Distributed Learning
distributed learning
N/A
The position integrates into an attractive environment of existing activities in artificial intelligence such as machine learning for robotics and computer vision, natural language processing, recommender systems, schedulers, virtual and augmented reality, and digital forensics. The candidate should engage in research and teaching in the general area of artificial intelligence. Examples of possible foci include machine learning for pattern recognition, prediction and decision making, data-driven, adaptive, learning and self-optimizing systems, explainable and transparent AI, representation learning; generative models, neuro-symbolic AI, causality, distributed/decentralized learning, environmentally-friendly, sustainable, data-efficient, privacy-preserving AI, neuromorphic computing and hardware aspects, knowledge representations, reasoning, ontologies. Cooperations with research groups at the Department of Computer Science, the Research Areas and in particular the Digital Science Center of the University as well as with business, industry and international research institutions are expected. The candidate should reinforce or complement existing strengths of the Department of Computer Science.
Motor BMIs for probing sensorimotor control and parsing distributed learning
Brain-machine interfaces (BMIs) change how the brain sends and receives information from the environment, opening new ways to probe brain function. For instance, motor BMIs allow us to precisely define and manipulate the sensorimotor loop which has enabled new insights into motor control and learning. In this talk, I’ll first present an example study where sensory-motor loop manipulations in BMI allowed us to probe feed-forward and feedback control mechanisms in ways that are not possible in the natural motor system. This study shed light on sensorimotor processing, and in turn led to state-of-the-art neural interface performance. I’ll then survey recent work that highlights the likelihood that BMIs, much like natural motor learning, engages multiple distributed learning mechanisms that can be carefully interrogated with BMI.
Correcting cortical output: a distributed learning framework for motor adaptation
Bernstein Conference 2024