MIND Director named coveted CIFAR Fellow
- Wits University
Professor Benjamin Rosman has been appointed a Fellow of the prestigious CIFAR Learning in Machines & Brains programme.
The appointment of Rosman, the founding Director of the new Wits Machine Learning and Neural Discovery (MIND) Institute, follows the completion of his scholarship as one of only 18 early-career researchers in the world who were chosen for the CIFAR Azrieli Global Scholars for 2022-2024 programme.
.png)
He can now continue with his work as a Fellow in the Learning in Machines & Brains (LMB) programme of the Canadian Institute for Advanced Research (CIFAR), where he joins world experts in the field, including the three ‘Godfathers of AI’: Professors Yoshua Bengio, Yann LeCun, and the 2024 Nobel Prize in Physics laureate, Professor Geoffrey Hinton.
Rosman is a Full Professor in the School of Computer Science and Applied Mathematics at Wits University, where he runs the Robotics, Autonomous Intelligence and Learning (RAIL) Laboratory. In 2024, he became the founding Director of the Wits MIND Institute, which focuses on the fundamental science of intelligence in machines, humans, and animals. He is additionally a co-founder of both the Deep Learning Indaba and Lelapa AI.
“This is truly a profound honour. The CIFAR LMB programme has been a major influence in the development of AI, and particularly deep learning. I’m grateful to be a part of this community,” says Rosman.
“LMB, and CIFAR more generally, have been a source of inspiration for the MIND Institute through their focus on fundamental problems and their embrace of interdisciplinarity.”
About the CIFAR programme in Learning in Machines & Brains
The Canadian Institute for Advanced Research (CIFAR) is a global research organisation exploring the most pressing questions facing science and humanity.
The CIFAR Learning in Machines & Brains programme draws on neuro- and computer science to investigate how brains and artificial systems become intelligent through learning. The programme’s fundamental approach — going back to basic questions rather than focusing on short-term technological advances — has the dual benefit of improving the engineering of intelligent machines and leading to new insights into human intelligence.