September 2024 Newsletter

Upcoming Workshops

September 30 - October 4, 2024: Quantum Networks

October 14-18, 2024: Quantum Sensing

October 28-31, 2024: Quantum Hardware

November 11-14, 2024: Quantum Error Correction

December 9-13, 2024: Mathematical Modeling of Biological Interfacial Phenomena

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

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

OPPORTUNITY: IMSI Summer Internship Program

IMSI encourages qualified graduate students in math or statistics in the US to apply for the Summer 2025 Internship Program IMSI Summer Internship Program . The IMSI Summer Internship Program is hosted by the University of Illinois Urbana-Champaign. Internship hosts will primarily be companies and scientific labs in Urbana-Champaign, Chicago, and the surrounding region. Internship hosts for the IMSI in previous years have included AbbVie Innovation Center, Ameren Innovation Center, Bud Analytics Lab, Wolfram Research, Personify, Caterpillar Data Innovation Lab, and the Urbana-Champaign Public Health District, among others. Scientific internship hosts have included the University of Illinois departments of Entomology, Speech and Hearing Science, Industrial Engineering, Plant Biology, the University of Illinois College of Medicine at Chicago, the USDA Agricultural Research Service, and more. Summer 2025 application and deadline: The Summer 2025 application closes November 1, 2024. Structure of the program: The program begins with a two week skills workshop on computation and data science using Python and R. Prior coding experience is not required. Students will then be placed in internships for eight weeks during the summer. 2025 Program Dates: The boot camp will be May 23, May 27—30, and June 2—6, while the internship will be eight weeks in the date range of June 9 to August 15 (actual dates to be arranged between intern and host). Eligibility: This program is open to Ph.D. students in mathematics or statistics programs in the United States. Women and students from historically underrepresented US minority groups are strongly encouraged to apply. Participants must be eligible to work in the United States. The program is open to both US and international students based in the US. International students will be responsible for arranging work authorization (e.g. CPT) before starting the program. Financial support: Costs incurred in traveling to Urbana-Champaign (if required for program participation) can be reimbursed (subject to NSF guidelines). In addition, interns receive a stipend from IMSI along with salary from the host company. Participants accepted into the program will be provided with more information about the rules for reimbursement and the amount of stipend support. The deadline for applications is November 1, 2024..

Apply here for The IMSI Summer Internship Program

OPPORTUNITY: 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..

IMSI Seeks Proposals for Scientific Activity

IMSI is currently seeking proposals for scientific activity, with a deadline of March 15, 2025. Information about how to submit proposals can be found on the proposal overview page and the resources linked therein. During this cycle, proposals for interdisciplinary research clusters (IRCs), workshops, and other scientific activities will be considered. There are currently openings for workshops in the winter of 2026 and beyond. IMSI holds two proposal cycles per year, with deadlines of March 15 and September 15.

IMSI Launches Season Three of its Podcast, Carry the Two

Carry the Two, the IMSI podcast, launched Season Three in September. Season Three is inspired by the upcoming general election in the US and focuses on the mathematics and statistics that can be used to describe and understand the various ways that democracy can work. The hosts of Carry the Two, Sam Hansen and Sadie Witkowski, will spend Season Three in conversation with mathematicians and scientists who are probing the nature of different voting systems, the dynamics of coalitions, and other topics central to a healthy democracy.

Episodes are available on your favorite podcast hosting sites like Apple Podcasts or Spotify, and can also be found at Carry the Two

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

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