Projects

Classical approaches to statistical inference and machine learning assume that all data is available at a central location.
However, with recent concerns about privacy has brought about new approaches like federated learning.

If we take the example of learning user preferences (rating movies, for example), it would be helpful if a central server could learn the model without accessing all user data.
In addition to privacy issues, there is also the cost of communicating data from each user to the server.

Here is a cartoon by Google with a non-technical introduction to federated learning.

Distributed estimation also has other applications. Take the example of a wireless sensor network where we have multiple low cost sensors measuring certain parameters, and a central server (sometimes called a fusion center) wants to infer something from these measurements (maybe the average, or some such function of all the measurements).
The link from the sensors to the server will in general be noisy and communication-limited. In such a scenario, how do we design a set-up to perform reliable inference at the server?

See this paper and this one for recent work on distributed mean estimation with limited communication. See this paper for the broad scope.

The problem is rooted deeply in estimation theory, and has applications in signal processing and machine learning.

I am also generally open to work on other problems. See this page for some of my recent research interests.