PhD Shinichi Nakajima
PhD Shinichi Nakajima
Research Junior Group Lead
Affiliation: BIFOLD
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".
- Generative modeling
- Bayesian inference
- Bayesian optimization
- Inverse problems
- Quantum computing
Marco Morik, Jesse Palarus, Carmen Vidaurre, Klaus-Robert Müller, Shinichi Nakajima
Decoupling Time and Space: A Temporally Conditioned Refinement for EEG Source Imaging
Sidney Bender, Benedikt Kunz, Ahmed Zeid, Shinichi Nakajima, Klaus-Robert Müller, Marco Morik
Towards Fast and Disentangled Counterfactuals for Visual Foundation Models
Ankur Singha, Jacob Finkenrath, Karl Jansen, Vipul Arora, Shinichi Nakajima
Multilevel Plaquette-Space Sampling for Lattice Gauge Theories with Local Constraint Solves
Ping Xiong, Shanglin Li, Yi Ding, Thomas Schnake, Shinichi Nakajima
Explaining Hyperbolic Neural Networks via Geometry-Aware Relevance Propagation
Ankur Singha, Jacob Kauffmann, Karl Jansen, Jacob Finkenrath, Vipul Arora, Shinichi Nakajima
Sector-Resolved Flow Sampling for Topologically Frozen Lattice Gauge Theories
Symbolic XAI
Researchers at BIFOLD have been exploring how to make AI explain itself in the same way, people explain themselves. The team’s work focuses on making AI predictions as clear and intuitive as a human explanation.
ICLR 2025 Conference Contributions
The BIFOLD research groups Machine Learning and Explainable Machine Learning in Medicine will participate in ICLR 2025, contributing eight papers, one blog article, and one talk. The conference is set for April 24-28, 2025, in Singapore.
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.