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Within this work we set to advance research in the domain of neural memory systems, focusing particularly on the issues of catastrophic interference, few-shots and continual learning, autoassociative and predictive episodic memorization and retrieval. The progress in these areas is imperative to the realization of true intelligent agents. While the ability to acquire, retain and recall past experiences comes natural to us, it represents a long standing challenge for machine learning systems, namely being able to continually learn from new information coming from non stationary environments without interfering with the past knowledge, associating new observations with the old ones, predicting temporal sequences, transferring old knowledge to new situations or generating fictional memories derived from the accumulated world knowledge.
With an engineering background in electronic and radio communication, I spent more than 20 years in the industry conducting research and development in domains ranging from DSP, peer to peer networking, dynamic systems and control, computer vision and machine learning, primarily for robotics. Over the time I have started several companies where I leveraged my passion for the multidisciplinary software and hardware engineering. Very recently I have been offering my expertise as an independent contractor/consultant. I hold MSc from Kingston University, London in embedded systems and computer vision, and currently pursuing PhD in machine learning for computer vision.