Qualifications:
Previous Positions:
My research interests span across different fields, from autonomous robotics and artificial intelligence, to computational modelling and cognitive sciences. I am particularly interested in sensorimotor control in both natural and artificial systems.
Currently, I am mainly dedicated to the following themes:
Chinellato, Eris (2019) The competitive and multi-faceted nature of neural coding in motor imagery: Comment on "Muscleless motor synergies and actions without movements: From motor neuroscience to cognitive robotics" by V. Mohan et al. Physics of life reviews . ISSN 1873-1457
Solari, Fabio and Chessa, Manuela and Chinellato, Eris and Bresciani, Jean-Pierre (2018) Advances in human-computer interactions: methods, algorithms, and applications. Computational Intelligence and Neuroscience , 2018 . ISSN 1687-5265
Varsani, Puja and Moseley, Ralph and Jones, Simon and James-Reynolds, Carl and Chinellato, Eris and Augusto, Juan Carlos (2018) Sensorial computing. In: New Directions in Third Wave Human-Computer Interaction: Volume 1 - Technologies. Filimowicz, Michael and Tzankova, Veronika , eds. Human–Computer Interaction Series . Springer, pp. 265-284. ISBN 9783319733555
Hawes, Nick and Burbridge, Christopher and Jovan, Ferdian and Kunze, Lars and Lacerda, Bruno and Mudrova, Lenka and Young, Jay and Wyatt, Jeremy and Hebesberger, Denise and Kortner, Tobias and Ambrus, Rares and Bore, Nils and Folkesson, John and Jensfelt, Patric and Beyer, Lucas and Hermans, Alexander and Leibe, Bastian and Aldoma, Aitor and Faulhammer, Thomas and Zillich, Michael and Vincze, Markus and Chinellato, Eris and Al-Omari, Muhannad and Duckworth, Paul and Gatsoulis, Yiannis and Hogg, David C. and Cohn, Anthony G. and Dondrup, Christian and Pulido Fentanes, Jaime and Krajnik, Tomas and Santos, Joao M. and Duckett, Tom and Hanheide, Marc (2017) The STRANDS project: long-term autonomy in everyday environments. IEEE Robotics & Automation Magazine , 24 (3). pp. 146-156. ISSN 1070-9932
Chinellato, Eris and Mardia, Kanti V. and Hogg, David C. and Cohn, Anthony G. (2017) An incremental von mises mixture framework for modelling human activity streaming data. In: International Work-Conference on Time Series Analysis (ITISE 2017), 18-20 Sept 2017, Granada, Spain.
