At UCL I am Deputy Director of the Leverhulme Doctoral Training Programme for the Ecological Study of the Brain. I am also the Director of the UCL Master in Geospatial Analysis.
We won the Test-of-Time Award at ACM SenSys 2018 for our paper “Sensing Meets Mobile Social Networks: The Design, Implementation and Evaluation of the CenceMe Application” (presented at SenSys 2008).
The application process for the 2019 intake of the cross-disciplinary Ecological Brain Doctoral Training Centre funded by Leverhulme Trust is now open. The Centre will fund 5 Ph.D. studentships (stipend plus fee) this year. The focus of the centre is the study of the brain and human behaviour in-the-wild. If you are interested please get in touch.
I am Program co-Chair for ACM UbiComp 2019, which will take place in London in September 2019.
I am also currently involved in the following upcoming scientific events.
- 17th ACM International Conference on Mobile Systems, Applications and Services (ACM MobiSys 2019) [Program Committee Member]
- 4th ACM/IEEE International Conference on Internet of Things Design and Implementation (ACM/IEEE IoTDI 2019)
- 13th International AAAI Conference on Web and Social Media (AAAI ICWSM 2019) [Program Committee Member]
- 24th ACM International Conference on Intelligent User Interfaces (ACM IUI 2019) [Program Committee Member]
- 13th International Conference on Pervasive Computing Technologies for Healthcare [Program Committee Member]
Please consider submitting your work to and attending these conferences and workshops!
The focus of the work of my lab is on the design of next-generation intelligent systems based on computational models of human behaviour and social dynamics. I am interested in both theoretical and systems-oriented aspects. In other words, I enjoy investigating theoretical foundations and models, and then using them in a variety of applications, including the development of real-world systems.
Current areas of interest include:
- Artificial Intelligence/Machine Learning for mobile and ubiquitous systems;
- Reinforcement learning and its applications;
- Computational and mathematical models of human behaviour and social systems (using mobile data, sensor and Internet of Things data, social media and other user-generated content, etc.) and their applications;
- Machine Learning techniques applied to personal data (in particular data from mobile devices and social media) and its privacy implications.
Learning through Probing: a Decentralized Reinforcement Learning Architecture for Social Dilemmas
Nicolas Anastassacos and Mirco Musolesi
Submitted for Publication. Preprint available on arXiv: arXiv:1809.10007
Interpretable Machine Learning for Privacy-Preserving Pervasive Systems
Benjamin Baron and Mirco Musolesi
Submitted for Publication. Preprint available on arXiv: arXiv:1710.08464
Characterizing Animal Movement Patterns Across Different Scales and Habitats using Information Theory
Kehinde Ayodeji Owoeye, Mirco Musolesi and Stephen Hailes
Submitted for Publication. Preprint available on biorXiv: bioRxiv:311241
Intelligent Notification Systems: A Survey of the State of the Art and Research Challenges
Abhinav Mehrotra and Mirco Musolesi
Submitted for Publication. Preprint available on arXiv: arXiv:1711:10171
NotifyMeHere: Intelligent Notification Delivery in Multi-Device Environments
Abhinav Mehrotra, Robert Hendley and Mirco Musolesi
Accepted for Publication in the Proceedings of the 2019 ACM SIGIR Conference on Human Information Interaction and Retrieval (ACM CHIIR’19). Glasgow, Scotland. To Appear. March 2019.
Under and over the Surface: a Comparison of the Use of Leaked Account Credentials in the Dark and Surface Web
Dario Adriano Bermudez Villalva, Jeremiah Onaolapo, Gianluca Stringhini and Mirco Musolesi
In Crime Science. Volume 7. Issue 17. Springer. December 2018.
Using Unsupervised Deep Autoencoders to Automatically Extract Mobility Features for Predicting Depressive States
Abhinav Mehrotra and Mirco Musolesi
In Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT). Volume 2. Issue 3. Presented in the Research Track of 2018 International Joint Conference on Pervasive and Ubiquitous Computing (ACM UbiComp’18). Singapore. October 2018.
Analyzing and Predicting the Spatial Penetration of Airbnb in U.S. Cities
Giovanni Quattrone, Andrew Greatorex, Daniele Quercia, Licia Capra and Mirco Musolesi
In EPJ Data Science. Volume 7. Issue 31. Springer. September 2018.
Precise Time-matching in Chimpanzee Allogrooming does not Occur After a Short Delay
Steve Phelps, Wing Lon Ng, Mirco Musolesi and Yvan I. Russell
In PLOS ONE. Volume 13. Issue 9. e0201810. September 2018.
The Hidden Image of Mobile Apps: Geographic, Demographic, and Cultural Factors in Mobile Usage
Ella Peltonen, Eemil Lagerspetz, Jonatan Hamberg, Abhinav Mehrotra, Mirco Musolesi, Petteri Nurmi and Sasu Tarkoma
In Proceedings of the 20th International ACM Conference on Human-Computer Interaction with Mobile Devices and Services (ACM MobileHCI 2018). Barcelona, Spain. September 2018.
A Comparison of Spatial-based Targeted Disease Containment Strategies using Mobile Phone Data
Stefania Rubrichi, Zbigniew Smoreda and Mirco Musolesi
In EPJ Data Science. Volume 7. Issue 17. Springer. June 2018.
You are your Metadata: Identification and Obfuscation of Social Media Users using Metadata Information
Beatrice Perez, Mirco Musolesi and Gianluca Stringhini
In Proceedings of the 12th International AAAI Conference on Web and Social Media (ICWSM 2018). Palo Alto, CA, USA. June 2018.
Predicting the Temporal Activity Patterns of New Venues
Krittika D’Silva, Anastasios Noulas, Mirco Musolesi, Cecilia Mascolo and Max Sklar
In EPJ Data Science. Volume 7. Issue 13. Springer. May 2018.
Personal Informatics Tools Benefit from Combining Automatic and Manual Data Capture in the Long-Term
Nora Ptakauskaite, Anna Cox, Mirco Musolesi, Abhinav Mehrotra, James Cheshire and Chiara Garattini
In Proceedings of the ACM CHI Workshop on Next Steps Towards Long Term Self Tracking. Colocated with ACM CHI 2018. Montreal, Canada. April 2018.
Towards Deep Learning Models for Psychological State Prediction using Smartphone Data: Challenges and Opportunities
Gatis Mikelsons, Matthew Smith, Abhinav Mehrotra and Mirco Musolesi
In Proceedings of the NIPS Workshop on Machine Learning for Healthcare 2017 (ML4H’17). Colocated with NIPS’17. Long Beach, California, USA. December 2017.
Upcoming office hours:
Thursday 13 December 9-11am
Friday 14 December 9-10am
Last updated: 11 December 2018.