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.
March 12, 2025
Emtiyaz Khan will give talks at:
- March 20-21, 2025 at the UW-RIKEN AIP Joint Workshop at University of Washington.
- March 24-25, 2025 at the 2nd Vector Institute and RIKEN AIP Joint Symposium on ML and AI at University of Toronto.
Afterwards, our team will visit the Flatiron Institute at New York University.
February 12, 2025
Call for applications for the position of Special Postdoctoral Researcher (SPDR) for FY 2026, the closing date is April 10th, 2025. Please check here for details.
January 22, 2025
Emtiyaz Khan will give talks at:
- February 17-21, 2025 at BIRS workshop on Uncertainty Quantification in Neural Network Models.
- April 27-28, 2025 at ICLR 2025 Workshop on Frontiers in Probabilistic Inference.
December 12, 2024
New preprint on improving multitask finetuning using Bayesian model merging is available on arXiv.
December 06, 2024
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Our team have two accepted workshops at ICLR 2025: “Quantify Uncertainty and Hallucination” and “XAI4Science”.