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.

For more information, see our current publications.


This blog provides a medium for our researchers to present their recent research findings, insights and updates. The posts in the blog are written with a general audience in mind and aim to provide an accessible introduction to our research.

Natural-Gradient Variational Inference 1: The Maths

Bayesian Deep Learning hopes to tackle neural networks’ poorly-calibrated uncertainties by injecting some level of Bayesian thinking. There has been mixed success: progress is difficult as scaling Bayesian methods to such huge models is difficult!... Continue

Universal estimation with Maximum Mean Discrepancy (MMD)

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