Projects
Design, analysis and interpretability of large language models Transformers and large language models (LLMs) have had enormous success in recent years. Yet they remain poorly understood, in particular why and how they work. We are trying to answer such questions…
Classical algorithms (exact/ approximation) work with an input which is entirely specified up front. While this offline model is useful for static optimization problems, there are several domains which need algorithms to make decisions with partial/uncertain information which evolves over…
Established:
We design algorithms to address the challenges of scaling ANNS for web and enterprise search and recommendation systems. Our goal is to build systems that serve trillions of points in a streaming setting cost effectively.
Consider the following scenario: Two hospitals, each having sensitive patient data, must compute statistical information about their joint data. Or, one of the hospitals has a pre-trained ML model based on sensitive patient data and another hospital either wants to…
Established:
We explore theoretical properties of simple non-convex optimization methods for problems that feature prominently in several important areas such as recommendation systems, compressive sensing, computer vision etc.