Invited and Special Sessions

Invited Sessions

  1. Statistical Machine Learning in High Dimensional Data
  2. Recent Application and Methodology Development on Causal Inference and Level Set Estimation
  3. Recent Advances in Large-scale Time Series Analysis
  4. Statistical Learning Methods for Large and Complex Data Sets
  5. Some Recent Developments in Dynamic Treatment Regimes
  6. Analyses of coronavirus pathogen-host interactions via sequence/structure/dynamics-based methods
  7. New Frontiers in Statistical Learning Theory
  8. Alumni Invited Session
  9. Statistical Decision Mechanisms and their Applications
  10. Biomarker Trial Design and Analysis Methods
  11. Integrative statistical methods for high-dimensional omics data
  12. On the Impact of the Bayesian Paradigm in Machine Learning and Knowledge Discovery
  13. Recent Advances in Computational Bayesian Methods

Special Invited Session on Convergence Science

Sarah F. Muldoon, Math, University at Buffalo

Modeling and analyzing neuroimaging data across scales

Network neuroscience is a rapidly growing field that uses tools from network theory to investigate brain networks across multiple scales and modalities. While the use of network analysis in neuroscience has led to many important insights about brain structure and function, properly mapping neuroimaging data to a graph presents multiple difficulties. In this talk, I’ll discuss some of the challenges of building networks from neuroimaging data and why the structure of these networks can be difficult to properly interpret. Further, because many network metrics were designed for other systems such as social networks, typical network measures may not be appropriate for detecting and analyzing structure in brain networks. I’ll present work that aims to modify existing network metrics and/or design new metrics that are specifically tailored to the features of brain networks, both at large at small scales.


Dr. Sarah Muldoon is an Associate Professor in the Mathematics Department, core faculty in the Computational and Data-Enabled Sciences and Engineering Program, and member of the Neuroscience Program at the University at Buffalo, SUNY. Her research interests lie at the intersection of experiment and theory with a focus on applications of network theory to neuroscience data. She has spent extensive time working in experimental neurobiology labs and now runs a research group that couples theoretical advancement, computational modeling, and data-intensive analysis to study the relationship between structure and function in brain networks.