September 2023 Newsletter
October 9-13, 2023: Algebraic Statistics for Ecological and Biological Systems
November 6-10, 2023: Algebraic Economics
December 11-15, 2023: Bayesian Statistics and Statistical Learning: New Directions in Algebraic Statistics
January 10-12, 2024: Teaching and Evaluating Data Communication At Scale
February 5-9, 2024: Decision Making and Uncertainty
Accepting Applications for the Spring 2024 Long Program: Data-Driven Materials Informatics: Statistical Methods and Mathematical Analysis
The Spring 2024 Long Program (March 4 - May 24, 2024) is accepting applications on a rolling basis. Materials informatics is an emerging field defined by the use of simulation tools combined with methods from data sciences and machine learning to better understand materials properties and design innovative materials. The models which are considered cover an extremely wide range, from Schrödinger's equation, which describes matter at the (sub)atomistic scale, to the equations of continuum mechanics. Mathematical sciences play a key role in materials informatics, both to construct the databases used to train machine learning algorithms (since these databases are made of reference simulation results), and to harness them in order to extract the most relevant information. The aim of this program is bring together a diverse scientific audience, both between scientific fields (physical sciences, materials sciences, biophysics, etc) and within mathematics (mathematical modeling, numerical analysis, statistics and data analysis, etc), to make progress on key questions of materials informatics.
This Long Program is organized by Claudia Draxl (Humboldt-Universität zu Berlin), Risi Kondor (University of Chicago), Marina Meila (University of Washington), Danny Perez (Los Alamos National Laboratory), Gabriel Stoltz (Ecole des Ponts and Inria), and Francois Willaime (French Alternative Energies and Atomic Energy Commission (CEA)).
Apply here for Data-Driven Materials Informatics: Statistical Methods and Mathematical Analysis.
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
Farewell to Carry the Two's Co-Hosts
The third season of Carry the Two concluded this month with a farewell message from the podcast's hosts, Sadie Witkowski and Ian Martin. The duo announced their career updates and departure from the podcast in their episode that was released on September 19.
You can find all of Carry the Two's episodes on IMSI's website and can listen and subscribe on podcast platforms including Apple Podcasts, Google Podcasts, Spotify, and Stitcher.
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