Foundations of Data Science and Complex Systems

The mission of this research area is to develop new mathematical and computational theories and methods for the study of Complex Systems, including the development of rigorous theoretical foundations and novel applications in areas such as Artificial Intelligence, Network Science, Epidemiology and Brain Sciences.

  • Mathematical Foundations of Complex Systems
  • Structure and Dynamics of Networks
  • Computational Epidemiology
  • Topological Data Analysis
  • Higher-order Structures
  • Theoretical description of Computation "beyond Turing“
  • Mathematical Epidemiology of Tuberculosis
  • Theoretical Foundations of Brain Sciences
  • Exploring synergies between Machine Learning and Complex Systems
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The AccelNet-MultiNet program was launched as an NSF-supported, joint effort led by the Network Science Institutes of Northeastern University (NetSI) and Indiana University (IUNI) to expand the international community of scientific discovery and training focused around the exploration of multilayer network science. NetSI and IUNI have partnered with 4 key institutions: ISI Foundation, Central European University, UB Institute of Complex Systems, and the Center for Theoretical Physics, to build a joint US-Europe Fellowship program for young scholars and establish collaborative efforts to build consensus on the priorities and direction of the field.

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The global epidemic and mobility model, Gleam, combines real-world data on populations and human mobility with elaborate stochastic models of disease transmission to deliver analytic and forecasting power to address the challenges faced in developing intervention strategies that minimize the impact of potentially devastating epidemics.

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ICARUS - Aviation-driven Data Value Chain for Diversified Global and Local Operations

ICARUS will build a novel data value chain in the aviation-related sectors towards data-driven innovation and collaboration across currently diversified and fragmented industry players, acting as multiplier of the “combined” data value that can be accrued, shared and traded, and rejuvenating the existing, increasingly non-linear models  processes in aviation.

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STAMINA develops an intelligent decision support toolset for pandemic prediction and management and demonstrates its use by practitioners at national and regional levels within and across EU borders.

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Dense and well-connected subgraph detection in dual networks

T. Chen, F. Bonchi, D. Garcia-Soriano, A. Miyauchi, C. Tsourakakis

Proceedings of the 2022 SIAM International Conference on Data Mining (SDM) (2022)

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Other Computational Settings

F. Bonchi, D. Garcia-Soriano, F. Gullo

Synthesis Lectures on Data Mining and Knowledge Discovery book series (SLDMKD) Correlation Clustering, chap. 5 (2022)

Relaxed Formulations

F. Bonchi, D. Garcia-Soriano, F. Gullo

Synthesis Lectures on Data Mining and Knowledge Discovery book series (SLDMKD) Correlation Clustering, chap. 3 (2022)

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