معرفی
Dr. Mihail Bogojeski is a postdoctoral researcher at the Technical University of Berlin, affiliated with the Machine Learning Group and the Berlin Institute for the Foundations of Learning and Data (BIFOLD). His research focuses on advancing machine learning techniques for applications in the physical sciences and medical sciences, particularly in quantum chemistry, computational biology, brain-computer interfaces, and sequential data analysis.
- B.Sc. in Software and Information Engineering (2014), Vienna University of Technology
- M.Sc. in Computer Science (2017), TU Berlin
- Ph.D. in Computer Science (2023), TU Berlin
His research integrates geometric deep learning with quantum chemistry to develop models for predicting electronic structure of molecular systems and electron densities. Recent work includes equivariant neural networks for molecular wavefunction prediction, generative time-series models for industrial aging processes, and explainable AI frameworks for catalyst discovery. Publications highlight his expertise in machine learning, quantum chemical accuracy, and multimodal data augmentation.
His 15 most recent articles focus on machine learning applications in chemistry, neuroscience, and industrial process modeling, with subfields such as generative models, equivariant networks, density functional theory, and neural signal processing.
