About

John Baillieul has held professorial appointments in three departments at Boston University: Aerospace/Mechanical Engineering, Electrical and Computer Engineering, and Manufacturing Engineering. He is a past Chairman of Aerospace/Mechanical Engineering and also past Chairman of the Department of Manufacturing Engineering. He has served as Associate Dean for Academic Programs in the Boston University College of Engineering. After receiving his Ph.D. from Harvard University in 1975, he joined the Mathematics Department of Georgetown University. He was the Vinton Hayes Visiting Scientist in Robotics at Harvard University during the academic year 1983-84 and a visiting scientist in the Department of Electrical Engineering at MIT in 1991. Professor Baillieul has been an active member of the IEEE Control Systems Society for many years, serving in various editorial and leadership roles including Editor-in-Chief of the IEEE Transactions on Automatic Control for six years from 1992 through 1998, and holding positions such as Vice-President for Technical Activities, Vice-President for Publications, and President of the IEEE Control Systems Society. At the corporate IEEE level, his service has included roles such as TAB Transactions Chair, member at large of the Publications Services and Products Board, Chair of the PSPB Strategic Planning Committee, Chair of the PSPB Finance Committee, and IEEE Vice President of Publication Services and Products for 2007 and 2008. John Baillieul's research focuses on robotics, the control of mechanical systems, and mathematical system theory. His Ph.D. dissertation at Harvard University under R.W. Brockett was an early work connecting optimal control theory and sub-Riemannian geometry. He developed geometric methods for nonlinear optimal control problems and studied control of nonlinear systems modeled by homogeneous polynomial differential equations, such as the controlled dynamics of a rigid body. His main controllability theorem applied the Hilbert basis theorem to develop a controllability condition verifiable by checking the rank of a finite dimensional operator. In the mid-1980s, he collaborated with M. Levi to develop control theory for rotating elastic systems. More recently, he has written on motion planning and control of kinematically redundant manipulators and investigated problems related to anholonomy in planning motions for robots with elastic joints and energy-storing components. Much of his current research applies dynamical systems theory and classical geometric nonlinear control theory to technological problems including fluid-structure interactions, microelectromechanical dynamics, adaptive optics, and network-mediated control of large scale device arrays. Recent developments have led him to explore the interplay between communications, information theory, and control.

Research topics

• Computer Science
• Artificial Intelligence
• Information Retrieval
• World Wide Web
• Mathematics
• Applied mathematics
• Data science
• Financial economics
• Economics
• Library science
• Computer vision

Selected publications

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

  IEEE Control Systems · 2025-04-01

  articleSenior author
  PublisherDOI

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

  IEEE Control Systems · 2025-04-01

  articleSenior author
  PublisherDOI

• Structure and Control of Biology-Inspired Networks

  IEEE Access · 2025-01-01

  articleOpen accessSenior author

  There is growing interest in developing the theoretical foundations of networked control systems. These efforts aim to illuminate how brain networks support sensory perception, motor control, memory, and other functions essential for survival. The present paper proposes a biologically inspired network model featuring dynamic connections regulated by Hebbian learning. Drawing on tools from graph theory and classical control, we show that our novel nonlinear model exhibits several biologically plausible features, including bounded evolution, stability, and resilience. It also demonstrates a form of structural stability, meaning that perturbations to the model parameters do not alter its essential properties. The proposed network model involves generalized cactus graphs with multiple control input nodes, and it is shown that the properties of the network are resilient to various changes in network topology provided these changes preserve the generalized cactus structure. A particular example described in what follows is an idealized network model of the visual system of a Macaque monkey. The model remains resilient to disruptions that may occur in living organisms, such as those caused by disease or injury. A different model of the same type provides an example of a system that can perform data classification.

  PublisherDOI

• Control of Ensemble Systems with Nilpotent Moments

  2025-12-09

  articleSenior author

  Families of discrete-time nilpotent control systems are studied, and a time optimal control problem is analyzed. Ensemble systems are considered, and nilpotent network structures are introduced to study aggregation of small-scale systems into high dimensional nilpotent systems of the same type. For a class of ensembles constructed in this way, it is shown that corresponding moment system retains nilpotent structure and structural controllability, and these features enable a tractable approach to design an analysis of the ensemble control system. Control sequences defined for the truncated moment system are used to steer the original ensemble.

  PublisherDOI

• Synthesis of Infinite-Dimensional Observers for Infinite-Dimensional Vibrating Systems

  SIAM Journal on Control and Optimization · 2025-05-09 · 1 citations

  articleSenior author
  PublisherDOI

• Stable autonomous visual navigation: an expert prediction approach

  2025-12-09

  article

  The proposed algorithm guarantees safe navigation of unknown visual scenes by autonomous robotic systems equipped with a monocular digital camera. Our methodology exploits synergistic connections between the design of almost globally stable nonlinear control systems and the design of exponentially weighted policies for sequential decision problems under the expert prediction protocol. These connections yield practical tools aiding the design of safe algorithms for autonomous visual navigation. Our algorithms are implemented in Warp and demonstrated in the Isaac Lab simulation environment.

  PublisherDOI

• Structure and Control of Biology-inspired Networks

  arXiv (Cornell University) · 2024-10-09

  preprintOpen accessSenior author

  There is increasing interest in developing the theoretical foundations of networked control systems that illuminate how brain networks function so as to enable sensory perception, control of movement, memory and all the operations that are needed for animals to survive. The present paper proposes a biologically inspired network model featuring dynamic connections regulated by Hebbian learning. Drawing on the machinery of graph theory and classical control we show that our novel nonlinear model exhibits such biologically plausible features as bounded evolution, stability, resilience, and a kind of structural stability -- meaning that perturbations of the model parameters leave the essential properties of the model in tact. The proposed network model involves generalized cactus graphs with multiple control input nodes, and it is shown that the properties of the network are resilient to various changes in network topology provided these changes preserve the generalized cactus structure. A particular example described in what follows is an idealized network model of the visual system of a macaque monkey. The model displays resilience to network disruptions such as might occur in a living organism due to disease or injury. A different model of the same type provides an example of a system that can perform data classification.

  PublisherOA PDFDOI

• Koopman-based Deep Learning for Nonlinear System Estimation

  2024-12-16 · 2 citations

  articleSenior author

  Nonlinear differential equations are encountered as models of fluid flow, spiking neurons, and many other systems of interest in the real world. Common features of these systems are that their behaviors are difficult to describe exactly and invariably unmodeled dynamics present challenges in making precise predictions. In this paper, we present a novel data-driven linear estimator based on Koopman operator theory to extract meaningful finite-dimensional representations of complex non-linear systems. The Koopman model is used together with deep reinforcement networks that learn the optimal stepwise actions to predict future states of nonlinear systems. Our estimator is also adaptive to a diffeomorphic transformation of the estimated nonlinear system, which enables it to compute optimal state estimates without re-learning.

  PublisherDOI

• Koopman-based Deep Learning for Nonlinear System Estimation

  arXiv (Cornell University) · 2024-05-01

  preprintOpen accessSenior author

  Nonlinear differential equations are encountered as models of fluid flow, spiking neurons, and many other systems of interest in the real world. Common features of these systems are that their behaviors are difficult to describe exactly and invariably unmodeled dynamics present challenges in making precise predictions. In this paper, we present a novel data-driven linear estimator based on Koopman operator theory to extract meaningful finite-dimensional representations of complex non-linear systems. The Koopman model is used together with deep reinforcement networks that learn the optimal stepwise actions to predict future states of nonlinear systems. Our estimator is also adaptive to a diffeomorphic transformation of the estimated nonlinear system, which enables it to compute optimal state estimates without re-learning.

  PublisherOA PDFDOI

• Neuromimetic Dynamic Networks with Hebbian Learning

  2024-07-10 · 1 citations

  articleSenior author

  Continuing work on what we have called neuromimetic control system designs is reported. The focus here is on control system models in which the dynamics of networks of neuron-like states are governed by hybrid con-tinuous/discrete, linear/nonlinear models. The models studied support Hebbian-like learning of network structure, and formal analysis grounded in graph theory and classical control allows us to prove that the biological model exhibits boundedness, stability, and structural controllability. The results make contact with previous results involving sym-cactus graphs. Simulations using a 14-node generalized sym-cactus network with two input types validate the model's effectiveness in capturing key neural dynamics.

  PublisherDOI

Recent grants

• Workshop: Engineering Research Communication 2020 - Data and Software Curation and the Relationship to Reproducible Research. November 5,6, 2016, Washington D.C.

  NSF · $100k · 2016–2018

Frequent coauthors

• Thomas Siegert

  University of West Florida

  144 shared

• Gianluca Setti

  King Abdullah University of Science and Technology

  143 shared

• Mary Ward-Callan

  Institute of Electrical and Electronics Engineers

  143 shared

• Dawn Melley

  131 shared

• Konstantinos Karachalios

  Institute of Electrical and Electronics Engineers

  129 shared

• Karen Hawkins

  Xi'an Jiaotong University

  125 shared

• Kevin Lisankie

  Institute of Electrical and Electronics Engineers

  113 shared

• Vaishali Damle

  113 shared

Education

• Ph.D., Electrical Engineering and Computer Science

  Massachusetts Institute of Technology

  1990

• M.S., Electrical Engineering and Computer Science

  Massachusetts Institute of Technology

  1985

• B.S., Electrical Engineering and Computer Science

  University of California, Berkeley

  1983

Awards & honors

• Fellow, International Federation of Automatic Control (IFAC)…
• Fellow, Society for Industrial and Applied Mathematics (SIAM…
• Inaugural recipient of the BU Charles DeLisi Award (2008)
• Fellow, IEEE (1993)