*modeling, analysis, and control of dynamical processes and strategic interactions in complex networked systems and infrastructures*, with applications in social networks, technological infrastructure, and biological systems. Since critical societal functions are increasingly dependent on complex networks, it is important to acquire a deeper understanding of the relationship between the structure and the dynamics of these systems.

**Data-Driven Analysis and Control of Complex Networks**

*Relevant references from our research group:*

- Data-Driven Network Allocation for Controlling Spreading Processes,
*IEEE Transactions on Network Science and Engineering*, 2015. - On the Complexity of the Minimum Input Selection Problem for Structural Controllability,
*IFAC NecSys, 2015.*

**Spectral Analysis of Complex Socio-Technical Systems (Support: NSF Bigdata)**

*Relevant references from our research group:*

- Stability of Spreading Processes over Time-Varying Networks
*Moment-Based Spectral Analysis of Large-Scale Networks Using Local Structural Information**Structural Analysis of Laplacian Spectral Properties of Large-Scale Networks**Low-Order Spectral Analysis of the Kirchhoff Matrices for a Probabilistic Graph with a Prescribed Expected Degree Sequence**Laplacian Spectral Properties of a Graph from Random Structural Sample*

**Optimal Communication for Fast in-Network Coordination (Support: NSF NeTS)**

*Relevant references from our research group:*

- Spectral Control of Mobile Robot Networks. Proc. 2011 American Control Conference, San Francisco, CA, June 2011, pp. 3245-3250.
- V. M. Preciado, M. M. Zavlanos, A. Jadbabaie, and G. J. Pappas. Distributed Control of the Laplacian Spectral Moments of a Network. Proc. 2010 American Control Conference, Baltimore, MD, June 2010, pp. 4462-4467.
- V.M. Preciado and M.M. Zavlanos,
__Distributed Network Design for Laplacian Eigenvalue Placement__*, IEEE Transactions on Control of Network Systems*, accepted for publication.

**Optimal Resource Allocation for Epidemic Control**

Understanding spreading processes in complex networks and designing control strategies to contain them are relevant problems in many different settings, such as epidemiology and public health. The aim of our research is to control spreading processes in networks by distributing protection resources throughout the nodes. In our study, we consider two types of containment resources: (i) Preventive resources able to protect (or ‘immunize’) nodes against the spreading (such as vaccines in a viral infection process), and (ii) corrective resources able to neutralize the spreading after it has reached a node, such as antidotes in a viral infection. In our framework, we associate a cost to these resources and study the problem of finding the cost-optimal distribution of resources throughout the network to contain the spreading.* [Slides]*

*Relevant references from our research group:*

- Optimal Resource Allocation for Network Protection Against Spreading Processes,
*IEEE Transaction on Control of Network Systems, 2014* - Optimal Vaccine Allocation to Control Epidemic Outbreaks in Arbitrary Networks,
*IEEE Conference on Decision and Control*, 2013. - Analysis and Control of Epidemics: A Survey of Spreading Processes in Complex Networks,
*IEEE Control Systems Magazine,*2016. - Optimal Resource Allocation for Control of Networked Epidemic Models,
*IEEE Transactions on Control of Network Systems*, 2015.

**Resilience of Critical Networked Infrastructure**

*Relevant references from our research group:*

- M.A. Rahimian and V.M. Preciado, Detection and Isolation of Failures in Directed Networks of LTI Systems,
*IEEE Transactions on Control of Network Systems*, vol. 2, no. 2, pp. 183-192, June 2015. - V.M. Preciado, M. Zargham, C. Nowzari, S. Han, M. Ogura, and G.J. Pappas, Bio-Inspired Framework for Allocation of Protection Resources in Cyber-Physical Networks, in
*Principles of Cyber-Physical Systems, Cambridge University Press,*in press. *Detection and Isolation of Link Failures under Consensus Dynamics**Failure Detection and Isolation in Integrator Networks**Worst-Case Scenarios for Greedy, Centrality-Based Network Protection Strategies**Bio-Inspired Framework for Allocation of Protection Resources in Cyber-Physical Networks*

**Network Identification from Dynamic Behavior**

The reconstruction of networks of dynamical systems is an important task in many realms of science and engineering, including biology, physics and finance. The aim of our research is to propose several algorithms to reconstruct the structure of a directed dynamic networks. We have proposed an algorithm to find the Boolean structure of the unknown topology based on the analysis of power spectral properties of the network response when the inputs are wide-sense stationary (WSS) processes of unknown power spectral density (PSD). Apart from recovering the Boolean structure of the network, we have also proposed an algorithm to recover the exact structure of the network (including edge weights) when an eigenvalue-eigenvector pair of the connectivity matrix is known.

*Relevant references from our research group:*

- C. Becker, S. Pequito, D. Bassett, G.J. Pappas, and V.M. Preciado,
__Spectral Inference of Functional Connectivity in Brain Networks__, submitted. - S. Shahrampour and V.M. Preciado, Topology Identification of Directed Dynamical Networks via Power Spectral Analysis,
*IEEE Transactions on Automatic Control*, vol. 60, no. 8, August 2015*.* *Reconstruction of Directed Networks from Consensus Dynamics*

**CURRENT GROUP MEMBERS:**

Postdoctoral Researcher:

- Masaki Ogura (Nov 2014-)
Graduate Students:

- Cassiano Becker (Ph.D. candidate Sep 2013-)
- Ximing Chen (Ph.D. candidate Sep 2013-)
- Mahyar Fazlyab (Ph.D. candidate Sep 2013-)
- Zhen Xiang (M.Sc. candidate Sep 2014-May 2016)
- Christopher Cerezo Falco (Ph.D. candidate 2015-)

Undergraduate Students:

- Trevin Gandhi
- Seth Bartynski

**ALUMNI/PAST MEMBERS:**

Graduate Students:

- Zhengwei Wu (M.Sc. 2012-2013). Thesis: "Spectral Analysis of Spreading Processes in Networks"
- David Sun (M.Sc. 2012-2015). Thesis: "Predicting Mean-Field Theory Accuracy on Real-World Networks"
- Ishaan Nerurkar (undergraduate member)