January 2024 Newsletter

Upcoming Workshops

February 5-9, 2024: Decision Making and Uncertainty

February 19-22, 2024: Computational Challenges and Optimization in Kinetic Plasma Physics

March 7-8, 2024: Methods for Solving and Analyzing Dynamic Models in the Face of Uncertainty and Cross-sectional Heterogeneity

March 11-15, 2024: Materials Informatics: Tutorials and Hands-On

March 25-29, 2024: Machine Learning in Electronic-Structure Theory

April 8-12, 2024: Machine Learning Force Fields

April 22-26, 2024: Learning Collective Variables and Coarse Grained Models

April 29-May 3, 2024: New Approaches to Ecological Dynamics of Microbial Communities (joint with NITMB)

May 13-17, 2024: Data Sciences for Mesoscale and Macroscale Materials Models

Accepting Applications for the Summer 2024 Long Program: The Architecture of Green Energy

The Summer 2024 Long Program (June 17 - August 23, 2024) is accepting applications. This program will focus on how mathematical modeling can help answer questions regarding the impact of green (low carbon) energy on society and the ways in which financial incentives and regulations and infrastructure changes can enhance outcomes and accelerate the transition to a green electricity system. It will identify the ways in which mathematical tools can inform and shape appropriate public and private investments and decisions, and navigate the trade-offs encountered in moving to a more sustainable economy.

Reports from the Intergovernmental Panel on Climate Change and other national and international scientific advisory bodies are spurring governments to make announcements about net zero commitments. The transition to economies with zero carbon will require substantial investment and deployment of new technologies for providing, transporting, storing and consuming green energy. It will also require institutional changes to manage an orderly and just green energy transition. This transition is happening very slowly due to technical, socio-economic, and political constraints. There is also uncertainty and complexity due to the wide range of actors shaping the energy transition and the interdependencies across sectors, infrastructures, and countries. Energy providers have been slow to increase renewable energy capacity and infrastructure at the rates required to keep global temperature rise in line with the goals of the Paris Agreement, for a range of reasons including their institutional incentives, and the changing policy and international environment. There is also increasing evidence that some of the policies and decisions that have already been made have imposed a greater burden on vulnerable and marginalized parts of society. In short, recent research across a range of disciplines has helped to understand the role and relationships across different institutions, drivers, and systems in failing to deliver the pace of change required in the energy system in a just manner and what can be done to speed it up. However, insufficient attention has been paid to the formal application of mathematics in this setting of complex systems with multiple sources of uncertainty and variability. This program is intended to initiate the development of a core body of research that will aim to provide a systematic framework or set of frameworks for analyzing some of these problems. It will bring together leading researchers who have demonstrated an interest and willingness to work at the boundary of different disciplines, but for whom face-to-face encounters are difficult to arrange due to disciplinary diversity and separation.

This Long Program is organized by Laura Diaz Anadon (University of Cambridge), Michael C. Ferris (University of Wisconsin-Madison), Dennice F. Gayme (Johns Hopkins University), and Andy Philpott (University of Auckland).

Apply here for The Architecture of Green Energy

Accepting Applications for the Fall 2024 Long Program: Statistical Methods and Mathematical Analysis for Quantum Information Science

The Fall 2024 Long Program (September 16 - December 13, 2024) is accepting applications. Quantum information science is a rapidly developing and broad field of research. It has made significant progress over the last decade, including the development of many promising applications such as efficient quantum computational algorithms, secure quantum communication protocols, and ultra-sensitive quantum sensors (to name just a few). Besides practical applications, quantum information science also sheds light on fundamental physics questions, including efficient descriptions of many-body systems, entanglement characterization of topological quantum systems, and quantum information scrambling of many-body systems. Novel mathematical tools and statistical models play a crucial role in investigating quantum systems. However, there are still many important open questions in quantum information science, which urgently need novel mathematical tools and statistical models. The aim of this program is to bring experts with different backgrounds of mathematics, control, statistics, physics, material, and computer science together, to spur transformational change in quantum information science.

This Long Program is organized by Aashish Clerk (University of Chicago), Liang Jiang (University of Chicago), Mazyar Mirrahimi (Inria Paris), and Pierre Rouchon (Mines Paris-PSL).

Apply here for Statistical Methods and Mathematical Analysis for Quantum Information Science

Accepting Applications for the Spring 2025 Long Program: Uncertainty Quantification and AI for Complex Systems

The Spring 2025 Long Program (March 3 - May 23, 2025) is accepting applications. The field of Uncertainty Quantification (UQ) has broad applications in science and engineering and provides a computational framework for quantifying input and response uncertainties, making model-based predictions and their inferences. As science and technology advance, UQ problems become more complex and diverse, requiring many concepts and tools from mathematics, statistics, machine learning, optimization, and advanced computing techniques. The fast development of Artificial Intelligence (AI) has benefited many fields, including UQ. Specifically, new AI and machine learning algorithms are applied to solve larger-scale and more complicated UQ problems. UQ, together with the advancements in AI and machine learning, has the potential to drive new scientific discoveries and enable engineers to design more robust and reliable systems.

This long program will focus on the newest development of UQ methodologies and how they can improve AI systems and provide solutions to modeling complex systems. It will also give an outlook on future UQ directions and challenges. Through all the activities proposed, the program will bring together interested parties, researchers, practitioners, and students from different areas of UQ, promote communication, and further break down the barriers between disciplines. The program also has a significant mentoring component, which connects researchers and students at different career stages.

This Long Program is organized by Mihai Anitescu (Argonne National Laboratory and University of Chicago), Xinwei Deng (Virginia Tech), Robert B. Gramacy (Virginia Tech), Fred Hickernell (Illinois Institute of Technology), Roshan Joseph (Georgia Tech), Lulu Kang (University of Massachusetts-Amherst), and C. Devon Lin (Queen's University), and Guang Lin (Purdue University).

Apply here for Uncertainty Quantification and AI for Complex Systems

Summer 2024 Math & Stats Bootcamp for Undergraduates

The Summer Undergraduate Mathematics and Statistics Accelerator (SUMSA) (June 10 - August 2, 2024) is accepting applications. SUMSA is an eight week mathematics and statistics summer bootcamp for undergraduates at U.S. colleges and universities which will be hosted by IMSI on the campus of the University of Chicago starting June 12, 2024. The aim of the program is to help prepare students for the rigors of graduate school in a mathematical science with lecture series and problems sessions taught by experienced postdocs and advanced graduate students from the University of Chicago. The primary focus of this bootcamp is basic coursework; in particular, the program is not an REU, which tends to be more project-oriented. Accepted applicants will be offered travel support and housing on the University of Chicago campus, and a stipend. The bootcamp is only available to participants who are able to attend in person. Participants are expected to spend the full eight weeks in residence during the program. The deadline for applications is February 16, 2024.

IMSI Requests Proposals to Host GROW in 2026 and 2027

The GROW Steering Committee is requesting proposals from groups wishing to host GROW in 2026 and/or 2027. GROW is intended to provide support and encouragement to undergraduate students considering going to graduate school in the mathematical sciences and to help build a profession in which gender identity is not a barrier to participation. The conference runs annually on a weekend in the fall, and is open to undergraduates from U.S. colleges and universities, including international students.

Hosts are typically departments or groups of departments offering Ph.D. programs in the mathematical sciences. Organizing committees are responsible for finding the funding needed for GROW. Proposals to host GROW are due by April 1, 2024. A description of the expected elements of a proposal can be found in the request for proposals. Proposals and questions may be sent to the steering committee at [email protected].

IMSI Seeks Proposals for Scientific Activity

IMSI is currently seeking proposals for long programs, workshops, interdisciplinary research clusters, and other scientific activity with a deadline of March 15, 2024. Information about how to submit proposals can be found on the proposal overview page and the resources linked therein. There are currently openings for long programs in 2025-26 and beyond, for workshops in the winter of 2025 and beyond, and for interdisciplinary research clusters in fall of 2024 and beyond. IMSI holds two proposal cycles per year, with deadlines of March 15 and September 15.

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IMSI acknowledges support from the National Science Foundation
(Grant No. DMS-1929348)

Institute for Mathematical and Statistical Innovation
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