The two NSF-CBMS Regional Research Conferences in the Mathematical Sciences for 2023 are now set! These are a series of five-day conferences that feature a distinguished lecturer and seek to stimulate interest and activity in one sharply focused area of the mathematical sciences. See below for more information on the 2023 topics, lecturers, and dates/locations. CBMS is the Conference Board of the Mathematical Sciences, an organization representing 18 professional societies in the mathematical sciences. SIAM is proud to be a member of the CBMS, and as such, part of the NSF-CBMS Regional Research Conferences. Learn more and apply for these conferences at the respective websites below.
Foundations of Causal Graphical Models and Structure Discovery
Understanding causality is arguably the ultimate goal in any field of science. Knowledge about causality allows one to predict a system’s behavior under external interventions, a key step towards understanding and engineering that system. While the gold standard for establishing causality remains controlled experimentation, such experimentation is not always possible due to practical or ethical concerns. Inferring causality from observational data thus has become an increasingly popular area of study, attracting researchers from statistics, philosophy, machine learning, artificial intelligence, and data science. The ever-changing field of causal discovery makes for a steep learning curve for students and junior researchers. This conference aims to provide a deep review of causal discovery to help orient researchers new to the topic.
Deep Learning and Numerical PDEs
The conference lectures will focus on the latest developments on the theory and applications of deep learning, bridging models and algorithms from two different fields: (1) machine learning, including logistic regression and deep neural networks; and, (2) numerical PDEs, including finite element and multigrid methods. The lecture series will build upon a discussion of the latest developments in machine learning models and algorithms and will present cutting-edge research on intrinsically connected topics. It is expected that this conference will bring novel insights into the understanding of deep learning, and further promote their analysis and applications in different scientific and engineering fields.
Foundations of Causal Graphical Models and Structure Discovery
Understanding causality is arguably the ultimate goal in any field of science. Knowledge about causality allows one to predict a system’s behavior under external interventions, a key step towards understanding and engineering that system. While the gold standard for establishing causality remains controlled experimentation, such experimentation is not always possible due to practical or ethical concerns. Inferring causality from observational data thus has become an increasingly popular area of study, attracting researchers from statistics, philosophy, machine learning, artificial intelligence, and data science. The ever-changing field of causal discovery makes for a steep learning curve for students and junior researchers. This conference aims to provide a deep review of causal discovery to help orient researchers new to the topic.