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About

Denis Kleyko is a leading researcher in the emerging field of hyperdimensional computing (HDC) and vector symbolic architectures (VSAs), a bio-inspired approach to artificial intelligence that represents information using high-dimensional random vectors. His work focuses on developing efficient, brain-like methods for pattern recognition, sensorimotor control, and cognitive computing. Kleyko’s major contributions include pioneering the use of VSAs for robust pattern recognition, as detailed in his highly cited 2016 paper on the topic, and advancing the theoretical foundations of HDC in his 2018 thesis, which has become a key reference for researchers exploring computing with hyperdimensional spaces. His impact is evident in the growing adoption of these techniques, with his work cited over 19 times across top venues. Notably, Kleyko has engaged in critical dialogues on neuromorphic active perception, as seen in his 2020 commentary in *Science Robotics*, where he expanded on challenges and opportunities in integrating HDC with neuromorphic sensors. His research bridges neuroscience and machine learning, offering scalable, energy-efficient solutions for real-world AI applications.