Ali Hashemi is a research scientist at the Berlin Institute for the Foundations of Learning and Data (BIFOLD), and a postdoctoral researcher at TU Berlin with over six years of experience and domain expertise in machine learning, statistics, optimization, and signal processing. Previously, he worked at Uncertainty, Inverse Modeling and Machine Learning group at TU Berlin, and Brain and Data Science lab at Charité –Universitätsmedizin Berlin. His research interests lie at the intersection of Diffusion models and Explainable AI with applications to inverse problems and Neuroimaging. He received his PhD (Dr. rer. nat.) with distinction in 2022 from TU Berlin, and his MSc degree in Electrical Engineering from Sharif University of Technology in 2013.

Selected Publications

• Efficient Hierarchical Bayesian Inference for Spatio-temporal Regression Models in Neuroimaging [NeurIPS 2021]
• Joint Learning of Full-structure Noise in Hierarchical Bayesian Regression Models [TMI]
• Unification of Sparse Bayesian Learning Algorithms for Electromagnetic Brain Imaging with the Majorization Minimization Framework [NeuroImage]
• Robust Estimation of Noise for Electromagnetic Brain Imaging with the Champagne Algorithm [NeuroImage]