PhD, Research Associate,

University of Glasgow

Pattern Recognition, Machine Learning, Social Signal Processing

Sir Alwyn Williams Building, G12 8RZ


+44 141 330 4933


My primary research interests are in the areas of machine learning, computer vision, and social signal processing. Within these areas, my work 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.

To have a better understanding of the underlying process that generated the data, we are integrating Feature Selection strategies in deep learning frameworks to accurately interpret human social signals and social context.

I have published 19 papers in this area (see List of Publications) and attended, as an author, conferences such as the IEEE International Conference on Computer Vision, the ACM International Conference on Multimedia and ACM CHI Conference on Human Factors in Computing Systems.

My publishing track record shows one or more REF-quality papers (or A* conferences/journals - top 1% of the SCImago SJR index) per annum while being associated with different institutions.


  • Automating the Administration and Analysis of Psychiatric Tests: The Case of Attachment in School-Age Children. G.Roffo, D.-B.Vo, A.Sorrentino, M.Rooksby, M.Tayarani, S. Di Folco, H. Minnis, S.Brewster and A.Vinciarelli. In ACM CHI Conference on Human Factors in Computing Systems, (ACM CHI 2019) [pdf][bibtex]
  • Discrete-time evolution process descriptor for shape analysis and matching. Melzi, S., Ovsjanikov, M., Roffo, G., Cristani, M. and Castellani, U. ACM Transactions on Graphics, (TOG 2018). [pdf][bibtex]
  • 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]
  • Online Feature Selection for Visual Tracking. G. Roffo, S. Melzi. In Conf. The British Machine Vision Conference (BMVC 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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