I joined University of Milano Bicocca as Associate Professor in Computer Science and Artificial Intelligence in October 2020 (more...).
My research interests span from Robotics and Machine Learning to Computational Neuroscience. I want to create adaptive artificial systems with exploratory skills and active perception capabilities flexible enough to tackle complex social environments (physical or virtual). I also study how these skills are realized in living organisms together with the causes and consequences of their failures.
[Open Positions] Topics: Social Networks, HMI, NLP, Cognitive Modelling, Reinforcement Learning
The project aims to develop a Virtual Social Media Companion that educates and supports teenage school students facing the threats of social media such as discrimination and biases as well as hate speech, bullying, fake news and other toxic content.
Building on state-of-the-art NLP & AI methods to provide measurements of sentiment, bias, hatefulness, veracity, polarization, and sensationalism of social media content, we aim at developing computational models of interactions and beliefs dynamics of social media users to support governance and educational strategies autonomously improved using reinforcement learning methods. These models will also be applied to evaluate socially relevant variables, such as trust and inclusion.
Topics: Computational Neuroscience, Social Interaction, BCI, EEG, Virtual Reality
Our contribution in the project focuses on the development of Bayesian (DCM and Active Inference) computational models of multimodal social interaction. These models will be applied to evaluate socially relevant variables, such as trust, presence and inclusion as well as generate optimal stimula in artificially mediated social interactions, e.g. Virtual and Augmented Reality. In particular, the models will cover the role of human chemosignals perception in social interactions. The models will be identified using neurophysiological data (e.g. EEG), peripheral physiological activation (i.e., ECG, RESP, EDA) and behavioural changes (i.e., f-EMG) collected using VR scenarios of increasing complexity.
Topics: Deep Reinforcement Learning, Robotics, Computer Vision, HRI, Multi Agent Systems
Unstructured social environments, e.g. building sites, release an overwhelming amount of information yet behaviorally relevant variables may be not directly accessible, because of occlusions or other sensor limits.
Adaptive control of the sensors is a key solution found by nature to cope with such problems, as shown by the foveal anatomy of the eye and its high mobility and control accuracy.
In this project we are using and developing Machine Learning methodologies, such as Deep Reinforcement Learning, to endow robots with similar active perception capabilities and enable them to collaborate with humans in complex environments.
Active Perception in Artificial and Biological Systems
Social Interaction and Robotics
Social Media and Social Interaction In Artificial Environments
Computational Neuroscience and Psychiatry
Selected publications [more on Google Scholar]
D Ognibene, G Baldassarre, 'Ecological Active Vision: Four Bio-Inspired Principles to Integrate Bottom-Up and Adaptive Top-Down Attention Tested With a Simple Camera-Arm Robot', Autonomous Mental Development, IEEE Transactions on 7 (1), 3-25, 2015
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work email : <skype contact> @ essex.ac.uk
personal email: <skype contact> @ g m a i l . com
University Website: https://www.essex.ac.uk/people/ognib28704/dimitri-ognibene