Ali Hashemi

Ali Hashemi is a Researcher at the Berlin Institute for the Foundations of Learning and Data (BIFOLD) and a Postdoctoral Researcher at Technical University of Berlin. He holds a PhD (Dr. rer. nat.) with distinction from TU Berlin (2022) and an MSc in Electrical Engineering from Sharif University of Technology (2013). His research focuses on machine learning, Bayesian methods, and neuroimaging applications, particularly in inverse problems and explainable AI. He has contributed to advancements in structured noise modeling, robust source localization, and 3D neural networks for brain imaging. His work bridges signal processing and AI to improve biomedical data analysis.

Education:

• PhD in Machine Learning, Technical University of Berlin (2022)
• MSc in Electrical Engineering, Sharif University of Technology (2013)

Research Interests:

• Machine Learning & Optimization
• Bayesian Inference
• Neuroimaging (M/EEG, fMRI)
• Explainable AI & Diffusion Models
• Signal Processing for Biomedical Applications

Key Achievements:

• Developed the 'Champagne Algorithm' for noise estimation in brain imaging
• Published widely in top venues like NeurIPS and NeuroImage
• Recipient of PhD distinction award for outstanding dissertation

Labs & Affiliations: Active in TU Berlin's Uncertainty & Inverse Modeling group and Charité’s Brain and Data Science lab. Collaborates on open-source tools for M/EEG analysis (e.g., contributions to MNE-Python).