Ankur Singha

Ankur Singha is a Postdoctoral Researcher at Technical University of Berlin, where he is part of the Machine Learning group. His research focuses on advancing Generative AI models to optimize sampling processes in Lattice Field Theory, particularly for lattice QCD simulations. He holds a Ph.D. in Physics from the Indian Institute of Technology Kanpur (2023), an M.Sc. in Physics from Tezpur University (2017), and a B.Sc. in Physics from Gauhati University (2015).

His research interests include Lattice Field Theory, Generative Models, Renormalization Group Theory, and Equations Learner (EQL). During his doctoral studies, he applied conditional GANs and flow-based models to simulate lattice models such as the Gross-Neveu model, phi4 theory, and U(1) gauge theory in two dimensions.

While no specific articles or awards are listed in the provided information, his work bridges machine learning techniques with theoretical physics challenges, contributing to computational efficiency in complex simulations.

He has not listed any grants or advising roles in the provided information.

He is part of the Machine Learning group at Technical University of Berlin.