PhD, Research Associate,

University of Glasgow

Pattern Recognition, Machine Learning, Social Signal Processing

Sir Alwyn Williams Building, G12 8RZ


+44 141 330 1651



My primary research interests are in the area of machine learning, computer vision, social signal processing and machine perception of social behavior. Within these areas, my work focuses on developing novel strategies to accurately sense and interpret human social signals and social context.

Studies suggested that in human-human interactions more than a half of the messages exchanged are based on the way people move (e.g., posture, facial expression and gestures). However, machines have a poor understanding of these nonverbal cues. During social interactions, non-verbal behaviour conveys a continuous flow of signals about feelings, mental state, personality, and other traits of people.

My current research focuses on bringing deep learning solutions to tease out the structure of the elaborate code behind social interactions (Human-Human & Human-Robot), making it possible for machines to read and write human body language. Next-generation computing needs to include the essence of social intelligence in order to become more effective and possibly to understand a facet of our communication better than we do ourselves.

Within the EPSRC Socially Competent Robots project, we are studying the benefits of social robots for adults with Autism Spectrum Disorders. Social robots can be used to help people with autism by identifying social cues and emotional reactions that they can sometimes struggle to express and interpret themselves. One of the main goals is the development of a Robot Training Buddy, that can help people with ASD to socialize on a more regular basis identifying their mood and reacting appropriately.

Currently, we are working on analyzing and understanding dynamic scenes using deep learning techniques to efficiently detect the pose of multiple people. Long short-term memory networks are used to model socially relevant factors, such as posture and gestures, to infer whether the human is open to an interaction, or they are paying attention to the robot, and other sort of social signals that we use in our daily conversations.



  • Infinite Latent Feature Selection: A Probabilistic Latent Graph-Based Ranking Approach. G. Roffo, S. Melzi, U. Castellani, and A. Vinciarelli. In Conf. IEEE International Conference on Computer Vision (ICCV 2017). [pdf][bibtex]
  • Infinite Feature Selection. G. Roffo, S. Melzi and M. Cristani. In Conf. IEEE International Conference on Computer Vision (ICCV 2015). [pdf][bibtex]
  • Conversationally-inspired stylometric features for authorship attribution in instant messaging. M. Cristani, G. Roffo, C. Segalin, L. Bazzani, A. Vinciarelli, and V. Murino. ACM international conference on Multimedia, (ACMM 2012). [pdf][bibtex]
  • Online Feature Selection for Visual Tracking. G. Roffo, S. Melzi. In Conf. The British Machine Vision Conference (BMVC 2016). [pdf][bibtex]
  • The Visual Object Tracking VOT2016 Challenge Results. Joint Paper: G. Roffo, S. Melzi et Al. In Conf. IEEE European Conference on Computer Vision Workshops (ECCV 2016). [pdf][bibtex]
  • Ranking to Learn: Feature Ranking and Selection via Eigenvector Centrality. G. Roffo, S. Melzi. Springer Book Chapter: New Frontiers in Mining Complex Patterns, 2017. [pdf][bibtex]




nVIDIA GPU Grant 2017

March 15, 2017


NVIDIA GPU grants are intended to enable researchers to begin a new project and/or gain the preliminary results to support a larger proposal to other funding agencies (see GPU Grant Program).

MATLAB Central Coin 2016

April 10, 2017


Matlab FileExchange - Recognition for Outstanding Contributions (2016) in Feature Selection. The Feature Selection Library (FSLib) received more than 3,000 unique downloads in 2016, avg. ~300 downloads pcm (see FSLib online).

VOT 2016 Trackers

January 15, 2017


A library of roughly 40 trackers which is now publicly available from the VOT page: download source code



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