I am interested in artificial vision in the design of algorithms and systems using novel vision sensors and processors. More specifically I have been devoting my efforts developing event-based algorithms for Dynamic Vision Sensors, a computational framework that prescribes the joint inference of visual quantities, and imaging & processing methods for a near focal plane processor (SCAMP-5): a device embedding a general-purpose analog processing core along a photo-sensitive element in each of its pixels.
Yulia Sandamirskaya is leading the group ``Neuromorphic Cognitive Robots'' that builds biologically inspired computing architectures for robots using spiking and continuous neuronal dynamics. She is interested in long-term memory formation, sequence learning, sensorimotor maps learning, spatial cognition, and navigation, and aims at realising these processes in mixed signal analog/digital neuromorphic chips. She builds neuronal architectures that can perceive, learn, and act autonomously in a closed sensor-motor loop.
Understanding the computational principles behind how brains turn perception into behavior is one of the challenging research questions for the upcoming decades. The NST group at TUM investigates theory, models, and applied robotic implementations of distributed neuronal information processing, to (a) discover key principles by which large networks of neurons operate and (b) implement those in engineered systems to enhance their real-world performance. Current technical applications of our research are robust distributed visual information processing algorithms, efficient long range mapping and navigation for autonomous mobile robots, and massively parallel distributed low-power hardware. Undergoing scientific projects explore spiking neural networks for sensory fusion, graphical information processing for reasoning and control, and parallel distributed neural computation based on local perception, local representation and local computation, to generate globally consistent behavior.
Assistant Professorship of Neuroscientific System Theory,