Calendar
Posted December 29, 2025
Last modified January 9, 2026
Informal Analysis Seminar Questions or comments?
12:30 pm – 1:30 pm 233
Moisés Gómez-Solís, Louisiana State University
Laura Kurtz, Louisiana State University
Organizational Meeting
Posted November 24, 2025
Informal Geometry and Topology Seminar Questions or comments?
1:30 pm Lockett Hall 233
Krishnendu Kar, Louisiana State University
Matthew Lemoine, Louisiana State University
Organizational Meeting
Join us for the first meeting of the Spring Semester 2026 Informal Geometry and Topology Seminar to decide which topic we will follow. The Informal Geometry and Topology Seminar is an opportunity for graduate students to get experience presenting information that they learn or have learned. We normally have a topic, paper, book, or subject that we follow and take turns presenting the information we learn, or giving independent talks about our own research. If you have any questions or would like to be added to the email list, please feel free to email Matthew Lemoine (mlemo36@lsu.edu) or Krishnendu Kar (kkar2@lsu.edu).
Posted January 4, 2026
Last modified January 8, 2026
Control and Optimization Seminar Questions or comments?
9:30 am – 10:20 am Zoom (click here to join)
Alberto Bressan, Penn State
Eberly Family Chair Professor
Dynamic Blocking Problems for a Model of Fire Confinement
A classical problem in the Calculus of Variations asks to find a curve with a given length, which encloses a region of maximum area. In this talk I shall discuss the seemingly opposite problem of finding curves enclosing a region with minimum area. Problems of this kind arise naturally in the control of forest fires, where firemen seek to construct a barrier, minimizing the total area of the region burned by the fire. In this model, a key parameter is the speed at which the barrier is constructed. If the construction rate is too slow, the fire cannot be contained. After describing how the fire propagation can be modeled in terms of a PDE, the talk will focus on three main questions: (1) Can the fire be contained within a bounded region? (2) If so, is there an optimal strategy for constructing the barrier, minimizing the total value of the land destroyed by the fire? and (3) How can we find optimal strategies? Problem (1) is still largely open. See https://sites.psu.edu/bressan/2-research/ for a cash prize that has been offered for its solution since 2011.
Posted January 9, 2026
Informal Analysis Seminar Questions or comments?
3:30 pm – 4:30 pm Lockett 233TBD
tbd
Posted December 1, 2025
Last modified January 9, 2026
Control and Optimization Seminar Questions or comments?
9:30 am – 10:20 am Zoom (click here to join)
Jameson Graber, Baylor University
NSF CAREER Awardee
Remarks on Potential Mean Field Games
Mean field games were introduced about 20 years ago to model the limit of N-player differential games as N goes to infinity. There are many applications to economics, finance, social sciences and biology. In many interesting cases the Nash equilibrium turns out to be a critical point for a functional, called the potential, in which case the game itself is called potential. In this case I will present several mathematical results on potential mean field games, which are directly connected to the theory of optimal control of PDE. For related work, see https://doi.org/10.1007/s40687-024-00494-3.
Posted November 15, 2025
Algebra and Number Theory Seminar Questions or comments?
2:00 pm – 3:00 pm Lockett 233 or click here to attend on Zoom
Olivia Beckwith, Tulane University
TBA
TBA
Posted November 22, 2025
Last modified January 6, 2026
Control and Optimization Seminar Questions or comments?
9:30 am – 10:20 am Zoom (click here to join)
Henk van Waarde, University of Groningen
IEEE L-CSS Outstanding Paper and SIAM SIAG/CST Prize Awardee
Data-Driven Stabilization using Prior Knowledge on Stabilizability and Controllability
Direct approaches to data-driven control design map raw data directly into control policies, thereby avoiding the intermediate step of system identification. Such direct methods are beneficial in situations where system modelling is computationally expensive or even impossible due to a lack of rich data. We begin the talk by reviewing existing methods for direct data-driven stabilization. Thereafter, we discuss the inclusion of prior knowledge that, in conjunction with the data, can be used to improve the sample efficiency of data-driven methods. In particular, we study prior knowledge of stabilizability and controllability of the underlying system. In the case of controllability, we prove that the conditions on the data required for stabilization are equivalent to those without the inclusion of prior knowledge. However, in the case of stabilizability as prior knowledge, we show that the conditions on the data are, in general, weaker. We close the talk by discussing experiment design methods. These methods construct suitable inputs for the unknown system, in such a way that the resulting data contain enough information for data-driven stabilization (taking into account the prior knowledge).
Posted December 29, 2025
Colloquium Questions or comments?
3:30 pm Lockett 232
R. Tyrrell Rockafellar, University of Washington
TBA
Posted December 31, 2025
Control and Optimization Seminar Questions or comments?
Time and Location To Be Announced (In Person and Telecast Live on Zoom)
R. Tyrrell Rockafellar, University of Washington
TBA
Posted December 17, 2025
Last modified January 11, 2026
Applied Analysis Seminar Questions or comments?
3:30 pm – 4:30 pm Lockett 223
Tuoc Phan, University of Tennessee–Knoxville
TBA
Posted November 26, 2025
Control and Optimization Seminar Questions or comments?
9:30 am – 10:20 am Zoom (click here to join)
Anthony Bloch, University of Michigan
AMS, IEEE, and SIAM Fellow
TBA
Posted December 7, 2025
Last modified December 28, 2025
Control and Optimization Seminar Questions or comments?
9:30 am – 10:20 am Zoom (click here to join)
Richard Vinter, Imperial College London
IEEE Fellow
Control of Lumped-Distributed Control Systems
Lumped-distributed control systems are collections of interacting sub-systems, some of which have finite dimensional vector state spaces (comprising ‘lumped’ components) and some of which have infinite dimensional vector state spaces (comprising ‘distributed’ components). Lumped-distributed control systems are encountered, for example, in models of thermal or distributed mechanical devices under boundary control, when we take the control actuator dynamics or certain kinds of dynamic loading effects into account. This talk will focus on an important class of (possibly non-linear) lumped-distributed control systems, in which the control action directly affects only the lumped subsystems and the output is a function of the lumped state variables alone. We will give examples of such systems, including a temperature-controlled test bed for measuring semiconductor material properties under changing temperature conditions and robot arms with flexible links. A key observation is an exact representation of the mapping from control inputs to outputs, in terms of a finite dimensional control system with memory. (We call it the reduced system representation.) The reduced system representation can be seen as a time-domain analogue of frequency response descriptions involving the transfer function from input to output. In contrast to frequency response descriptions, the reduced system representation allows non-linear dynamics, hard constraints on controls and outputs, and non-zero initial data. We report recent case studies illustrating the computational advantages of the reduced system representation. We show that, for related output tracking problems, computation methods based on the new representation offer significantly improved tracking and reduction in computation time, as compared with traditional methods, based on the approximation of infinite dimensional state spaces by high dimensional linear subspaces.
Posted November 15, 2025
Algebra and Number Theory Seminar Questions or comments?
2:00 pm – 3:00 pm Lockett 233 or click here to attend on Zoom
Marco Sangiovanni Vincentelli, Columbia University
TBA
TBA
Posted January 8, 2026
Control and Optimization Seminar Questions or comments?
9:30 am – 10:20 am Zoom (click here to join)
Lars Gruene, University of Bayreuth
SIAM Fellow
Can Neural Networks Solve High Dimensional Optimal Feedback Control Problems?
Deep reinforcement learning has established itself as a standard method for solving nonlinear optimal feedback control problems. In this method, the optimal value function (and, in some variants, the optimal feedback law also) is stored using a deep neural network. Hence, the applicability of this approach to high-dimensional problems relies crucially on the network's ability to store a high-dimensional function. It is known that for general high-dimensional functions, neural networks suffer from the same exponential growth of the number of coefficients as traditional grid based methods, the so-called curse of dimensionality. In this talk, we use methods from distributed optimal control to describe optimal control problems in which this problem does not occur.
Posted November 15, 2025
Algebra and Number Theory Seminar Questions or comments?
2:00 pm – 3:00 pm Lockett 233 or click here to attend on Zoom
Kiran Kedlaya, University of California, San Diego
TBA
TBA
Posted December 1, 2025
Control and Optimization Seminar Questions or comments?
9:30 am – 10:20 am Zoom (click here to join)
Khai Nguyen, North Carolina State University
TBA
Posted January 11, 2026
Applied Analysis Seminar Questions or comments?
3:30 pm – 4:30 pm Lockett 223
Zhiyuan Geng, Purdue University
TBA
Posted January 5, 2026
Control and Optimization Seminar Questions or comments?
9:30 am – 10:20 am Zoom (click here to join)
Jonathan How, Massachusetts Institute of Technology
AIAA and IEEE Fellow
TBA
Posted January 2, 2026
Last modified January 8, 2026
Control and Optimization Seminar Questions or comments?
10:30 am – 11:20 am Joint Computational Mathematics and Control and Optimization Seminar to Be Held In Person at Location TBA and on Zoom (click here to join)
Jia-Jie Zhu, KTH Royal Institute of Technology in Stockholm
TBA
Posted December 27, 2025
Control and Optimization Seminar Questions or comments?
9:30 am – 10:20 am Zoom (click here to join)
Aris Daniilidis, Technische Universität Wien
TBA
Posted January 2, 2026
Control and Optimization Seminar Questions or comments?
9:30 am – 10:20 am Zoom (click here to join)
Behçet Açıkmeşe, University of Washington
AIAA and IEEE Fellow
Optimization-Based Design and Control for Next-Generation Aerospace Systems
Next-generation aerospace systems (e.g., asteroid-mining robots, spacecraft swarms, hypersonic vehicles, and urban air mobility) demand autonomy that transcends current limits. These missions require spacecraft to operate safely, efficiently, and decisively in unpredictable environments, where every decision must balance performance, resource constraints, and risk. The core challenge lies in solving complex optimal control problems in real time, while (i) exploiting full system capabilities without violating safety limits, (ii) certifying algorithmic reliability for critical guidance, navigation, and control (GNC) systems, and (iii) co-designing hardware and software subsystems for optimal end-to-end performance. Our solution is optimization-based autonomy. By transforming GNC challenges into structured optimization problems, we achieve provably robust, computationally tractable solutions. This approach has already revolutionized aerospace, e.g., reusable rockets land autonomously via real-time trajectory planning, drones navigate dynamic obstacles, and spacecraft perform precision docking, all powered by algorithms that solve optimization problems with complex physics-based equations and inequalities in milliseconds. Emerging frontiers (such on-orbit satellite servicing, multi-vehicle asteroid exploration, large-scale orbital spacecraft swarms, and global hypersonic transport) push these methods further. Yet barriers remain, e.g., handling non-convex constraints, ensuring solver resilience, large-scale optimization for decision making and co-design, and bridging the gap between theory and flight-ready systems. This talk explores how real-time optimization is rewriting the rules of autonomy, and how researchers can turn these innovations into practice, propelling aerospace engineering into an era where aerospace systems think, adapt, and perform at the edge of the possible.
Posted January 5, 2026
Control and Optimization Seminar Questions or comments?
9:30 am – 10:20 am Zoom (click here to join)
Necmiye Ozay, University of Michigan
IEEE Fellow, and ONR Young Investigator, NASA Early Career Faculty, and NSF CAREER Awardee
TBA