Vittorio Murino is full professor @ University of Verona, Dept. of Computer Science, Verona, Italy, and Principal Investigator of the AIGO - AI for Good research line at the Italian Institute of Technology (IIT).
It was
affiliated researcher of the
Pattern Analysis & Computer Vision (PAVIS) research line at IIT, where he has been the
former (and first)
director (Principal Investigator) from 2009 to 2019 (
https://pavis.iit.it/). From 2019 to 2021, he joined the
Huawei Ireland Research Centre, Huawei Technologies (Ireland) Co. Ltd., in Dublin, as
Senior Video Intelligence Expert.
Main expertise on computer vision and pattern recognition, machine learning, image and signal processing, and neuroimaging (data analysis). His main research focus lies now on deep learning, specifically on domain adaptation and generalization, multi-modal deep learning models, learning with privileged information, zero/one/few-shot learning and imbalance data in general, and disentangling representation models. Related applications involve classification and recognition in general, in supervised and unsupervised scenarios, including (fine-grained) activity recognition.
He has a particular interest in multimodal social signal processing approaches for the analysis of human behavior, with main applications related to surveillance and security, human-human and human-machine interaction, ambient intelligence, and retailing. Also major experience in standard industrial applicative domains such as visual inspection and automation.
Concerning the biomedical area, Prof. Murino's main work and interest lie in neuroimaging data analysis, namely Magnetic Resonance Imaging and, in particular, in the study of neural correlates responsible of (social) behavior, with applications in behavioral neurological pathologies (e.g., schizophrenia, autism, etc.) and brain function understanding in general. We deal with these problems using a connectomics approach, specifically by integrating structural and functional connectomics data/information.
He has former experience in underwater vision (acoustical and optical), data fusion and sensory integration with applications on (underwater)
object detection and recognition, object and scene reconstruction.
His current interests lie around the above topics, and can be summarized as the investigation of problems and solutions related to learning with imperfect data with focus on multimodal scenarios. Here, "imperfect" refers to unsupervised or semi-supervised settings, as well as self-supervised ones, where data can be unlabeled, weakly or noisy labeled, data may be too few, biased or class imbalanced. Generative AI, lightweight machine learning, and explainable and trustworthy AI are also topics to consider in this research.
He has former experience in underwater vision (acoustical and optical), data fusion and sensory integration with applications on (underwater) object detection and recognition, object and scene reconstruction.
He is author/co-author of more than 400 papers in computer vision, pattern recognition, machine learning, image processing, and underwater acoustic imaging published in refereed international scientific journals or presented at the major conferences.
IEEE Fellow, the Institute of Electrical and Electronics Engineers (since 2022). Formerly, IEEE Senior Member (since 2002).
IAPR Fellow, International Association of Pattern Recognition (since 2006).
ELLIS Fellow, European Laboratory for Learning and Intelligent Systems (since 2021).
