Bio

I joined University of Essex as Lecturer in Computer Science and Artificial Intelligence in October 2017 (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.

Current Projects

[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.

Research Interests

Active Perception in Artificial and Biological Systems

Social Interaction and Robotics

Social Media and Social Interaction In Artificial Environments

Computational Neuroscience and Psychiatry

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University Website: https://www.essex.ac.uk/people/ognib28704/dimitri-ognibene