May 2025 Newsletter

Upcoming Workshops

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

July 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: Bridging Theory and Practice

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

September 3 - 5, 2025: Discrete Exterior Calculus: DIfferential Geometry and Applications

September 15 - 19, 2025: Opening Tutorial: Mathematical, Statistical, and Computational Foundations of Digitial Twins (Workshop 1 in the Fall 2025 Long Program on Digital Twins)

October 6 - 10, 2025: Data Assimilation and Inverse Problems for Digital Twins (Workshop 2 in the Fall 2025 Long Program on Digital Twins)

October 20 - 24, 2025: Data Science at the Intersection of Public Health and the Environment: an Ideas Lab

October 27 - 31, 2025: Optimal Control and Decision Making Under Uncertainty for Digital Twins (Workshop 3 in the Fall 2025 Long Program on Digital Twins)

November 10 - 14, 2025: Reduced Order and Surrogate Modeling for Digital Twins (Workshop 4 in the Fall 2025 Long Program on Digital Twins)

December 1 - 5, 2025: Applications of Digital Twins to Large-Scale Complex Systems (Workshop 5 in the Fall 2025 Long Program on Digital Twins)

Accepting Applications for the Fall 2025 Long Program: Digital Twins

The Digital Twins 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 Long Program on Theoretical Advances in Reinforcement Learning and Control 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 and 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 Mellon University).

Apply here for Theoretical Advances in Reinforcement Learning and Control

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 2027-28, and for workshops from August 2026 through July 2027. 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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