Constrained Bayesian optimization (BO) is aimed to solve constrained optimization problems where objective and constraint(s) are black-box expensive-to-evaluate functions. In this talk, I develop a constrained batch BO algorithm, dubbed c-BBO, which allows the user to: (i) incorporate noise in objective and constraints, (ii) suggest new querying points in batch, and (iii) specify the tolerance to constraint violation at each time step. First, I introduce the main components of the c-BBO algorithm, namely, the underlying Bayesian model for objective and constraint(s) based on Gaussian Processes regression, and our data acquisition mechanism based on a novel extension of the constrained Expected Improvement. Then, I illustrate c-BBO using several synthetic examples showing that c-BBO manages to sample at feasible locations within the user pre-specified tolerance, and that it is able to find the feasible optimum in a considerably small number of steps (compared to approaches that use a single update). Finally, I briefly outline how c-BBO can be utilized to tune ranking systems. This work was jointly developed with Dr. Aditya Maheshwari.