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. Please check out research and publications pages for a more exhaustive overview.
If you are interested in joining us, see the people page and the news below for current opportunities.
News
Vacancies
Please have a look at open positions in our group. See here for more details and join our team.
April 29, 2025
-
Emtiyaz Khan gave an Invited talk at the AABI 2025.
-
“Variational Learning Induces Adaptive Label Smoothing” by Sin-Han Yang et al. received honorable mention at AABI 2025 [ ArXiv ]
April 28, 2025
Emtiyaz Khan gave an Invited talk at ICLR 2025 Workshop on Frontiers in Probabilistic Inference.
April 27, 2025
Show older news...
- Organizing ICLR2025 workshop on “Quantify Uncertainty and Hallucination”
- Organizing ICLR2025 workshop on XAI4Science