## Programs & Events

##### From Impact Factor to Influence Factor: Data Science and Policy for Social Justice

Jun 20 - 23, 2023

How can quantitative science inform policy decisions? This workshop is designed to help mathematicians and data scientists leverage their expertise to contribute positively to social justice efforts ranging from small town issues to global concerns. It is intended for those interested in data science, mathematical modeling, and policy interventions.

The main technical skill that the workshop will focus on is data storytelling, specifically with an eye towards policy. Data storytelling includes a mix of rhetorical and technical skills. On the technical side, these skills include data visualization, conclusion description, crafting human stories from data (example: the â€œmiddle thirdâ€ technique), all done effectively and ethically, with an eye toward the audience and purpose of the communication. Participants will be exposed to other technical skills through four projects, each of which will have its own project leadership team. In advance of the workshop, participants will be asked... (more)

##### Organizing Committee

- Jessica Libertini
- Victor Piercey
- Tian An Wong

##### Modern Applied and Computational Analysis

Jun 26 - 30, 2023

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 â€œChallenges in Geometry, Analysis, and Computation: High-Dimensional Synthesisâ€ workshop at Yale, the â€œModern Applied and Computational Analysisâ€ 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.

The organizers would like to thank James Bremer, Ronald Coifman, Jingfang Huang, Peter Jones, Mauro Maggioni, Yair Minsky, Vladimir Rokhlin, Wilhelm Schlag, John Schotland, Amit Singer, Stefan Steinerberger, and Mark... (more)

##### Organizing Committee

- Anna Gilbert
- Roy Lederman
- Gilad Lerman
- Per-Gunnar Martinsson
- Andrea Nahmod
- Kirill Serkh
- Christoph Thiele
- Sijue Wu

##### LMFDB, Computation, and Number Theory (LuCaNT)

Jul 10 - 14, 2023

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. We plan to publish a proceedings volume that will include all of the accepted papers.

The organizers of the first conference on LMFDB, Computation, and Number Theory (LuCaNT) are excited to issue a call for papers for an associated proceedings volume to be published in an open access volume of Contemporary Mathematics. We strongly encourage anyone with research related to mathematical databases or computation to submit a paper here. The suggested length... (more)

##### Organizing Committee

- John Cremona
- John Jones
- Jennifer Paulhus
- Andrew Sutherland
- John Voight

##### Educating at the Intersection of Data Science and Social Justice

Jul 17 - 21, 2023

This workshop is designed to highlight the intersections between data science and social justice in K-16 education. The goals of this workshop are to 1) explore, and identify avenues to expand, current research on and methods for using data science in education; 2) raise awareness about research on issues of social justice in education and teaching pedagogy; and 3) work with community partners to create, evaluate and disseminate new K-16 curricular materials.

The week will focus on integrating computational social justice research methods, open problems, and best practices across different levels of training. Participants will collaborate on curating professional artifacts, such as: articulating collections of open problems in data science research in social justice; compiling a collection of computational social justice research projects; and developing course modules suitable for use in K-16 education. The results of these collaborations will be shared publicly by the organizers and... (more)

##### Organizing Committee

- Ron Buckmire
- Drew Lewis
- Omayra Ortega
- Katharine Ott
- Matt Salomone

##### Acceleration and Extrapolation Methods

Jul 24 - 28, 2023

Solving systems of nonlinear equations and optimization problems are pervasive issues throughout the mathematical sciences with applications in many areas. Acceleration and extrapolation methods have emerged as a key technology to solve these problems efficiently and robustly. The simple underlying idea of these methods is to recombine previous approximations in a sequence to determine the next term or approximation.

This approach has been applied repeatedly and from different angles to numerous problems over the last several decades. Important methods including epsilon algorithms and Anderson acceleration were introduced throughout the early and mid-20th century, and are now common in many applied fields including optimization, machine learning, computational chemistry, materials, and climate sciences. Within the last decade, theoretical advances on convergence, acceleration mechanisms, and the development of unified frameworks to understand these methods have come to light, yet our... (more)

##### Organizing Committee

- Hans De Sterck
- David Gardner
- Agnieszka Miedlar
- Sara Pollock

##### Spring 2021 Reunion Event

Jul 31 - Aug 18, 2023

The aim of this reunion meeting is to bring together the participants from the spring 2021 program “Combinatorial Algebraic Geometry” bringing together experts in both pure and applied parts of mathematics as well mathematical programmers, all working at the confluence of discrete mathematics and algebraic geometry, with the aim of creating an environment conducive to interdisciplinary collaboration.

##### Organizing Committee

- Anders Buch
- Melody Chan
- June Huh
- Thomas Lam
- Leonardo Mihalcea
- Sam Payne
- Lauren Williams

##### GirlsGetMath@ICERM: Summer Math Camp for High School Students

Aug 14 - 18, 2023

GirlsGetMath@ICERM is a five-day non-residential mathematics program that is open to high schoolers, regardless of gender, who live in or near greater Rhode Island and who will be entering the 10th or 11th grade in the fall of 2023.

GirlsGetMath occurs in an encouraging environment that builds young students' confidence in math and science.

GirlsGetMath expands participants' understanding and knowledge of mathematics through computations and experimentations.

GirlsGetMath provides expert mathematical training and mentoring.

GirlsGetMath@ICERM encourages 20-25 high schoolers to explore topics such as cryptography, the mathematics of voting, image processing, prime numbers and factoring, and fractals.

The goals of the program are:

- to show young adults that the study of mathematics can be exciting,... (more)

##### Math + Neuroscience: Strengthening the Interplay Between Theory and Mathematics

Sep 6 - Dec 8, 2023

The goal of this semester program is to bring together a variety of mathematicians with researchers working in theoretical and computational neuroscience as well as some theory-friendly experimentalists. However, unlike programs in neuroscience that emphasize connections between theory and experiment, this program will focus on building bridges between theory and mathematics. This is motivated in part by the observation that theoretical developments in neuroscience are often limited not only by lack of data but also by the need to better develop the relevant mathematics. For example, theorists often rely on linear or near-linear modeling frameworks for neural networks simply because the mathematics of nonlinear network dynamics is still poorly understood. Conversely, just as in the history of physics, neuroscience problems give rise to new questions in mathematics. In recent years, these questions have touched on a rich variety of fields including geometry, topology, combinatorics,... (more)

##### Organizing Committee

- Carina Curto
- Brent Doiron
- Robert Ghrist
- Kathryn Hess
- Zachary Kilpatrick
- Matilde Marcolli
- Konstantin Mischaikow
- Katie Morrison
- Elad Schneidman
- Tatyana Sharpee

##### Mathematical Challenges in Neuronal Network Dynamics

Sep 18 - 22, 2023

One of the fundamental questions in neuroscience is to understand how network connectivity shapes neural activity. Over the last 10 years, tremendous progress has been made in collecting neural activity and connectivity data, but theoretical advances have lagged behind. This workshop will focus on identifying mathematical challenges that arise in studying the dynamics of learning, memory, plasticity, decision-making, sequence generation, and central pattern generator circuits. Mathematical ideas and approaches from dynamical systems, statistical mechanics, linear algebra, graph theory, topology, and traditional areas of applied mathematics are all expected to play an important role.

##### Organizing Committee

- Carina Curto
- Brent Doiron
- Zachary Kilpatrick
- Konstantin Mischaikow
- Katie Morrison

##### Topology and Geometry in Neuroscience

Oct 16 - 20, 2023

In the last decade or so, applied topology and algebraic geometry have come into their own as vibrant areas of applied mathematics. At the same time, ideas and tools from topology and geometry have infiltrated theoretical and computational neuroscience. This kind of mathematics has shown itself to be a natural and useful language not only for analyzing neural data sets, but also as a means of understanding principles of neural coding and computation. This workshop will bring together leading researchers at the interfaces of topology, geometry and neuroscience to take stock of recent work and outline future directions. This includes a focus on topological data analysis (persistent homology and related methods), topological analysis of neural networks and their dynamics, topological decoding of neural activity, evolving topology of dynamic networks (e.g., networks that are changing as a result of learning), and analysis of connectome data. Related topics may include the geometry and... (more)

##### Organizing Committee

- Carina Curto
- Robert Ghrist
- Kathryn Hess
- Matilde Marcolli
- Elad Schneidman
- Tatyana Sharpee

##### Neural Coding and Combinatorics

Oct 30 - Nov 3, 2023

Cracking the neural code is one of the longstanding questions in neuroscience. How does the activity of populations of neurons represent stimuli and perform neural computations? Decades of theoretical and experimental work have provided valuable clues about the principles of neural coding, as well as descriptive understandings of various neural codes. This raises a number of mathematical questions touching on algebra, combinatorics, probability, and geometry. This workshop will explore questions that arise from sensory perception and processing in olfactory, auditory, and visual coding, as well as properties of place field codes and grid cell codes, mechanisms for decoding population activity, and the role of noise and correlations. These questions may be tackled with techniques from information theory, mathematical coding theory, combinatorial commutative algebra, hyperplane arrangements, oriented matroids, convex geometry, statistical mechanics, and more.

##### Organizing Committee

- Zachary Kilpatrick
- Katie Morrison
- Elad Schneidman
- Tatyana Sharpee
- Nora Youngs

##### Connecting Higher-Order Statistics and Symmetric Tensors

Jan 8 - 12, 2024

This workshop focuses on connections between higher-order statistics and symmetric tensors, and their applications to machine learning, network science and other domains. Higher-order statistics refers to the study of correlations between three or more covariates. This is in contrast to the usual mean and covariance, which are based on one and two covariates.

Higher-order statistics are needed to characterize complex data distributions, such as mixture models. Symmetric tensors, meanwhile, are multi-dimensional arrays. They generalize covariance matrices and affinity matrices, and can be used to represent higher-order correlations. Tensor decompositions extend matrix factorizations from numerical linear algebra to multilinear algebra. Recently tensor-based approaches have been become more practical, due to the availability of bigger datasets and new algorithms.

The workshop brings together applied mathematicians, statisticians, probabilists, machine learning experts and computational... (more)

##### Organizing Committee

- Joe Kileel
- Tamara Kolda
- Joao Morais Carreira Pereira