Matteo Venanzi

In am an applied scientist in Microsoft working for Bing.

I have spent five years working as a researcher at the University Of Southampton. I was part of the ORCHID project focussing on delivering the science of human-agent collectives.

Prior to this, I completed my PhD in Computer Science in Southampton investigating and developing new trust-based data fusion algorithms for aggregating crowdsourced data. My first and second supervisors were Prof. Nick Jennings and Prof. Alex Rogers respectively.

I have also been a two-times intern at Microsoft Research and Bing working on data quality and crowdsourcing. I have also worked as data scientist on crowd-curated recommender systems at Lumi.

Research Interests

I am interested in problems concerning crowdsourcing, human computation, autonomous agents and multi-agent systems.

I am currently investigating data fusion models and intelligent task allocation for reliable crowdsourcing. Related interests also include cognitive trust, reputation and rescue robotics

Professional activities

Workshop organiser:

  • 2015: MassiveMAS: The first workshop of Autonomous Agents and Multi-Agent Systems at Scale co-located with AAMAS 2015
  • 2015: CrowdML: The third workshop on Crowdsourcing and Machine Learning co-located with ICML 2015

Journal and book chapter reviewer:

PC memberships:

  • Conferences: IJCAI 2016, IJCAI 2015, PRIMA 2015, AAMAS 2015, AAMAS 2014, IJCAI 2014
  • Workshops: HAIDM 2016, HAIDM 2015, HAIDM 2014 and HAIDM 2013


  • AAMAS 2013, AAMAS 2012, ATES 2012 and TRUST 2012


July 2016.Paper Time-Sensitive Bayesian Information Aggregation for Crowdsourcing Systems accepted for publication in the Journal of Artificial Intelligence Research in the Special Track on Human Computation and AI

July 2015.Our ActiveCrowd: open-source crowdsourcing toolkit for you to play with many state-of-the-art data aggregation algorithms for crowdsourcing is available on Github. Try it out!

April 2015.Paper Bayesian modelling of community-based multidimensional trust in participatory sensing under data sparsity accepted for publication at IJCAI 2015.