Personalized Machine Learning: Towards Human-centered Machine Intelligence

  • Ognjen Rudovic | MIT Media Lab

Recent developments in AI and Machine Learning (ML) are revolutionizing traditional technologies for health and education by enabling more intelligent therapeutic and learning tools that can automatically perceive and predict user’s behavior (e.g. from videos) or health status from user’s past clinical data. To date, most of these tools still rely on traditional “on-size-fits-all” ML paradigm, rendering generic learning algorithms that, in most cases, are suboptimal on the individual level, mainly because of the large heterogeneity of target population. Furthermore, such approach may provide misleading outcomes as it fails to account for context in which target behaviors/clinical data are being analyzed. This calls for new human-centered machine intelligence enabled by ML algorithms that are tailored to each individual and context under the study. In this talk, I will present the key ideas and applications of Personalized Machine Learning (PML) framework specifically designed to tackle these challenges. I will then show how this framework can be used to devise Personalized Deep Neural Networks for a challenging problem of the robot perception of affect and engagement in autism therapy. Lastly, I will discuss the future research on PML and human-centered ML design, outlining challenges and opportunities.

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Speaker Details

Ognjen (Oggi) Rudovic is a Postdoctoral Fellow in the Affective Computing Group at MIT Media Lab, working on personalized machine learning for social robots and analysis of human data. He received a Bachelor degree in Automatic Control Theory from University of Belgrade, in 2007. In 2009, he received a Master’s degree from Computer Vision Center, Barcelona, and in 2014, he received a Ph.D. from Imperial College London, UK, where he worked on machine-learning and computer vision models for automated analysis of human facial behavior. He is a recipient of Marie Curie Fellowship, one of the most prestigious European Fellowships for rising scientists, and his work has been featured in New Scientist and other media world-wide, and the BBC radio. His current research focus is on developing of novel personalized machine learning models using deep learning, reinforcement learning and Gaussian Process frameworks.

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