## Programs & Events

##### Tangled in Knot Theory

May 22 - 25, 2023

In spite of their omnipresence and importance, a number of questions about knots remain elusive. Addressing them solicits techniques from a range of mathematical disciplines at the interface of algebra, analysis, geometry, modeling, and low-dimensional topology. Some of the most exciting recent avenues of research include optimizing geometry, quantum knot invariants, and applications in material sciences, physics, and molecular biology.

This workshop emphasizes bridging the gap between theoretical, computational, and experimental approaches in knot theory and its applications, including artificial intelligence.

##### Organizing Committee

- Simon Blatt
- Eleni Panagiotou
- Philipp Reiter
- Radmila Sazdanovic
- Armin Schikorra

##### Mathematical and Scientific Machine Learning

Jun 5 - 9, 2023

MSML2023 is the third edition of a newly established conference, with emphasis on promoting the study of mathematical theory and algorithms of machine learning, as well as applications of machine learning in scientific computing and engineering disciplines. This conference aims to bring together the communities of machine learning, applied mathematics, and computational science and engineering, to exchange ideas and progress in the fast-growing field of scientific machine learning (SciML). The objective of this annual conference series is to promote the study of:

- Theory and algorithms of machine learning.
- Applications in scientific and engineering disciplines such as physics, chemistry, material sciences, fluid and solid mechanics, etc.
- To provide hands-on tutorials for students and new researchers in the field.

##### Organizing Committee

- Marta D'Elia
- George Karniadakis
- Siddhartha Mishra
- Themistoklis Sapsis
- Jinchao Xu
- Zhongqiang Zhang

##### Summer@ICERM 2023: Mathematical Modeling of DNA Self-Assembly

Jun 12 - Aug 4, 2023

The Summer@ICERM faculty advisers will present a variety of research projects on the combinatorial and graph theoretical properties of DNA self-assembly. By modeling nanostructures with discrete graphs, efficient DNA self-assembly becomes a mathematical puzzle. Faculty will also guide the development of computational tools which can be used to aid in answering fundamental questions that arise in this field.

Throughout the eight-week program, students will be introduced to the research topic with interactive lectures. Afterward, students will work on their projects in assigned groups of two to four, supervised by faculty advisors and aided by teaching assistants. Students will meet daily, give regular talks about their findings, attend mini-courses, guest talks, and professional development seminars, practice coding, and Tex typesetting, and will acquire skills in free software development. Students will learn how to collaborate mathematically, working closely in their teams to write... (more)

##### Organizing Committee

- Leyda Almodóvar Velázquez
- Amanda Harsy
- Cory Johnson
- Jessica Sorrells

##### Mathematical and Computational Biology

Jun 12 - 16, 2023

The field of mathematical and computational biology is rapidly growing. The most applicable computational models have been developed in collaboration between computational and life science researchers. This workshop aims to bring these groups together to facilitate and promote collaborations among them.

A mathematical model for one disease might also be useful in modeling another disease. Some researchers are working on theoretical mathematical & statistical problems related to biological and biomedical applications, while others are developing computational methodologies to address fundamental life science knowledge gaps.

This workshop fosters and features collaborations among these groups along with experimentalists and physicians. Theoreticians will be exposed to a variety of open biological questions in need of state-of-the-art and efficient mathematical methods. Computational scientists will learn about more robust and efficient methods that could be tailored to answer... (more)

##### Organizing Committee

- Wenrui Hao
- Panayotis Kevrekidis
- Natalia Komarova
- Marieke Kuijjer
- Olivia Prosper
- Leili Shahriyari
- Nathaniel Whitaker

##### Data Science and Social Justice: Networks, Policy, and Education (Part II)

Jun 20 - Jul 28, 2023

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... (more)

##### Organizing Committee

- Carrie Diaz Eaton
- Joseph Hibdon
- Drew Lewis
- Jessica Libertini
- Omayra Ortega
- Victor Piercey
- Bjorn Sandstede
- Talitha Washington
- Tian An Wong
- Heather Zinn Brooks

##### 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. The suggested length for papers is no more than 20-25 pages, excluding references. However, longer... (more)

##### Organizing Committee

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

##### 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

##### 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