Copyright © 2024 Roland Bouffanais | All Rights Reserved
University of Geneva
Selected Recent Publications
- A Sequential Deep Learning Algorithm for Sampled Mixed-integer Optimisation Problems
Chamanbaz M & Bouffanais R
Information Science (634), 73-84, 2023. [pdf] [doi] - Effect of Swarm Density on Collective Tracking Performance
Kwa HL, Philippot J & Bouffanais R
Swarm Intelligence (17), 253-281, 2023. [pdf] [doi] - Transition from Simple to Complex Contagion in Collective Decision-Making, N. Horsevad, D. Mateo, R.E. Kooij, A. Barrat & R. Bouffanais, Nature Communications (13), 1442, 2022. [pdf] [doi]
- Complexity Science for Urban Solutions, A. D. S. Srikanth, W. C. B. Chin, T. Schroepfer & R. Bouffanais, Chapter 3: Artificial Intelligence in Urban Planning and Design (Eds. I. As and P. Basu and P. Talwar), Elsevier, Pages 39–58, 2022. [pdf] [doi]
- Adapting the Exploration-Exploitation Balance in Heterogeneous Swarms, H. L. Kwa, V. Babineau, J. Philippot & R. Bouffanais, Artificial Life (28), In Press, 2022. [pdf]
- Cities – Try to Predict Superspreading Hotspots for COVID-19, R. Bouffanais & S. S. Lim, Nature (583), 352-355, 2020. [pdf] [doi]
- ‘Data dregs’ and its Implications for AI Ethics: Revelations From the Pandemic, S. S. Lim & R. Bouffanais, AI & Ethics (2), 595-598, 2022. [pdf] [doi]
- Interplay Between Success and Patterns of Human Collaboration: Case Study of a Thai
Research Institute, A. M. Fiscarelli, M. R. Brust, R. Bouffanais, A. Piyatumrong, G. Danois & P. Bouvry, Scientific Reports (11), 318, 2021. [pdf] [doi] - Optimal Network Topology for Responsive Collective Behavior, D. Mateo, N. Horsevad, V. Hassani, M. Chamanbaz & R. Bouffanais, Science Advances (5), eaau0999, 2019. [pdf] [doi]
- From Senseless Swarms to Smart Mobs: Tuning Networks for Prosocial Behavior, S. S. Lim & R. Bouffanais, IEEE Technology and Society Magazine (38):4, 17-19, 2019. [pdf] [doi]
- Randomized Constraints Consensus for Distributed Robust Mixed-Integer Programming, M. Chamanbaz, G. Notarstefano, F. Sasso & R. Bouffanais, IEEE Trans. Control Network Systems (8), 295-306, 2020. [pdf] [doi]
- Spatial Super-spreaders and Super-susceptibles in Human Movement Networks, W. C. B. Chin & R. Bouffanais, Scientific Reports (10), 18642, 2020. [pdf] [doi]
- Self-Organizing Maps for Storage and Transfer of Knowledge in Reinforcement Learning, T. G. Karimpanal & R. Bouffanais, Adaptive Behavior (27):2, 111-126, 2019. [pdf] [doi]
- From Senseless Swarms to Smart Mobs: Tuning Networks for Prosocial Behavior, Lim SS & Bouffanais R, IEEE Technology and Society Magazine (38):4, 17-19, 2019. [pdf] [doi]
- Are the Different Layers of a Social Network Conveying the Same Information? A. Manivannan, W. Q. Yow, R. Bouffanais & A. Barrat, EPJ Data Science (7), 34, 2018. [pdf] [doi]
About Us
The applied Complexity group (ACG), directed by Prof. Roland Bouffanais (Computer Science & Global Studies Institute, University of Geneva), conducts interdisciplinary research at the intersection of Complexity Science, Multi-Agent Systems, Network Science, Computational Social Science, Data Science, including Artificial Intelligence.
Our research involves a synergistic combination of computational and theoretical developments, with real-life experimental validations.
We foster cross-disciplinary exploration to gain insights into a range of complex systems including social networks, swarm intelligence, complex urban systems, human dynamics, etc. We maintain a constructive and open dialogue between science, society and industry.
Our team members hail from various fields and have expertise in a vast range of disciplines – including computational science, social sciences, machine learning, network science, robotics, and control theory.
A significant part of our funding comes from industry collaborations, with local industry or government agencies, as well as multi-national companies.
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