My research interest primarily lies in machine learning and optimization. Below are some specific themes I have worked on with my collaborators in industry and academia.
- ML solutions for Kaizala [Microsoft]. Microsoft Kaizala is a mobile app and service designed for large group communications and work management. Kaizala makes it easy to connect and coordinate work with your entire value chain, including field employees, vendors, partners, and customers wherever they are. Focused on improving the user experience, we are developing machine learning based algorithms for Kaizala.
- ML solutions for Office Lens [Microsoft]. We are developing data-driven deep learning based solutions for various scenarios in the Office Lens app. An important requirement is to have implementation such that the algorithms should run in mobile devices. Our algorithms/codes is being consumed by the internal teams.
- Product recommendation [Microsoft]. We proposed and developed a product recommendation algorithm for a Microsoft customer.
- Style recommendation for fashion [Amazon]. We developed algorithms for cold-start product recommendation, based on user attributes and product information. Our algorithms have been productionized for internal launch.
- Competitive pricing of products [Amazon]. Our goal is to estimate competitive reference pricing of millions of products sold in Amazon. This has been deployed internally and the price recommendations are consumed by multiple internal consumers.
- Identifying product safety concern [Amazon]. We developed algorithms that identify quality defects in products sold at Amazon’s platform. The quality defects of interest to me are those that may result in any safety concern. The model have been productionized and is being consumed by internal teams.
- Identify consumable products [Amazon]. The goal here is to identify products that are consumables.
- Multilingual translation. We propose a novel geometric framework for learning mapping words of multiple languages. An initial version of the work is at [arXiv:1808.08773]. The work has been accepted in the Transactions of the Association for Computational Linguistics (TACL).
- McTorch. Currently working on McTorch, a manifold optimization library for deep learning. It is a Python package that adds manifold optimization functionality to PyTorch.
- Low-rank tensor learning: We propose to learn a low-rank tensor in the regularized latent trace-norm framework. Existing works aim at learning a sparse combination of tensors. We empirically show that learning sparse combination of tensors may not always perform well on real-world problems. Subsequently, we propose a non-sparse latent trace-norm regularizer, that learns a non-sparse combination of tensors. We develop a dual framework for solving the problem. We show a novel characterization of the solution space and then propose two scalable optimization formulations. The problems are shown to lie on a Cartesian product of Riemannian spectrahedron manifolds. We exploit the versatile Riemannian optimization framework for proposing computationally efficient trust region algorithms. The experiments show the good performance of the proposed algorithms on several real-world data sets in different applications. This work has been accepted in NIPS 2018.
- Low-rank optimization with structure: We proposed the first generic framework for learning low-rank matrices with structural constraints for large scale applications. We exploit a variational characterization of the nuclear norm regularization. Our key technical contribution is a saddle point approach for solving such problems, where the low-rank and structural constraints are equivalently decoupled onto two separate factors. This makes our approach simpler and scalable. The proposed algorithms are developed in Riemannian optimization setting. Empirically, we showed results in four different applications: a) matrix completion, b) robust matrix completion, c) Hankel matrix learning, and d) multi-task feature learning. This work was accepted in ICML 2018.
- Sparse positive semi-definite matrices: Learning a sparse positive semi-definite matrix is an important problem in multi-task learning, among other settings. We propose to learn this sparse positive semi-definite matrix efficiently without enforcing the positive semi-definite constraint on the matrix. This makes our solution more amenable to big data settings. Our analysis ensures that we are guaranteed to learn a positive semi-definite output kernel. The algorithm was developed in the stochastic dual-coordinate ascent framework. This work was accepted in NIPS 2015.