Establishing general findings on mental function is plagued by the diversity of experimental paradigms, mental processes, and psychological traits. I will present a research program to address these challenges in brain imaging by using explicit predictive modeling (machine learning) to assemble a coherent picture across a very diverse set of conditions –psychological manipulations or individual traits–, increasing statistical power and generality of findings. couple of sentences briefly describing the event.
Gaël Varoquaux is a tenured research director at Inria. His research focuses on statistical-learning tools for data science and scientific inference. Since 2008, he has been exploring data-intensive approaches to understand brain function and mental health. More generally, he develops tools to make machine learning easier, with statistical models suited for real-life, uncurated data, and software for data science. He co-funded scikit-learn, one of the reference machine-learning toolboxes, and helped build various central tools for data analysis in Python. Varoquaux has contributed key methods for learning on spatial data, matrix factorizations, and modeling covariance matrices. He has a PhD in quantum physics and is a graduate from Ecole Normale Superieure, Paris.