About

Carolyn L. Beck is a professor at the University of Illinois Urbana-Champaign, affiliated with the Department of Industrial and Enterprise Systems Engineering and the Coordinated Science Laboratory. She holds the Lise Meitner Professorship (Visiting 20%) at Lund University and has served as Associate Head for Undergraduate and Graduate Programs within her department. Her academic background includes a Ph.D. in Electrical Engineering from the California Institute of Technology, an M.S. in Electrical & Computer Engineering from Carnegie Mellon University, and a B.S. in Electrical & Computer Engineering with a Physics minor from California State Polytechnic University. Her research interests encompass control and optimization, epidemic processes over networks, network inference, dynamic network data clustering and aggregation, model approximation and reduction for control and system analysis, and mathematical systems theory with applications in bioengineering, networks, and graphs. She has held various professional roles, including research and development engineer at Hewlett-Packard, and has been involved in numerous academic positions such as visiting associate professorships at the Royal Institute of Technology in Stockholm and Stanford University. Her scholarly work includes significant contributions to modeling and analysis of epidemic processes, control systems, and network inference, with numerous publications in reputable journals.

Research topics

• Artificial Intelligence
• Computer Science
• Machine Learning
• Engineering
• Electrical engineering
• Mathematics
• Control engineering
• Distributed computing
• Mathematical optimization
• Algorithm
• Theoretical computer science
• Data science
• Economics
• Systems engineering

Selected publications

• Peer-to-Peer: Reviewing and Publishing in the IEEE Control Systems Society [President’s Message]

  IEEE Control Systems · 2026-04-01

  article1st authorCorresponding
  PublisherDOI

• Automating Automation: Large Language Models for Control Design [President’s Message]

  IEEE Control Systems · 2026-01-23

  article1st authorCorresponding
  PublisherDOI

• Learning the Fundamentals: Some Thoughts on the Present and Future of Controls [President’s Message]

  IEEE Control Systems · 2025-05-29

  article1st authorCorresponding
  PublisherDOI

• More Optimal [President’s Message]

  IEEE Control Systems · 2025-09-25

  article1st authorCorresponding
  PublisherDOI

• The Roger W. Brockett Control Systems Award [Awards]

  IEEE Control Systems · 2025-04-01

  article1st authorCorresponding
  PublisherDOI

• Maximum Entropy Approach for Water Distribution Network Optimization

  2025-08-25

  articleSenior author

  In this paper we consider a Deterministic Annealing (DA) framework for optimizing reservoir placement and associated water transport costs in Water Distribution Networks (WDNs). We introduce a capacity-constrained formulation to ensure that each junction in the network serves a limited number of demand points, preventing overload and improving system stability. Moreover, we propose update rules for secondary Lagrange multipliers corresponding to capacity inequality constraints. Finally, we extend the framework to utilize real-world pipe networks and validate our algorithm through a case study on the Modena, Italy network. Experimental results demonstrate that our DA-based method achieves lower operational costs compared to previous approach.

  PublisherDOI

• João Manoel Gomes da Silva Jr. [People In Control]

  IEEE Control Systems · 2025-04-01

  article
  PublisherDOI

• The Long Arc of Research [President’s Message]

  IEEE Control Systems · 2025-08-01

  article1st authorCorresponding
  PublisherDOI

• Diversity, Outreach, and Development in the CSS [President’s Message]

  IEEE Control Systems · 2025-04-01

  article1st authorCorresponding
  PublisherDOI

• Online Model Order Reduction of Linear Systems via $(γ,δ)$-Similarity

  ArXiv.org · 2025-04-14

  preprintOpen access

  Model order reduction aims to determine a low-order approximation of high-order models with least possible approximation errors. For application to physical systems, it is crucial that the reduced order model (ROM) is robust to any disturbance that acts on the full order model (FOM) -- in the sense that the output of the ROM remains a good approximation of that of the FOM, even in the presence of such disturbances. In this work, we present a framework for online model order reduction for a class of continuous-time linear systems that ensures this property for any $\mathcal{L}_2$ disturbance. Apart from robustness to disturbances in this sense, the proposed framework also displays other desirable properties for model order reduction: (1) a provable bound on the error defined as the $L_2$ norm of the difference between the output of the ROM and FOM, (2) preservation of stability, (3) compositionality properties and a provable error bound for arbitrary interconnected systems, (4) a provable bound on the output of the FOM when the controller designed for the ROM is used with the FOM, and finally, (5) compatibility with existing approaches such as balanced truncation and moment matching. Property (4) does not require computation of any gap metric and property (5) is beneficial as existing approaches can also be equipped with some of the preceding properties. The theoretical results are corroborated on numerical case studies, including on a building model.

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: Multivariable Modeling and Control of Clinical Pharmacodynamics

  NSF · $206k · 2007–2011

• Computationally tractable graph clustering algorithms for reducing large scale dynamic network models

  NSF · $300k · 2015–2019

• CPS: Breakthrough: Design of Network Dynamics for Strategic Team-Competition

  NSF · $500k · 2016–2019

• Collaborative Research: A comprehensive approach to modeling, learning, analysis and control of epidemic processes over time-varying and multi-layer networks

  NSF · $300k · 2020–2025

Frequent coauthors

• João P. Hespanha

  321 shared

• Thomas Parisini

  321 shared

• Magnus Egerstedt

  318 shared

• Jorge Cortés

  University of California, San Diego

  306 shared

• Andrew G. Alleyne

  University of Minnesota

  306 shared

• Luca Zaccarian

  299 shared

• F Lamnabhi-Lagarrigue

  Imperial College London

  296 shared

• V Balakrishnan

  Institute for Plasma Research

  296 shared

Awards & honors

• Lise Meitner Professorship (Visiting 20%)
• College Awards Policies and Bylaws
• Grainger Engineering for Social Justice
• Grainger CARES
• Collegewide News Media Mentions