
Publications
Projects
Overview
We work on both ML-in-the-large and ML-in-the-small. Our large scale ML work includes extreme classification (classifiers with millions of labels), and contextual recommendation systems (which recommend entities to users depending on context). Our work on contextual recommendation systems model situations in email clients or other productivity applications where there is no explicit query, but results are needed depending on implicit context of the user. Our research in ML-in-the small includes EdgeML, where we explore what it takes to run ML on small devices, and Natural Language Processing for low resource and mixed-code languages, where there is huge scarcity for labeled data. We also work on neuro symbolic reasoning, where we combine data driven machine learning methods with symbolic methods such as program synthesis, resulting in systems that are not only adaptive, but also interpretable and verifiable.
Some current projects
Resource-efficient ML for Edge and Endpoint IoT Devices