January 2025 Newsletter

Upcoming Workshops

January 29 - February 7, 2025: Winter School: Reduced-Order Modeling for Complex Enginering Problems - From Analysis to Practical Implementation

February 24-27, 2025: Uncertainty Quantification Strategies for Multi-Physics Systems and Digital Twins

March 3 - 7, 2025: UQ and Trustworthy AI Algorithms for Complex Systems and Social Good (Workshop 1 in the Spring 2025 Long Program on Uncertainty Quantification and AI for Complex Systems)

March 17 - 21, 2025: Emergent Behavior in Complex Systems of Interacting Agents

March 31 - April 4, 2025: Kernel Methods in Uncertainty Quantification and Experimental Design (Workshop 2 in the Spring 2025 Long Program on Uncertainty Quantification and AI for Complex Systems)

April 21 - 25, 2025: Uncertainty Quantification for Material Science and Engineeering (Workshop 3 in the Spring 2025 Long Program on Uncertainty Quantification and AI for Complex Systems)

May 5 - 9, 2025: Statistics Meets Tensors

May 19 - 23, 2025: Uncertainty Quantification and Machine Learning for Complex Physical Systems (Workshop 4 in the Spring 2025 Long Program on Uncertainty Quantification and AI for Complex Systems)

June 9 - 13, 2025: Statistical and Computational Challenges in Probabalistic Scientific Machine Learning

June 21 - 25, 2025: New Directions in Algebraic Statistics

July 28 - August 1, 2025: 15th International Conference on Monte Carlo Methods and Applications (MCM)

August 11 - 14, 2025: Contemporary Challenges in Large-Scale Sequence Alignments and Phylogenies

August 18 - 22, 2025: The Geometric Realization of AATRN (Applied Algebraic Topology Research Network)

Upcoming Long Programs

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

Accepting Applications for the Fall 2025 Long Program: Digital Twins

The Fall 2025 Long Program (September 15 - December 12, 2025) is accepting applications. A digital twin (DT) is a computational model of a physical system that continually updates its knowledge of the system by assimilating observational data to reduce uncertainties and improve predictions of the model, which in turn is used as a basis to inform decisions and optimally control the system to achieve a desired goal. The cycle of data assimilation and decision/control repeats over a continually evolving time horizon. Interest in DTs has intensified significantly in recent years in many areas of science, engineering, technology, health, finance, social systems, and beyond, driven by their potential to transform the role of models and data in decision-making for complex systems. At the same time, DTs present significant mathematical, statistical, and computational challenges. This stems from the enormous complexity and scale of models describing many natural and engineered systems, the numerous uncertainties that underlie them, the complexity of observing systems and indirect and multimodal nature of the data they produce, the need to execute rapidly enough to support decisions and controls in time scales relevant to the physical system, and the critical societal impact of model-based decision making. The long program will elucidate the mathematical, statistical, and computational challenges presented by DTs, explore avenues for overcoming them, and discuss state of the art applications to problems arising in complex systems in science, engineering, technology, medicine, and beyond. Events include an opening tutorial on data assimilation, three workshops on foundational components of DTs—data assimilation and inverse problems, optimal control and decision making under uncertainty, reduced order and surrogate models—and a final workshop integrating these components to address applications of digital twins in complex systems.

This Long Program is organized by Ludovic Chamoin (Ecole Normale Superieure Paris-Saclay), Omar Ghattas (The Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin), Youssef Marzouk (MIT), Georg Stadler (Courant Institute of Mathematical Sciences, New York University), Karen-Veroy-Grepl (Technical University of Eindhoven).

Apply here for Digital Twins

Accepting Applications for the Spring 2026 Long Program: Theoretical Advances in Reinforcement Learning and Control

The Spring 2026 Long Program (March 9 - May 29, 2026) is accepting applications. Reinforcement learning (RL) and control theory are concerned with training intelligent agents to make sequential decisions by interacting with an environment. In both formulations, an agent learns to navigate its surroundings through a process of trial and error, receiving feedback in the form of rewards or penalties based on the actions it takes. The agent’s goal is to learn a policy, a mapping from states to actions, that maximizes the cumulative reward over time. In the past few years, there has been a notable increase in enthusiasm for RL and the interplay between learning and control. The surge of interest is driven by the compelling application of RL and control methods to diverse challenges in artificial intelligence, robotics, and the natural sciences. Numerous breakthroughs owe their success to large-scale computational resources, creative deployment of adaptable neural network structures and training approaches, as well as both modern and traditional decision-making algorithms. Nevertheless, there remains a significant gap in our understanding regarding the conditions, reasons, and the degree to which these algorithms effectively operate. Such a challenge has drawn significant attention from various communities including computer science, numerical analysis, artificial intelligence, control theory, operations research, and statistics. This program aims to advance the theoretical foundations of reinforcement learning (RL) and control, and foster new collaborations between these researchers.

This Long Program is organized by Xinyi Chen (Princeton University), Elad Hazan (Princeton University), Cong Ma (University of Chicago), Nati Srebro (Toyota Technological Institute at Chicago), and Andrea Zanette (Carnegie Melon University).

Apply here for Theoretical Advances in Reinforcement Learning and Control

Upcoming Opportunities at IMSI

NSF PRIMES

IMSI encourages qualified researchers in the mathematical sciences to submit a proposal to the NSF PRIMES program. NSF PRIMES . The NSF Division of Mathematical Sciences Partnerships for Research Innovation in the Mathematical Sciences (PRIMES) program aims to enhance partnerships between minority-serving institutions and DMS-supported Mathematical Sciences Research Institutes, including IMSI. The activity seeks to boost the participation of members of groups underrepresented in the mathematical sciences by enabling their increased involvement in research programs at the institutes. IMSI encourages those interested to apply for a PRIMES grant for participation in the Digital Twins Long Program for the February 12, 2025 NSF application deadline, or the Theoretical Advances in Reinforcement Learning and Control Long Program for the August 20, 2025 NSF application deadline. The deadlines for applications are February 12, 2025 and August 20, 2025.

Summer 2025 Math & Stats Bootcamp for Undergraduates

The Summer Undergraduate Mathematics and Statistics Accelerator (SUMSA) (June 16 - August 8, 2025) 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 from June 16 to August 8, 2025. 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 14, 2025. Go to the SUMSA webpage for the most current information.

Infinite Possibilities Conference

IMSI will host the next Infinite Possibilities Conference , April 11-12, 2025. The Infinite Possibilities Conference (IPC) is a national conference designed to broaden participation in the mathematical sciences. Conference sessions are designed to showcase the infinite possibilities of mathematics to participants at all stages of the pipeline, from undergraduate and graduate students to professionals in academia, industry and government. IPC 2025 marks the 20th year of IPC programming! Applications for attendance and funding opportunities can be found at IPC webpage. The deadline for applications is February 24, 2025.

IMSI Seeks Proposals for Scientific Activity

IMSI is currently seeking proposals for long programs, workshops, interdisciplinary research clusters, and other scientific activity. 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 2026-27 and beyond, and openings for workshops in the Fall of 2025 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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