Neuroscience, AI, Machine Learning, Reinforcement Learning, Robotics, Brain-Computer Interfaces

This page is a collection of my blog posts, talks, publications, ideas and projects. They touch on machine learning and computer science, neuroscience and brain-computer interfaces, reinforcement learning and robotics. You can find umbrella categories below and pick those relevant to your interests! skip intro

I am yet to find out how this motley of AI and neuro related interests will come together into a coherent career story, but it seems to me that it is at the intersections of fields and technologies where some of the most interesting questions seem to form and challenges to arise.

It all began in 2010 with almost science fictional appeal of brain-computer interface technology. Our brains emit electrical signals to communicate? Different brain activity results in measurably different signals? This definitely sounded exciting! BCI is a fascinating field that exists on the intersection of Neuroscience and Machine Learning, and that was how I got introduced to both these fields. Over the next few years Machine Learning became my major focus and subject of study, allowing me join in 2013 a newly formed Natural and Artificial Intelligence Lab at the Institute of Computer Science of University of Tartu, Estonia. Here both ML and Neuro knowledge were exactly the right tools to analyze a unique dataset of intracoritcal recordings from more than 100 human subjects, explore the intersection of machine learning and neuroscience, and to work on my thesis on “Understanding Information Processing in Human Brain by Interpreting Machine Learning Models” where I compare biological and artificial models of vision and explore the importance of explainability of machine learning models for gaining neuroscientific insights from models trained on brain data.

My first years at the lab coincided with the “AI spring” of 2012, when Deep Neural Networks have first revolutionized the field of computer vision, and then several other fields of computer science followed. One particular field that was propelled by DNNs was the field of Reinforcement Learning that offers a framework for building intelligent agents that learn continuously from interactions with the surrounding environment. Robotics is one of the applications that naturally fits the Reinforcement Learning framework. This jumpstarted my involvement with a fascinating robotics startup OffWorld, Inc. based in Pasadena, CA. Here I have architected and then, as a Head of Machine Learning Strategy and Research, oversaw a long-term strategy and development of ML and RL methods that are applicable to real-world robots, increasing their autonomy to the level necessary for operating with minimal human oversight.

Sometime during my PhD studies I have moved from Tartu to Sydney, where I had a chance to lead a data science team at Omniscient Neurotechnology and apply machine learning and deep learning methods to unmatched data of human MRI recordings and develop techniques to discover biomarkers of neural conditions in human connectomes.

Shaped by this journey, my core interests now lie on the intersection of artificial intelligence and neuroscience, use of artificial neural networks to help us understand how the brain works, neurotechnolgy in general and brain-computer interfaces in particular. I am heading up an AI advisory company Entropy Reduction that provides services on AI strategy planning, ML-driven business solutions development, data analysis for research purposes, development of machine learning and AI models and data analysis solutions, and more.

To get in touch email me or message me on Twitter.