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Michail Zavlanos

Michail Zavlanos

Mary Milus Yoh and Harold L. Yoh, Jr. Associate Professor

Michael M. Zavlanos specializes in the area of networked control systems, distributed control and optimization, hybrid control, with applications to robotic, sensor, communication, and biomolecular networks.

His research interests cover a range of topics in the emerging discipline of networked dynamical systems, which studies systems of physical agents interacting via a communication medium in search of joint control principles that determine network behavior.

This includes a focus on robotic and sensor networks, with applications in formation flying, communication maintenance or intruder detection, as well as on biological and social networks, whose structure and operations respectively determine the way in which many diseases are formed and information is spread. Particularly interested in hybrid solution techniques, on the interface of control theory with the discrete science of networks and graphs.

Appointments and Affiliations

  • Mary Milus Yoh and Harold L. Yoh, Jr. Associate Professor
  • Associate Professor in the Department of Mechanical Engineering and Materials Science
  • Associate Professor in the Department of Electrical and Computer Engineering
  • Assistant Professor of Computer Science
  • Associate of the Duke Initiative for Science & Society

Contact Information

  • Office Location: 188 Hudson Hall, Box 90300, Durham, NC 27708
  • Office Phone: (919) 660-5528
  • Email Address: michael.zavlanos@duke.edu
  • Websites:

Education

  • Ph.D. University of Pennsylvania, 2008

Research Interests

Networked control systems, distributed control and optimization, hybrid control, with applications to robotic, sensor, communication, and biomolecular networks.

Awards, Honors, and Distinctions

  • Young Investigator Program Award. Office of Naval Research. 2014
  • Faculty Early Career Development (CAREER) Program. National Science Foundation. 2012
  • Faculty Early Career Development (CAREER) Program. National Science Foundation. 2011

Courses Taught

  • CEE 627: Linear System Theory
  • COMPSCI 391: Independent Study
  • ECE 382: Linear Control Systems
  • ECE 590: Advanced Topics in Electrical and Computer Engineering
  • ME 344L: Control of Dynamic Systems
  • ME 555: Advanced Topics in Mechanical Engineering
  • ME 591: Research Independent Study in Mechanical Engineering or Material Science
  • ME 592: Research Independent Study in Mechanical Engineering or Material Science
  • ME 627: Linear System Theory

In the News

Representative Publications

  • Lee, S; Chatzipanagiotis, N; Zavlanos, MM, Complexity Certification of a Distributed Augmented Lagrangian Method, Ieee Transactions on Automatic Control, vol 63 no. 3 (2018), pp. 827-834 [10.1109/TAC.2017.2747503] [abs].
  • Freundlich, C; Lee, S; Zavlanos, MM, Distributed Active State Estimation With User-Specified Accuracy, Ieee Transactions on Automatic Control, vol 63 no. 2 (2018), pp. 418-433 [10.1109/TAC.2017.2719867] [abs].
  • Calkins, L; Khodayi-Mehr, R; Aquino, W; Zavlanos, M, Stochastic model-based source identification, 2017 Ieee 56th Annual Conference on Decision and Control, Cdc 2017, vol 2018-January (2018), pp. 1272-1277 [10.1109/CDC.2017.8263831] [abs].
  • Lee, S; Chatzipanagiotis, N; Zavlanos, MM, A distributed augmented Lagrangian method for model predictive control, 2017 Ieee 56th Annual Conference on Decision and Control, Cdc 2017, vol 2018-January (2018), pp. 2888-2893 [10.1109/CDC.2017.8264078] [abs].
  • Guo, M; Zavlanos, MM, Temporal task planning in wirelessly connected environments with unknown channel quality, 2017 Ieee 56th Annual Conference on Decision and Control, Cdc 2017, vol 2018-January (2018), pp. 4161-4168 [10.1109/CDC.2017.8264271] [abs].