PhD Shinichi Nakajima
PhD Shinichi Nakajima
Research Junior Group Lead
Dr. Shinichi Nakajima is a member of Machine Learning Group in Technische Universität Berlin. He received the master degree on physics in 1995 from Kobe university, and worked with Nikon Corporation until September 2014 on statistical analysis, image processing, and machine learning. He received the doctoral degree on computer science in 2006 from Tokyo Institute of Technology. His research interest is in theory and applications of machine learning, in particular, Bayesian inference, generative modeling, uncertainty estimation, explainable artificial intelligence, and their applications for computer vision, natural language processing, science and quantum computing. At the BIFOLD he leads the research group "Probabilistic Modeling and Inference".
Gabriel Nobis, Maximilian Springenberg, Marco Aversa, Michael Detzel, Rembert Daems, Roderick Murray-Smith, Shinichi Nakajima, Sebastian Lapuschkin, Stefano Ermon, Tolga Birdal, Manfred Opper, Christoph Knochenhauer, Luis Oala, Wojciech Samek
Generative Fractional Diffusion Models
Dennis Grinwald, Philipp Wiesner, Shinichi Nakajima
Federated Learning over Connected Modes
Marco Morik, Ali Hashemi, Klaus-Robert Müller, Stefan Haufe, Shinichi Nakajima
Enhancing Brain Source Reconstruction through Physics-Informed 3D Neural Networks
Andrea Bulgarelli, Elia Cellini, Karl Jansen, Stefan Kühn, Alessandro Nada, Shinichi Nakajima, Kim A. Nicoli, Marco Panero
Flow-based Sampling for Entanglement Entropy and the Machine Learning of Defects
Thomas Schnake, Farnoush Rezaei Jafaria, Jonas Lederer, Ping Xiong, Shinichi Nakajima, Stefan Gugler, Grégoire Montavon, Klaus-Robert Müller
Towards Symbolic XAI -- Explanation Through Human Understandable Logical Relationships Between Features
Tackling Data Heterogeneity in Federated Learning
A persistent challenge in Federated Learning (FL) lies in handling statistical heterogeneity—namely, if the clients’ distributions are different from each other. Shinichi Nakajima, BIFOLD research Grouplead and his team propose FLOCO (Federated Learning over Connected Modes), to tackle those issues.