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By Meenakshi Khosla explores data-driven modeling in large-scale naturalistic neuroscience, focusing on brain activity representation and computational models.
Explores text mining of long-tail data in neuroscience and brain connectivity, including named entity recognition, protein concentration mining, and comparison of connectivity matrices.
Delves into Big Data in neuroscience, analyzing large datasets and addressing challenges in data organization, standardization, integration, and visualization.
Explores neuroimaging basics, brain network scales, connectivity, history, and physics, emphasizing the importance of understanding data at different scales.
Explores the development of data science, education initiatives, and challenges in bridging the gap between data scientists and domain experts at EPFL.
Introduces the Applied Data Analysis course at EPFL, covering a broad range of data analysis topics and emphasizing continuous learning in data science.
Delves into the intersection of physics and data in machine learning models, covering topics like atomic cluster expansion force fields and unsupervised learning.
