The Institute for Computational and Experimental Research in Mathematics (ICERM) is located at Brown University. ICERM's core funding comes from the U.S. National Science Foundation Division of Mathematical Sciences. | | Updates from ICERM's Director Brendan Hassett | | |
Greetings from ICERM!
This summer, hundreds of researchers, students, and teachers have come to ICERM to explore timely topics, from mathematical database development to modeling Arctic sea ice patterns. Almost every week since May, a new group of mathematical scientists has arrived here in Providence to advance research, education, and collaboration goals in their fields of study.
Each summer, ICERM sets out to create exciting opportunities for young mathematicians through education and outreach programs. At this year’s Research Experiences for Undergraduate Faculty, our annual joint program with the American Institute for Mathematics, researchers based at primarily undergraduate institutions gathered to discuss open problems and potential research topics for college-level mathematics students. During Summer@ICERM 2025: Building Useful Insights from Local Data, a select group of undergraduates in-residence at ICERM had an intensive introduction to the field of data science. By learning how to turn information into insights and conduct group research, our students left ICERM better prepared for their future data science endeavors, whether in academia or in industry. GoGetMath@ICERM, our annual summer intensive for high school students, introduced sixteen aspiring mathematical scientists to linear algebra, graph theory, cellular automata, and applications in public health and image processing.
Between these educational programs, we have been busy with a series of research-oriented workshops organized by leaders in mathematical biology, scientific machine learning, stiff differential equations, random polynomials, and mathematics-inspired art. Whether you are curious about mathematics, a student, or a seasoned researcher, we encourage you to check out the latest research talks to take place at ICERM in our video archive.
Finally, I am very pleased to announce ICERM has received a $16.5 million award from the National Science Foundation (NSF) to continue our mission of supporting innovative research at the nexus of mathematics and computation. ICERM's PIs (Brown faculty Javier Gómez-Serrano, Caroline Klivans, Jill Pipher, and Björn Sandstede), site-visit team (Fioralba Cakoni, Rachel Kuske, and Ulrica Wilson), Brown administrative leadership (Provost Francis Doyle, Vice President of Research Greg Hirth and Senior Associate Dean James Russell), and ICERM and Brown-sponsored projects staff made our successful proposal possible. This is the third renewal of the initial grant and will fund ICERM through its 20th anniversary in 2030! We are grateful for the support of the NSF, which has enabled ICERM to grow into an internationally recognized center for mathematical innovation and collaboration.
Keep reading for more information about how to get involved at ICERM. We hope to see you at a workshop or read your proposal soon!
Sincerely,
Brendan Hassett
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Propose a Program
ICERM is accepting proposals from prospective program organizers from all areas in the mathematical sciences. All proposed topics should relate to ICERM's mission to foster and broaden the relationship between mathematics and computation. Learn more about each program type:
Semester Programs
Every Spring and Fall, ICERM hosts a semester-long research program led by a group of 5-10 organizers. Each is attended by a group of 20-40 long-term faculty, graduate students, and postdoctoral fellows. Semester Programs are typically structured around 3 major workshops open to short-term participants.
Topical Workshops
Topical Workshops run over 5 weekdays and focus on a timely and exciting theme that aligns with the institute's mission. They are typically scheduled in December, January, and May through August (around the dates of the semester programs). ICERM hosts 10-15 Topical Workshops per year.
Hot Topics Workshops
Hot Topics Workshops allow ICERM to move quickly in order to start the public exploration of recent mathematical breakthroughs and emerging topics. These 2-3 day workshops are organized on a few months notice. Hot Topics Workshop ideas can originate through suggestions from ICERM boards or the mathematical community.
Collaborate@ICERM
The Collaborate@ICERM program invites teams of 3-6 mathematical researchers to spend five days at the institute during the summer (May-August) or during the month of January. Each team should be engaged in a research project with a computational or experimental component. ICERM provides Collaborate@ICERM groups access to a variety of software packages, as well as high performance computing through Brown University.
Graduate Training Workshops
Graduate Training Workshops are 1-3 week events focused on a timely and exciting theme that aligns with the institute's mission. They specifically focus on training of graduate students, recent PhDs, and researchers who may be new to the subject. They are typically scheduled in December, January, and May through August (around the dates of the semester programs).
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Deadlines and Review Process
The next target deadline for proposals is October 1, 2025. Submissions and inquiries should be sent to ICERM Director Brendan Hassett. ICERM's Directorate and Scientific Advisory Board review all proposals. Applicants will receive feedback within a month of the SAB meeting.
Learn more at icerm.brown.edu/proposals.
| Help ICERM continue to share cutting-edge research and resources with every segment of the mathematical community! Donations to ICERM directly support our participants' needs, from family support to student scholarships. No matter how big or small, your gift can make an impact. | | |
Upcoming Opportunities
ICERM is accepting applications for the following workshops and programs, scheduled during and between Fall 2025 and Spring 2026. ICERM welcomes applications from faculty, postdocs, graduate students, industry scientists, and other researchers who wish to participate.
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Hot Topics Workshop
From Modeling to Learning with HPC
Sep 13 - 14, 2025
Organizing Committee: Nancy Amato, Torsten Hoefler, David Keyes, Satoshi Matsuoka, Lois McInnes, and Barry Smith
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Over the past four decades, demands of resolution and fidelity have driven the performance of simulations of first-principles mathematical models in science and engineering from MegaFlop/s to ExaFlop/s, and their datasets from MegaBytes to ExaBytes. Over the most recent decade, the application of machine learning to science and engineering systems has created a similar demand for performance and associated storage. Increasingly, the same science or engineering application is more effectively addressable by simulation and machine learning working in tandem than by either alone, resulting in a confluence of two formerly distinct research communities. This workshop aims to capitalize by highlighting opportunities for cross-fertilization in both directions.
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Fall 2025 Semester Program Workshop
Nov 10 - 14, 2025
Organizing Committee: Greta Panova, Jose Simenthal Rodriguez, and Mike Zabrocki
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 | | This workshop encompasses three major aspects of computation within representation theory and algebraic combinatorics. One concerns the development of efficient algorithms to compute important quantities in order to understand and classify them better. This is closely related to understanding what optimality we could expect and in particular the computational complexity aspects of those problems. Computational complexity class can also be used to understand the existence of combinatorial interpretations, particularly for major structure constants lacking positive formulas like Kronecker and plethysm coefficients. On the other hand, representation theory has seen important applications within computational complexity theory in the context of geometric complexity theory and quantum information theory. In addition to these topics, workshop participants will discuss the collection of “experimental” data, which helps formulate conjectures, find counterexamples and understand the behavior for several problems.
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Hot Topics Workshop
Nov 22 - 23, 2025
Organizing Committee: Theodore Drivas, Javier Gomez Serrano, In-Jee Jeong, Igor Kukavica, and Wojciech Ożański
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This workshop focuses on advancing the mathematical understanding of fluid dynamics, particularly the intricate phenomenon of turbulence. It aims to explore recent breakthroughs in the analysis of fluid equations, such as the Navier-Stokes and Euler equations, addressing fundamental questions of well-posedness, singularity formation, stability, and anomalous energy dissipation. Participants will explore cutting-edge mathematical techniques, including singularity formation, anomalous dissipation, convex integration, optimal transport, and regularity theory for partial differential equations.
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Topical Workshop
Dec 8 - 12, 2025
Organizing Committee: Ian Le, Pavlo Pylyavskyy, Jessica Striker, and Joshua Swanson
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Webs are diagrammatic tools for representing complex calculations graphically. These diagrams first arose from the representation theory of classical groups, and they have since become important in disparate areas of mathematics such as representation theory, topology, and algebraic geometry. This workshop is motivated by the belief that webs may serve as a nexus for disparate communities of mathematicians to meet. This includes not only researchers in algebraic combinatorics, Schubert calculus, and representation theory, but also those in algebraic geometry, topology and knot theory. The goal of this workshop is to bring together experts from these communities and spread knowledge of recent developments and perspectives on our shared interest: webs. The program will also allow distinct research groups to share current code functionality, as well as future needs and desires, with an aim towards building collaborative and open-source computing capabilities.
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Topical Workshop
Jan 5 - 9, 2026
Organizing Committee: Stefanos Aretakis, Radouane Gannouji, Elena Giorgi, Gaurav Khanna, Steven Liebling, and Thomas Maedler
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Extremal black holes are mathematical solutions of Einstein's equations describing stationary black holes with either maximal spin or maximal charge, and these black holes have special properties such as zero temperature. The aim of this workshop is to explore the implications and next steps of recent advances in understanding the dynamics and thermodynamics of near extremal black holes. By bringing together researchers in this area spanning the wide range of approaches, from rigorous mathematics, perturbation theory, and numerical methods, the workshop will focus on future directions.
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Topical Workshop
Jan 12 - 16, 2026
Organizing Committee: Tamara Broderick, Pete Mueller, Igor Pruenster, Sinead Williamson, and Yanxun Xu
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Statistical models are almost never right. All models involve certain parametric and structural assumptions. Bayesian nonparametric inference (BNP) is an increasingly widely used approach to mitigate the dependence on such assumptions. This program bring together researchers working in BNP, including computation, foundations, methodology and application of BNP methods, with the goal of identifying newly emerging computational strategies and inference approaches. The program and invited talks are planned to balance theoretical expertise, interest and prowess in computational methods, and exposure to selected substantial application areas. The intended nature of the program as identifying synergies of different approaches and potentially new research directions naturally leads to favoring breath over depth, with more emphasis on covering diverse areas rather than on in-depth discussions of a single specific theme.
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Spring 2026 Semester Program
Jan 20 - April 24, 2026
Organizing Committee: Harbir Antil, Julianne Chung, Petros Drineas, Youssef Marzouk, Agnieszka Miedlar, and Arvind Krishna Saibaba
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In many scientific fields, advances in data collection and numerical simulation have resulted in large amounts of data for processing; however, relevant and efficient computational tools appropriate to analyze the data for further prediction and decision-making are still in their infancy. To tackle these challenges, the scientific research community has developed and used probabilistic tools in at least two different ways: first, stochastic methods to model and quantify these uncertainties in applications where there is underlying uncertainty; second, in applications that may be inherently deterministic but randomness is used as an algorithmic tool to drastically reduce computational costs while retaining the high accuracy of classic approaches.
Stochastic and randomized algorithms have already made a tremendous impact in areas such as numerical linear algebra (where matrix sketching and randomized approaches are used for efficient matrix approximations), Bayesian inverse problems (where probabilistic approaches are used to define priors and for posterior sampling), and machine learning (where stochastic optimization methods are used to efficiently train network models).
The main goal of this program is to advance foundational research in stochastic and randomized methods for scientific computing and optimization, enabling their effective and widespread use in challenging applications. This includes the development of new mathematical approaches, convergence results, next-generation hardware, statistical guarantees, and error bounds, as well as the integration of these tools in scientific applications at scale. We also aim to create research and mentorship opportunities for researchers at all levels, including advanced undergraduate students, graduate students, postdocs, and senior scientists.
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