Shailesh Chandrasekharan
Professor of Physics

Prof. Chandrasekharan is interested in understanding quantum field theories non-perturbatively from first principles calculations. His research focuses on lattice formulations with emphasis on strongly correlated fermionic systems of interest in condensed matter, particle and nuclear physics. He develops novel Monte-Carlo algorithms to study these problems. He is particularly excited about solutions to the notoriously difficult sign problem that haunts quantum systems containing fermions and gauge fields. He recently proposed an idea called the fermion bag approach , using which he has been able to solve numerous sign problems that seemed unsolvable earlier. Using various algorithmic advances over the past decade, he is interested in understanding the properties of quantum critical points containing interacting fermions. Some of his recent publications can be found here .

Current Research Interests

I am interested in strongly correlated quantum phenomena that arise naturally in nuclei, dense nuclear systems, quantum anti-ferromagnets, high Tc materials etc. I develop novel quantum Monte Carlo techniques to solve simplified microscopic lattice models that are expected to capture the important physics in these physical phenomena.  I am particularly fascinated by quantum critical phenomena that can occur in such models where the details of the microscopic models are no longer important and the answers one gets are universal and more broadly applicable. My goal is to compute these universal properties without approximations where possible. One of my expertise is in the area of solving sign problems that hinder Monte Carlo methods when applied to such systems. 

Current Appointments & Affiliations

Contact Information

Some information on this profile has been compiled automatically from Duke databases and external sources. (Our About page explains how this works.) If you see a problem with the information, please write to Scholars@Duke and let us know. We will reply promptly.