Fall 2026
Fall 2026
November 20, 2026 Gayashan Jayavilal (UC Math)
TBA (Journal Club)
TBA
November 13, 2026 Chethana Godage (UC Math)
TBA (Journal Club)
TBA
November 6, 2026 Donald French (UC Math)
TBA
TBA
October 23, 2026 Sameh Eisa (UC Engineering)
Control-affine Systems from a Differential Geometric Point of View: Recent Theoretical Developments and New Paradigm in Model-Free Optimization and System Biology
Control-affine systems have been at the center of differential geometric mechanics and control theory. Using their geometric structure, advanced concepts, and system’s properties (for example, reachability and controllability) have advanced significantly for decades. In our recent results, we provided a unified theory that generalized and connected both approximation and averaging of control-affine systems via constructive and sequential framework of Lie-bracket approximations. The new results are not only relevant to enabling stability analysis and design of control applications, but it is paving the way for new paradigm in model-free (zeroth-order), online optimization called for non-quadratic objective functions where exponential convergence can be guaranteed. Additionally, control-affine model-free, online optimization and control enabled us to provide fundamental new radical results in characterizing and emulating major optimized biological phenomena such as soaring birds optimized flight and hovering of flapping insects and hummingbirds, among other possibilities in systems biology.
October 16, 2026 Alex Misiats (Virginia Commonwealth U)
Regularity and asymptotic properties of nonlocal stochastic evolution equations arising in chemical and biomedical models
This talk is devoted to the influence of stochastic perturbations on the long time behavior of nonlocal evolution equations. I will start with a brief overview of the theory, applications, and long time behavior (invariant measures) for stochastic evolution equations driven by the colored noise. My specific focus will be on three of such models: the bidomain model of heart tissue, the aggregation-diffusion equation (Keller-Segel model), and the model of electroporation. The nonlocal character of these equations can be present either in the differential operator (bidomain, electroporation) or in the reaction term (Keller-Segel). Using the fundamental concepts in the area of stochastic analysis, semigroup theory and PDEs, in my talk, I will address the effects of noise on the existence of global vs. finite time blowup solutions (Keller-Segel). I will also address the existence of invariant measures for the bidomain model, as well as discuss the most recent development on electroporation, which is particularly interesting since it allowed us to perform a comprehensive analysis of a non-Lipschitz, not (even locally) monotone nonlinearity, for which neither of the classic results on this subject were applicable. This is the joint project with Prof. Oleksandr Stanzhytskyi (Kyiv) and Prof. Ihsan Topaloglu (VCU), and Irina Petterson (Chalmers University, Sweden)
Joint with the UC Probability Seminar.
October 9, 2026 Junghyun Lee (UC Math)
An underlying bistability sets amplitude and explains temperature compensation in the cyanobacterial circadian clock (Journal Club)
Abstract can be found here: https://doi.org/10.64898/2026.05.03.722537
September 25, 2026 Ian McGahan (UC Math)
Wildlife Movement through Heterogeneous Landscapes
Coordinated Animal movement is a longstanding problem in wildlife ecology. Understanding the effect of habitat heterogeneity in population distribution is of particular concern as this is critical to managing invasive species, understanding the impact of habitat loss on endangered species, and mitigating wildlife disease spread. While advances in tracking technology (e.g., GPS collars, camera traps) produced a wealth of spatio-temporal information about individual movement, linking this individual-scale data to population-scale consequences is, at best, computationally challenging. Partial differential equations (PDE) offer an avenue to link these disparate scales; the link between random walks and diffusion-type equations providing a prime example. However, the separation in spatial and temporal scale between the individual data (usually meters and hours) and population level questions like disease spread (tens to hundreds of kilometers and years) can pose significant analytic and computational barriers. In this talk expect to find an introduction to population-scale wildlife movement modeling, exploration of some of the analytic and computational challenges that arise, an application to white-tailed deer in Southwestern Wisconsin, and a discussion of the implications of this work on studying the spread of chronic wasting disease.
September 11, 2026 Zainab Almutawa (UC Math)
Modeling Beta Cell Network Dynamics: Genetic Algorithm Insights into Coupling Conditions and Critical Desynchronization
Coordinated insulin secretion depends on the electrical coupling of pancreatic beta cells via gap junctions. Variations in cell excitability and disruptions in coupling can compromise this synchrony, potentially contributing to diabetes. In this work, we use a computational beta cell network model to investigate how changes in gap junction connectivity and inherent cellular excitability impact synchronization. Using the Master Stability Function, we derive a necessary coupling condition for network-wide synchronization. A genetic algorithm efficiently navigates the multidimensional parameter space to identify candidate sets that sustain robust synchronization under normal conditions. These same parameter sets lead to complete desynchronization when a critical cell is silenced via chloride current activation. Furthermore, a neural network classifier differentiates between parameter sets leading to synchronized versus desynchronized states, underscoring a switch-like transition when a key cell is inactivated. Statistical analyses highlight the influence of individual parameters on network behavior. These findings provide new insights into beta cell coordination and the underlying mechanisms of insulin secretion dysregulation in diabetes.
TBD Tamara Lorenz (UC Psychology & Engineering)
Behavior Modelling of Humans and Robots
In CAP, the Center for Cognition, Action, and Perception at UC, we study human perception, action, and cognition from an ecological perspective. Our core assumption is that there are no mental representations of the world in our brains, and that the brain neither solely controls the body nor merely processes information. Instead, we view the brain–body–environment as a coupled system in which, over time, relevant information emerges—that is, becomes discoverable. Given our own action capabilities, we may perceive this information as action-relevant and experience the scene before us as offering action opportunities. When modeling the resulting human behavior, we therefore assume that all relevant information is available in the environment and can, in principle, be discovered. The models themselves serve as potential explanations of observable behavior, showing how behavior can be grounded in the environment given the laws of physics and our biology/physiology. Interestingly, individual behavior changes when we interact with others due to mutual adaptation of actions, and this same phenomenon can be observed in interactions with active machines, such as robots. To make robots safe and intuitive to interact with—and to better understand the subtle, nudging influence they can have on our behavior—I observe and dynamically model human action and interaction. In my talk, I will introduce our approach to dynamical systems modeling and present examples of my previous work, along with some early-stage ideas for future projects.