题目：Fuzzy environments for the perturbation evaluation and application of uncertainty quantification
分享嘉宾：Gregory Kiar, Research Scientist, Center for the Developing Brain, Child Mind Institute
Gregory Kiar, PhD, is a research scientist at the Child Mind Institute. Throughout his degrees in Biomedical Engineering, Greg has developed techniques to study biosignal data, a turn-key tool that allows researchers to generate maps of brain connectivity from diffusion MRI data, and techniques to assess and improve the stability of research in neuroscience.
Greg’s research bridges the fields of numerical analysis and brain imaging to evaluate and improve the trustworthiness of techniques used to study the brain. He has developed infrastructures that support the reliable evaluation of neuroimaging experiments, studied the role that unavoidable computing errors play in results, and developed techniques to consider such errors when building robust models of the brain. His work ultimately seeks to inform decision-making surrounding robust data collection, image processing and biomarker discovery. Greg has considerable experience in computational statistics, uncertainty quantification, pipeline development and machine learning.
In addition to his research aims, Greg has participated in the organization of over a dozen hackathon-style events focused on training and collaboration, and taught several full-length courses at the university level. Greg regularly takes on mentorship opportunities, which are often geared towards teaching academic writing, fundamental computational skills or research best practices.
With an increase in awareness regarding a troubling lack of reproducibility in analytical software tools, the degree of validity in scientific derivatives and their downstream results has become unclear. The nature of reproducibility issues may vary across domains, tools, data sets, and computational infrastructures, but numerical instabilities are thought to be a core contributor. In neuroimaging, unexpected deviations have been observed when varying operating systems, software implementations, or adding negligible quantities of noise. In the field of numerical analysis, these issues have recently been explored through Monte Carlo Arithmetic, a method involving the instrumentation of floating-point operations with probabilistic noise injections at a target precision. Exploring multiple simulations in this context allows the characterization of the result space for a given tool or operation. In this article, we compare various perturbation models to introduce instabilities within a typical neuroimaging pipeline, including (i) targeted noise, (ii) Monte Carlo Arithmetic, and (iii) operating system variation, to identify the significance and quality of their impact on the resulting derivatives. We demonstrate that even low-order models in neuroimaging such as the structural connectome estimation pipeline evaluated here are sensitive to numerical instabilities, suggesting that stability is a relevant axis upon which tools are compared, alongside more traditional criteria such as biological feasibility, computational efficiency, or, when possible, accuracy. Heterogeneity was observed across participants which clearly illustrates a strong interaction between the tool and data set being processed, requiring that the stability of a given tool be evaluated with respect to a given cohort. We identify use cases for each perturbation method tested, including quality assurance, pipeline error detection, and local sensitivity analysis, and make recommendations for the evaluation of stability in a practical and analytically focused setting. Identifying how these relationships and recommendations scale to higher order computational tools, distinct data sets, and their implication on biological feasibility remain exciting avenues for future work.
- 北京时间[GMT+8] 7月8日(周五) 20:00~21:00
- 欧洲中部时间[CEST] 7月8日(周五) 14:00~15:00
- 美国东部时间[EST] 7月8日(周五) 08:00~09:00
Meeting ID: 9139 4010 836
主持人：徐婷 Child Mind Institute
张晗(博士), A*STAR, Singapore
张磊 (博士), University of Vienna, Austria
楊毓芳(博士), Freie Universität Berlin, Germany
杨金骉, MPI Psycholinguistics, the Netherlands
肖钦予, University of Vienna, Austria
王庆(博士), Montreal Neurological Institute, Canada
金淑娴, Vrije Universiteit Amsterdam, the Netherlands
金海洋(博士), New York University Abu Dhabi, UAE
葛鉴桥 (博士), 北京大学
张文昊(UT Southwestern Medical Center, USA)
张磊(University of Vienna, AUT)
徐婷(Child Mind Institute, USA)
滕相斌(MPI for Human Development, DEU)
邸新(New Jersey Institute of Technology, USA)