UPCOMING Up Next: Math + Neuro Seminar
This Week at ICERM
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 theoryfriendly 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 nearlinear 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
December 3, 2023
There are no events currently scheduled for December 3rd.
December 4, 2023

3:00  3:30 pm ESTCoffee Break11th Floor Collaborative Space

3:30  5:00 pm ESTOpen Problem SessionProblem Session  10th Floor Classroom
December 5, 2023

1:30  3:00 pm ESTJournal ClubJournal Club  10th Floor Classroom

3:00  3:30 pm ESTCoffee Break11th Floor Collaborative Space
December 6, 2023

10:00  11:30 am ESTOpen Problem SessionProblem Session  10th Floor Classroom

3:00  3:30 pm ESTCoffee Break11th Floor Collaborative Space

3:30  4:30 pm ESTMath + Neuro Seminar11th Floor Lecture Hall
 Session Chair
 Peter Thomas, Case Western Reserve University
December 7, 2023

9:00  10:30 am ESTTDA TutorialTutorial  10th Floor Classroom

12:00  1:30 pm ESTOpen Problems LunchWorking Lunch  10th Floor Collaborative Space

3:00  3:30 pm ESTCoffee Break11th Floor Collaborative Space
December 8, 2023

9:30  10:30 am EST"Something Cool I Now Know" Seminar10th Floor Classroom

11:00 am  12:00 pm ESTTBDPost Doc/Graduate Student Seminar  10th Floor Classroom

1:30  3:00 pm ESTTopology+Neuro Working GroupGroup Work  10th Floor Classroom

3:00  3:30 pm ESTCoffee Break11th Floor Collaborative Space
December 9, 2023
There are no events currently scheduled for December 9th.
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 theoryfriendly 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 nearlinear 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
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 theoryfriendly 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 nearlinear 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
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 theoryfriendly 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 nearlinear 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
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 theoryfriendly 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 nearlinear 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
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Upcoming Programs
Computational Tools for SingleCell Omics
Dec 11  15, 2023
Singlecell assays provide a tool for investigating cellular heterogeneity and have led to new insights into a variety of biological processes that were not accessible with bulk sequencing technologies. Assays generate observations of many different molecular types and a grand mathematical challenge is to devise meaningful strategies to integrate data gathered across a variety of different sequencing modalities. The firstorder approach to do this is to analyze the projected data by clustering. Keeping more refined shape information about the data enables more meaningful and accurate analysis. Geometric methods include (i) Manifold learning: Whereas classical approaches (PCA, metric MDS) assume projection to a lowdimensional Euclidean subspace, manifold learning finds coordinates that lie on a not necessarily flat or contractible manifold. (ii) Topological data analysis: Algebraic topology provides qualitative descriptors of global shape. Integrating these descriptors across feature... (more)
Organizing Committee
 Elham Azizi
 Andrew Blumberg
 Lorin Crawford
 Bianca Dumitrascu
 Antonio Moretti
 Itsik Pe'er
Connecting HigherOrder Statistics and Symmetric Tensors
Jan 8  12, 2024
This workshop focuses on connections between higherorder statistics and symmetric tensors, and their applications to machine learning, network science, and other domains. Higherorder 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.
Higherorder statistics are needed to characterize complex data distributions, such as mixture models. Symmetric tensors, meanwhile, are multidimensional arrays. They generalize covariance matrices and affinity matrices and can be used to represent higherorder correlations. Tensor decompositions extend matrix factorizations from numerical linear algebra to multilinear algebra. Recently tensorbased approaches have 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 Pereira
Numerical PDEs: Analysis, Algorithms, and Data Challenges
Jan 29  May 3, 2024
This Semester Program will bring together both leading experts and junior researchers to discuss the current stateoftheart and emerging trends in computational PDEs. While there are scores of numerical methodologies designed for a wide variety of PDEs, the program will be designed around three workshops each centered around a specific theme: PDEs and Geometry, Nonlocal PDEs, and Numerical Analysis of Multiphysics problems. This grouping of topics embodies a broad representation of computational mathematics with each set possessing its own skill set of mathematical tools and viewpoints. Nonetheless, all workshops will have the common theme of using rigorous mathematical theory to develop and analyze the convergence and efficiency of numerical methods. The diversity of the workshop topics will bring together historically distinct groups of mathematicians to interact and facilitate new ideas and breakthroughs.
Organizing Committee
 Marta D'Elia
 Johnny Guzman
 Brittany Hamfeldt
 Michael Neilan
 Maxim Olshanskiy
 Sara Pollock
 Abner Salgado
 Valeria Simoncini
Numerical Analysis of Multiphysics Problems
Feb 12  16, 2024
It is practically rare that a natural phenomenon or engineering problem can be accurately described by a single law of physics. The striking diversity of rules of life forces scientists to continuously increase the complexity of models to address the evergrowing requirements for their prediction capabilities. It remains a formidable challenge to derive and analyze numerical methods which are universal enough to handle complex multiphysics problems with the same ease and efficiency as traditional methods do for textbook PDEs.
The workshop will focus on recent trends in the field of numerical methods for multiphysics problems that include the development of monolithic approaches, structure preserving discretizations, geometrically unfitted methods, datadriven techniques, and modern algebraic methods for the resulting linear and nonlinear discrete systems. The topics of interest include models and discretizations for fluid  elastic structure interaction, nonNewtonian fluids, phase... (more)
Organizing Committee
 Martina Bukač
 John Evans
 Hyesuk Lee
 Amnon Meir
 Maxim Olshanskiy
 Sara Pollock
 Valeria Simoncini
PDEs and Geometry: Numerical Aspects
Mar 11  15, 2024
The development and analysis of numerical methods for PDEs whose formulation or interpretation is derived from an underlying geometry is a persistent challenge in numerical analysis. Examples include PDEs posed on complicated manifolds or graphs, PDEs that describe interactions across complex interfaces, and equations derived from intrinsically geometric concepts such as curvaturedriven flows or highly nonlinear MongeAmpere equations arising in optimal transport. In recent years, these PDEs have gained significance in diverse areas such as machine learning, optical design problems, meteorology, medical imaging, and beyond. Hence, the development of numerical methods for this class of PDEs is poised to lead to breakthroughs for a wide range of timely problems. However, designing methods to accurately and efficiently solve these PDEs requires careful consideration of the interactions between discretization methods, the PDE operators, and the underlying geometric properties.
This... (more)
Organizing Committee
 Charlie Elliott
 Brittany Hamfeldt
 Michael Neilan
 Maxim Olshanskiy
 Axel Voigt
Nonlocality: Challenges in Modeling and Simulation
Apr 15  19, 2024
This workshop focuses on the modeling, analysis, approximation, and applications of nonlocal equations, which have raised new challenges to mathematical modeling, numerical analysis, and their computational implementation. Recent applications include, but are not limited to: heat and mass diffusion, mechanics, pattern formation, image processing, selforganized dynamics, and population dispersal.
Invited speakers and participants will bring expertise from a wide range of related fields, including mathematical and numerical analysis of nonlocal and fractional equations, numerical methods and discretization schemes, multiscale modeling, adaptivity, machine learning, software implementation, peridynamics modeling of material failure and damage, nonlocal and fractional modeling of anomalous heat and mass diffusion, and several engineering and scientific applications in which nonlocal modeling is useful.
Organizing Committee
 Marta D'Elia
 Abner Salgado
 Pablo Seleson
 Xiaochuan Tian
Connect with ICERM
ICERM Fall 2023 Newsletter
Fall 2023 Newsletter  In This Issue:
 Updates from Director Brendan Hassett
 A Creative Approach to Math + Neuroscience
 Call for Proposals
 Upcoming Programs
 Upcoming Opportunities
 Introducing ICERM's Fall 2023 Postdoctoral Fellows
 Nathaniel Whitaker Returned to ICERM for "Unscripted," a Public Lecture
 Probability Puzzle
 Iris Horng: Building Networks at ICERM
ICERM Summer 2023 Newsletter
Summer 2023 Newsletter  In This Issue:
 Updates from Director Brendan Hassett
 Propose a Program
 Collaborating at ICERM: Order Preserving Braids and Vacillating Tableaux for Integer Sequences
 Mathematical Databases and AI: How a UConn Undergraduate Research Project Led to a Major Breakthrough
 Upcoming Opportunities
 ICERM Summers Support Undergraduate Students and Faculty
 Data Science and Social Justice Group Wants to Fight Genocide Using Algorithms
 Anderson's Method Accelerates at ICERM
 GirlsGetMath@ICERM 2023
 Make a Gift to ICERM
ICERM Spring 2023 Newsletter
Spring 2023 Newsletter  in this issue:
 Updates from the Director
 Introducing ICERM's Spring 2023 Postdoctoral Fellows!
 Spring 2023 at ICERM
 Donate to ICERM
 A Unified Perspective on Set Dualities
 Announcing PRIMES
 Upcoming Workshops