Assistant Professor
Electrical and Computer EngineeringMiami University, Oxford, Ohio
Contact Information
bvanscoy@miamioh.eduRecent Presentations
 IEEE Conference on Decision and Control (CDC), 2020 (slides, video)
 IEEE Conference on Decision and Control (CDC), Nice, 2019 (slides)
 IFAC Workshop on Distributed Estimation and Control in Networked Systems (NecSys), Chicago, 2019 (poster)
 Systems Information Learning Optimization (SILO), Madison, 2019 (slides)
Research Interests
 robust control
 convex optimization
 multiagent systems
Education and Experience
 Postdoc at the Wisconsin Institute for Discovery

University of Wisconsin—Madison, 2017–2020
Supervisor: Laurent Lessard
 PhD in Electrical Engineering and Computer Science

Northwestern University, 2012–2017
Advisor: Randy Freeman  MS in Applied Mathematics

University of Akron, 2007–2012
Advisor: Gerald Young  BS in Applied Mathematics

University of Akron, 2007–2012
 BS in Electrical Engineering

University of Akron, 2007–2012
Forthcoming Publications

B. Van Scoy and L. Lessard, “Systematic analysis of distributed optimization algorithms over jointlyconnected networks,” arXiv:2003.10500, 2020.
[PDF] [BibTeX] [URL] [Slides] [Video] [Code]@inproceedings{vanscoy2020systematic, author = {Van Scoy, Bryan and Lessard, Laurent}, title = {Systematic analysis of distributed optimization algorithms over jointlyconnected networks}, booktitle = {arxiv.org:2003.10500}, year = {2020}, url = {https://arxiv.org/abs/2003.10500} }

A. Sundararajan, B. Van Scoy, and L. Lessard, “Analysis and design of firstorder distributed optimization algorithms over timevarying graphs,” arXiv:1907.05448, 2020.
[PDF] [BibTeX] [URL]@inproceedings{sundararajan2020analysis, author = {Sundararajan, Akhil and Van Scoy, Bryan and Lessard, Laurent}, title = {Analysis and design of firstorder distributed optimization algorithms over timevarying graphs}, booktitle = {arxiv.org:1907.05448}, year = {2020}, url = {https://arxiv.org/abs/1907.05448} }
Journal Publications

S. S. Kia, B. Van Scoy, J. Cortés, R. A. Freeman, K. M. Lynch, and S. Martínez, “Tutorial on dynamic average consensus: The problem, its applications, and the algorithms,” IEEE Control Systems Magazine, vol. 39, no. 3, pp. 40–72, 2019.
[PDF] [BibTeX] [URL]@inproceedings{kia2019tutorial, author = {Kia, Solmaz S. and Van Scoy, Bryan and Cort\'{e}s, J. and Freeman, Randy A. and Lynch, Kevin M. and Mart\'{i}nez, S.}, title = {Tutorial on Dynamic Average Consensus: {T}he problem, Its Applications, and the Algorithms}, journal = {IEEE Control Systems Magazine}, year = {2019}, volume = {39}, number = {3}, pages = {4072}, url = {https://ieeexplore.ieee.org/document/8716798}, doi = {10.1109/MCS.2019.2900783} }

B. Van Scoy, R. A. Freeman, and K. M. Lynch, “The fastest known globally convergent firstorder method for minimizing strongly convex functions,” IEEE Control Systems Letters, vol. 2, no. 1, pp. 49–54, 2018.
[PDF] [BibTeX] [URL] [Slides]@article{vanscoy2017fastest, author = {Van Scoy, Bryan and Freeman, Randy A. and Lynch, Kevin M.}, title = {The fastest known globally convergent firstorder method for minimizing strongly convex functions}, journal = {IEEE Control Systems Letters}, year = {2018}, volume = {2}, number = {1}, pages = {4954}, url = {http://ieeexplore.ieee.org/document/7967721/}, doi = {http://dx.doi.org/10.1109/LCSYS.2017.2722406} }
PeerReviewed Conference Proceedings

B. Van Scoy and L. Lessard, “Integral quadratic constraints: Exact convergence rates and worstcase trajectories,” IEEE Conference on Decision and Control, 2019.
[PDF] [BibTeX] [URL] [Slides]@inproceedings{vanscoy2019iqc, author = {Van Scoy, Bryan and Lessard, Laurent}, title = {Integral quadratic constraints: {E}xact convergence rates and worstcase trajectories}, booktitle = {IEEE Conference on Decision and Control}, year = {2019}, url = {https://arxiv.org/abs/1903.07668} }

B. Van Scoy and L. Lessard, “A distributed optimization algorithm over timevarying graphs with efficient gradient evaluations,” IFAC Workshop on Distributed Estimation and Control in Networked Systems, 2019.
[PDF] [BibTeX] [URL] [Poster]@inproceedings{vanscoy2019distributed, author = {Van Scoy, Bryan and Lessard, Laurent}, title = {A distributed optimization algorithm over timevarying graphs with efficient gradient evaluations}, booktitle = {IFAC Workshop on Distributed Estimation and Control in Networked Systems}, year = {2019}, url = {https://doi.org/10.1016/j.ifacol.2019.12.181} }

A. Sundararajan, B. Van Scoy, and L. Lessard, “A canonical form for firstorder distributed optimization algorithms,” American Control Conference, 2019.
[PDF] [BibTeX] [URL] [Slides]@inproceedings{sundararajan2019canonical, author = {Sundararajan, Akhil and Van Scoy, Bryan and Lessard, Laurent}, title = {A canonical form for firstorder distributed optimization algorithms}, booktitle = {American Control Conference}, year = {2019}, url = {https://doi.org/10.23919/ACC.2019.8814838} }

A. Taylor^{*}, B. Van Scoy^{*}, and L. Lessard^{*}, “Lyapunov functions for firstorder methods: Tight automated convergence guarantees,” International Conference on Machine Learning 2018 (^{*} denotes equal contribution).
[PDF] [BibTeX] [URL] [Slides] [Code]@inproceedings{taylor2018lyapunov, author = {Taylor, Adrien and Van Scoy, Bryan and Lessard, Laurent}, title = {Lyapunov functions for firstorder methods: {T}ight automated convergence guarantees}, booktitle = {International Conference on Machine Learning}, pages = {48974906}, year = {2018}, volume = {80}, series = {Proceedings of Machine Learning Research}, address = {Stockholmsmässan, Stockholm Sweden}, month = {Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v80/taylor18a/taylor18a.pdf}, url = {http://proceedings.mlr.press/v80/taylor18a.html}, }

S. Cyrus, B. Hu, B. Van Scoy, and L. Lessard, “A robust accelerated optimization algorithm for strongly convex functions,” American Control Conference, 2018.
[PDF] [BibTeX] [URL] [Slides]@inproceedings{cyrus2018robust, author = {Cyrus, Saman and Hu, Bin and Van Scoy, Bryan and Lessard, Laurent}, title = {A robust accelerated optimization algorithm for strongly convex functions}, booktitle = {American Control Conference}, year = {2018}, url = {https://ieeexplore.ieee.org/document/8430824/} }

B. Van Scoy, R. A. Freeman, and K. M. Lynch, “Feedforward estimators for the distributed average tracking of bandlimited signals in discrete time with switching graph topology,” IEEE Conference on Decision and Control, 2016.
[PDF] [BibTeX] [URL] [Slides]@inproceedings{vanscoy2016feedforward, author = {Van Scoy, Bryan and Freeman, Randy A. and Lynch, Kevin M.}, title = {Feedforward estimators for the distributed average tracking of bandlimited signals in discrete time with switching graph topology}, booktitle = {IEEE Conference on Decision and Control}, year = {2016}, url = {http://ieeexplore.ieee.org/document/7798918/} }

B. Van Scoy, R. A. Freeman, and K. M. Lynch, “Design of robust dynamic average consensus estimators,” IEEE Conference on Decision and Control, 2015.
[PDF] [BibTeX] [URL]@inproceedings{vanscoy2015design, author = {Van Scoy, Bryan and Freeman, Randy A. and Lynch, Kevin M.}, title = {Design of robust dynamic average consensus estimators}, booktitle = {IEEE Conference on Decision and Control}, year = {2015}, url = {http://ieeexplore.ieee.org/document/7403206/} }

B. Van Scoy, R. A. Freeman, and K. M. Lynch, “Exploiting memory in average consensus,” Allerton Conference on Communication, Control, and Computing, 2015.
[PDF] [BibTeX] [URL]@inproceedings{vanscoy2015exploiting, author = {Van Scoy, Bryan and Freeman, Randy A. and Lynch, Kevin M.}, title = {Exploiting memory in average consensus}, booktitle = {Allerton Conference on Communication, Control, and Computing}, year = {2015}, url = {http://ieeexplore.ieee.org/document/7447013/} }

B. Van Scoy, R. A. Freeman, and K. M. Lynch, “A fast robust nonlinear dynamic average consensus estimator in discrete time,” IFAC Workshop on Distributed Estimation and Control in Networked Systems, 2015.
[PDF] [BibTeX] [URL] [Poster]@inproceedings{vanscoy2015fast, author = {Van Scoy, Bryan and Freeman, Randy A. and Lynch, Kevin M.}, title = {A fast robust nonlinear dynamic average consensus estimator in discrete time}, booktitle = {IFAC Workshop on Distributed Estimation and Control in Networked Systems}, year = {2015}, url = {https://doi.org/10.1016/j.ifacol.2015.10.329} }

B. Van Scoy, R. A. Freeman, and K. M. Lynch, “Optimal worstcase dynamic average consensus,” American Control Conference, 2015.
[PDF] [BibTeX] [URL] [Slides]@inproceedings{vanscoy2015optimal, author = {Van Scoy, Bryan and Freeman, Randy A. and Lynch, Kevin M.}, title = {Optimal worstcase dynamic average consensus}, booktitle = {American Control Conference}, year = {2015}, url = {http://ieeexplore.ieee.org/document/7172171/} }

B. Van Scoy, R. A. Freeman, and K. M. Lynch, “Asymptotic mean ergodicity of average consensus estimators,” American Control Conference, 2014.
[PDF] [BibTeX] [URL] [Slides]@inproceedings{vanscoy2014asymptotic, author = {Van Scoy, Bryan and Freeman, Randy A. and Lynch, Kevin M.}, title = {Asymptotic mean ergodicity of average consensus estimators}, booktitle = {American Control Conference}, year = {2014}, url = {http://ieeexplore.ieee.org/document/6859059/} }
Doctoral Dissertation

B. Van Scoy, Analysis and Design of Algorithms for Dynamic Average Consensus and Convex Optimization. PhD thesis, Northwestern University, 2017.
[PDF] [BibTeX] [URL] [Slides]@phdthesis{vanscoy2017dissertation, author = {Van Scoy, Bryan}, title = {Analysis and Design of Algorithms for Dynamic Average Consensus and Convex Optimization}, journal = {ProQuest Dissertations and Theses}, school = {Northwestern University}, year = {2017}, pages = {227}, url = {http://turing.library.northwestern.edu/login?url=http://search.proquest.com.turing.library.northwestern.edu/docview/1911315018?accountid=12861} }
Master's Thesis

B. Van Scoy, “A Mathematical Model for Hydrogen Production from a Proton Exchange Membrane Photoelectrochemical Cell,” 2012.
[PDF] [BibTeX] [URL] [Slides]@mastersthesis{vanscoy2012thesis, author = {Van Scoy, Bryan}, title = {A Mathematical Model for Hydrogen Production from a Proton Exchange Membrane Photoelectrochemical Cell}, school = {University of Akron}, year = {2012}, url = {http://rave.ohiolink.edu/etdc/view?acc_num=akron1326217817} }
Research Overview
My research uses optimization and control to study largescale systems consisting of many complex interconnected components. I develop systematic tools for characterizing properties of the system, as well as design new algorithms to optimize the overall system performance while being robust to disturbances and uncertainties. [more]
Largescale cyberphysical systems are becoming more prevalent in today’s society. The smart grid, for example, consists of numerous dynamic renewable energy sources such as wind and solar, where controllers regulate the frequency while minimizing consumer costs. Transportation is moving towards a future where autonomous vehicles will coordinate with each other to optimize travel times and improve safety. While building the sensors and actuators for such systems is a difficult task, their ultimate success relies not on our ability to build such systems, but on our ability to control them.
The fields of optimization and control complement each other in the analysis of the algorithms used to control largescale interconnected systems. While optimization algorithms are wellsuited for finding the best solution to complex problems that remain fixed in time, control theory uses feedback to naturally adapt to dynamic uncertainties and disturbances while maintaining stability. Both fields are crucial to the analysis and design of complex interconnected systems that make efficient use of the resources available and are robust to dynamic environments and unknown operating conditions.
The world is becoming more interconnected, and the algorithms we develop must be capable of controlling these highly complex systems. Interconnected systems of the future must adapt to dynamic operating conditions, combat against cyber attacks, be robust to disturbances, guarantee consumer safety, learn from data, and optimize efficiency. My research aims to make such systems a reality.
Firstorder algorithms for convex optimization
Optimization is the cornerstone for solving a multitude of problems in machine learning, finance, control, and engineering. When these problems depend on highdimensional data, firstorder algorithms are typically used due to their low memory and computation requirements. Using tools from robust control, we were able to design the fastest known algorithm for minimizing smooth strongly convex objectives. [more]
Multiagent systems
Coordinating agents can interact with each other and their environment in order to solve complex problems that are difficult or impossible to solve individually. Swarms of flying robots, for example, can explore and map unknown environments, and smart cars can communicate with each other to strategically plan routes and minimize travel times. Using semidefinite programming, we have developed a systematic approach to the analysis of algorithms for distributed optimization, and then used this to design an algorithm with the fastest worstcase convergence rate that is also robust to changes in the communication network. [more]
Robust stability of interconnected systems
Practical systems often contain components that are uncertain, noisy, and/or nonlinear. Examples include saturation and friction in physical systems, fluctuations in wind and solar renewable energy sources in the smart grid, and activation functions in a neural network. Using integral quadratic constraints from robust control, we have developed a systematic characterization of the rate of convergence of such systems, and we used semidefinite programming duality to construct the most destabilizing problem instances. [more]