Markov Chain Monte Carlo Methods in Quantum Field Theories

Monte Carlo Method for Integration.- Monte Carlo with Importance Sampling.- Markov Chains.- Markov Chain Monte Carlo.- MCMC and Feynman Path Integrals.- Reliability of Simulations.- Hybrid (Hamiltonian) Monte Carlo.- MCMC and Quantum Field Theories on a Lattice.- Machine Learning and Quantum Field Theories.- C++ Programs.

Anosh Joseph is an Assistant Professor of Physics at the Indian Institute of Science Education and Research (IISER) Mohali, India. A graduate of Indian Institute of Technology (IIT) Madras, India, he obtained his PhD at Syracuse University, USA, in 2011. Since then, he has held post-doctoral Research Associate positions at the Los Alamos National Laboratory (LANL), USA; Deutsches Elektronen-Synchrotron (DESY), Germany; Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge, UK; and the International Centre for Theoretical Sciences (ICTS) of the Tata Institute of Fundamental Research (TIFR), India.

As theoretical and computational physicist his research explores ideas to solve problems in strongly coupled quantum field theories, including Quantum Chromodynamics (QCD), supersymmetric field theories, and quantum field theories with complex actions. He has explored various non-perturbative phenomena occurring in field theories, such as phase transitions, bound states of elementary particles, and symmetry breaking using analytical and numerical methods.

He has published numerous peer-reviewed journal articles on lattice quantum field theory, supersymmetric field theory, complex Langevin dynamics, and non-commutative field theory.