first theme is that of commercial and academic/open-source solvers that have allowed the solution of very large-scale problems, and of recent developments in exact solvers that have allowed for proofs of results in logic, knot theory, and combinatorics. The second theme is the interaction between optimization and machine learning: these two areas complement each other in several ways. The third theme is quantum computing and unconventional computing architectures: quantum computing has been used to tackle combinatorial optimization problems, and quantum algorithms exist for other related optimization problems such as linear and semidefinite relaxations.

ICERM

This workshop will focus on the intersection of mathematics, statistics, machine learning, and computation, when viewed through the lens of optimal transport (OT). Mathematical topics will include low-dimensional models for OT, linearizations of OT, and the geometry of OT including gradient flows and gradient descent in the space of measures. Relevant statistical topics will include reliable and efficient estimation of OT plans in high dimensions, the role of regularization in computing OT distances and plans, with applications to robust statistics, uncertainty quantification, and overparameterized machine learning. Computation will be a recurring theme of the workshop, with emphasis on the development of fast algorithms and applications to computational biology, high energy physics, material science, spatio-temporal modeling, natural language processing, and image processing.

Program Staff;     Phone: [1-401-863-5030];     Email: programstaff@icerm.brown.edu

Scientific Computing and Data Science,    Probability and Statistics, Game Theory

Society for Industrial and Applied Mathematics (SIAM)

Applied Mathematics (in general),    Algorithms and Data Structures

ICERM

In Summer 2023, ICERM hosts the second of two summer programs entitled The Social Justice and Data Science Summer Research Program. This program aims to increase interest, research training, and capacity for data science for social justice, and to develop both quantitative and qualitative approaches to those professional practices that call for community engagement, critical inquiry, and interdisciplinary cooperation. Building off of Summer 2022's program, which included a workshop on network science and analysis as well as foundational conversations with community partners, the Summer 2023 program will advance the mathematics community's understanding of the complexity of computational social justice work through three emphasis areas (1) policy, (2) education, and (3) community-driven research. As a new field emerges at the face of computational and applied mathematics and social justice, this requires new methods for working across community lines. In order to address the novel and interdisciplinary problems arising out of community needs, participants will work together to develop new or refine existing computational methods whose applications may be broader than the original problem. The organizers are committed to working with humility and in solidarity with one another and with the local community. The program will include engagement with the local community and invest in the education of the next generation of researchers by driving the development and direction of new computational methods for quantitative social justice research. Researchers with expertise and interests in using mathematical models and/or data science to examine social justice issues in policy and/or education are particularly encouraged to apply. The organizers also seek applications from researchers with specialties in digital humanities, computational social science, and data science education.

Program Staff;     Phone: [1-401-863-5030];     Email: programstaff@icerm.brown.edu

Scientific Computing and Data Science,    Applied Mathematics (in general)

ICERM

The mathematical and computational toolbox for modern experimental and engineering problems has become more diverse than ever before, with a flurry of new challenges in inverse problems and successful practical solutions that present further theoretical questions. In the spirit of the 2012 “Challenges in Geometry, Analysis, and Computation: High-Dimensional Synthesis” workshop at Yale, the “Modern Applied and Computational Analysis” workshop will be a celebration of different perspectives on inverse problems, models, inference, and harmonic analysis and a debate about the challenges and opportunities in the next decade of applied analysis. The topics include inverse problems, randomized linear algebra, machine learning in applied analysis, and tensor networks.

Program Staff;     Phone: [1-401-863-5030];     Email: info@icerm.brown.edu

ICERM

This will be a one-week conference broadly focused on the topics of the LMFDB, mathematical databases, computation, number theory, and arithmetic geometry. The conference will include invited talks, presentations by authors of papers submitted to the conference and selected by the scientific committee following peer-review, as well as time set aside for research and collaboration. This workshop is an activity of the Simons Collaboration 'Arithmetic Geometry, Number Theory, and Computation' and is supported by the Simons Foundation.

Program Staff;     Phone: [1-401-863-5030];     Email: info@icerm.brown.edu

Number Theory, Arithmetic,    Geometry and Topology

MIT, Cambridge, MA

This conference is in celebration of Alan Edelman's 60th birthday. Alan is well-known for his contributions across many fields in mathematics, computational science and computer science. In this conference, we are trying to cover the many areas of random matrix theory, numerical linear algebra, and scientific computing that have been enriched by Alan's contributions, which often bring into sharp focus how a nice computational "trick" often offers a nice theoretical insight, in reverse from the usual direction.