# Research

Humans, animals, and other living beings have a natural ability to autonomously learn throughout their lives and quickly adapt to their surroundings, but computers lack such abilities. Our goal is to bridge such gaps between the learning of living-beings and computers. We are machine learning researchers with an expertise in areas such as approximate inference, Bayesian statistics, continuous optimization, information geometry etc. We work on a variety of learning problems, especially those involving supervised, continual, active, federated, online, and reinforcement learning.

Currently, we are developing algorithms which enable computers to autonomously learn to perceive, act, and reason throughout their lives. Our research often brings together ideas from a variety of theoretical and applied fields, such as, mathematical optimization, Bayesian statistics, information geometry, signal processing, and control systems.

A very old and yet very exciting problem in statistics is the definition of a universal estimator $\hat{\theta}$. An estimation procedure that would work all the time. Close your eyes, push the button, it works,... Continue