Pritish Mohapatra received his doctorate in Computer Science and Engineering (CSE). His research work was supervised by Prof. C V Jawahar. Here’s a summary of  his research work on Optimization for and by Machine Learning: 

In machine learning, tasks like making predictions using a model and learning model parameters can often be formulated as optimization problems. The feasibility of using a machine learning model depends on the efficiency with which the corresponding optimization problems can be solved. As such, the area of machine learning throws up many challenges and interesting problems for research in the field of optimization. While in some cases, it is possible to directly apply off-the-shelf optimization methods for problems in machine learning, in many other cases, it becomes necessary to develop optimization algorithms that are tailor-made for specific problems. On the other hand, developing optimization algorithms for specific problem domains can itself be helped by machine learning techniques. Learning optimization algorithms from data can help relieve tedious effort required to develop optimization methods for new problem domains. The challenge here is to appropriately parameterize the space of algorithms for different optimization problems. In this context, we explore the interplay between the areas of optimization and machine learning and make contributions in specific problems of interest that lie in the overlap of these fields. 

We look into the optimization challenge presented by multi-class classification with a large number of output classes and propose a partial-linearization based approach that intuitively generalizes over several popular optimization algorithms applicable to this task. It is popular to use measures like average precision (ap) and normalized discounted cumulative gain (ndcg) to evaluate a model for a classification task or a ranked retrieval task, however, they are not as popularly used for learning the parameters of these models. This is because of the difficulty in optimizing these non-decomposable loss functions. We propose a useful characterization of a large class of such loss functions that we show are amenable to efficient optimization and present an algorithm that can be used to efficiently optimize this class of loss functions. On the other hand, we explore the possible application of machine learning techniques for developing optimization algorithms, specifically, for combinatorial optimization problems. A wide class of approximate algorithms for np-hard combinatorial optimization problems solve a continuous relaxation of the original problem and then round the fractional solution to obtain an approximate discrete solution. The rounding procedures involved in such methods are nontrivial and generally have to be ingeniously designed for each task. We propose a framework that allows the learning of such rounding procedures from unsupervised data. Such a learning based approach permits rapid development of algorithms for novel optimization problems.