This Week in Mathematics


>> Next Week [2026-03-15 - 2026-03-21]	>> Beyond Next Week [2026-03-21+]

<< View Previously Scheduled Events

Current Week [Mar 08, 2026 - Mar 14, 2026]

March
S	M	T	W	R	F	S
1	2	3	4	5	6	7
8	9	10	11	12	13	14
15	16	17	18	19	20	21
22	23	24	25	26	27	28
29	30	31

Today:

Applied and Computational Mathematics [url]
High-field Superconducting Magnets: Science and Applied Mathematics
- Jeseok Bang, Applied Superconductivity Center of the National High Magnetic Field Laboratory
Time: 3:05 Room: 231
Abstract/Desc: Superconductors are promising options for high-field magnets. Among them, rare-earth barium copper oxide (REBCO) materials, a class of high-temperature superconductors, are the most promising option for such a high-field system, as they offer high current-carrying capacity in high fields and robust mechanical properties against high stresses. Indeed, the MagLab at FSU completed a 32 T level superconducting user magnet facility, thus now developing a 40 T level facility using REBCO insert magnets, while on the R&D side, challenging the highest magnetic field every year as breaking world records via a 'Little Big coil' testbed. However, despite such excellence compared to other superconductor counterparts, we are facing several technical issues for stable and reliable high-field superconducting magnets, even in the design stage. The Applied Superconductivity Center (ASC) of the MagLab at FSU aims to address such issues with detailed conductor modeling and numerical simulations. In this talk, I briefly introduce REBCO-coated conductors for high-field superconducting magnets and the key technical issues, present what lessons we learned from high-field REBCO coil tests in recent years, and discuss how science and applied mathematics are used to address such issues.

- Nir Gadish, UPenn
Time: 3:05PM Room: LOV 232

Entries for this week: 9

Monday March 09, 2026

Stochastic Computing Seminar
Model Data Integration in Complex Dynamical Systems: Applications to Materials Synthesis
- Ruth Lopez Fajardo, Florida State University
Time: 3:05PM Room: LOV 232
Abstract/Desc: Many dynamical systems in science and engineering are described by mathematical models, often formulated as systems of ordinary or partial differential equations. These models, however, are only approximations of reality: they rely on idealized assumptions, simplified kinetics, and parameters that are often uncertain or unknown. As a result, accurately describing system behavior over time requires integrating these models with observational data, which is typically indirect, noisy, and incomplete In this talk, I present a general framework for model data integration based on sequential Bayesian inference. I discuss how ensemble and score-based filtering methods address challenges related to high dimensionality, sparse observations, and the interpretation of dynamically inferred parameters. Finally, I illustrate the approach through an application to real-time joint state and parameter estimation in thin-film growth, where in-situ optical measurements are combined with kinetic models to infer the evolving system dynamics.

PhD Dissertation Defense
Long-Only Minimum Variance Optimization: Analytical Foundations, Asymptotic Estimation, and the Enhanced Active-Set Algorithm
- Ololade Sowunmi, FSU
Time: 3:00 pm Room: LOV 204
Abstract/Desc: This dissertation develops a unified theoretical and algorithmic framework for the long-only mini- mum variance (LOMV) portfolio problem under a one-factor covariance structure. The work begins by deriving an explicit closed-form solution to the classical LOMV problem under this structure, providing a complete analytical characterization of the optimal weights and the associated active set. Building on this foundation, the dissertation investigates the impact of factor-loading esti- mation in high dimensions. It establishes that the dispersion bias inherent in the PCA estimator leads to persistent over-diversification, and suggests that the James–Stein eigenvector shrinkage (JSE) estimator produces a strictly smaller asymptotic deviation from the true active-set propor- tion. The analysis further shows that PCA systematically underestimates portfolio risk, whereas the JSE estimator—evaluated through the Variance Forecast Ratio—does not exhibit this bias. The final part of the dissertation introduces the Enhanced Active-Set Algorithm for the One-Factor Model and proves that, under non-degeneracy assump- tions, the algorithm recovers the optimal active set exactly. Numerical experiments demonstrate that the proposed algorithm reliably identifies the true solution and outperforms standard opti- mization routines in both accuracy and stability. Collectively, these results provide new insight into the geometry, estimation sensitivity, and computational structure of long-only minimum variance portfolios, particularly in high-dimensional regimes.

Tuesday March 10, 2026

Applied and Computational Mathematics [url]
High-field Superconducting Magnets: Science and Applied Mathematics
- Jeseok Bang, Applied Superconductivity Center of the National High Magnetic Field Laboratory
Time: 3:05 Room: 231
Abstract/Desc: Superconductors are promising options for high-field magnets. Among them, rare-earth barium copper oxide (REBCO) materials, a class of high-temperature superconductors, are the most promising option for such a high-field system, as they offer high current-carrying capacity in high fields and robust mechanical properties against high stresses. Indeed, the MagLab at FSU completed a 32 T level superconducting user magnet facility, thus now developing a 40 T level facility using REBCO insert magnets, while on the R&D side, challenging the highest magnetic field every year as breaking world records via a 'Little Big coil' testbed. However, despite such excellence compared to other superconductor counterparts, we are facing several technical issues for stable and reliable high-field superconducting magnets, even in the design stage. The Applied Superconductivity Center (ASC) of the MagLab at FSU aims to address such issues with detailed conductor modeling and numerical simulations. In this talk, I briefly introduce REBCO-coated conductors for high-field superconducting magnets and the key technical issues, present what lessons we learned from high-field REBCO coil tests in recent years, and discuss how science and applied mathematics are used to address such issues.

- Nir Gadish, UPenn
Time: 3:05PM Room: LOV 232

Wednesday March 11, 2026

Biomath lab meetings
Synchronization of disease dynamics between groups via social coupling
- Jennifer Wang, FSU
Time: 2:00 Room: LOV102
Abstract/Desc: The spread of infectious disease is strongly influenced by social dynamics. In addition to infection risk, individuals’ vaccination decisions depend on prevailing social behavior: high infection levels and widespread vaccination can increase vaccine uptake, which in turn suppresses infection. This feedback can generate sustained oscillations in disease prevalence and vaccination behavior. In this talk, we study two such populations undergoing the same behavioral--epidemiological limit cycle and introduce weak coupling between them through social influence. We show that coupling leads to synchronization of disease dynamics between the two groups. Moreover, we find that different payoff sensitivity may lead to synchronization or anti-synchronization.

Biomathematics Journals
Operating Principles of Interconnected Feedback Loops Driving Cell Fate Transitions
- Philip Asare, FSU
Time: 5:00 Room: Dirac Library

Thursday March 12, 2026

Financial Math
Cooperation, Competition, and Common Pool Resources in Mean Field Games
- Gökçe Dayanıklı, University of Illinois Urbana-Champaign
Time: 3.05 Room: LOV 231
Abstract/Desc: The tragedy of the commons (TOTC) states that the individual incentives will result in overusing common pool resources (CPRs) which in turn may have detrimental future consequences that affect everyone involved negatively. However, in many real-life situations this does not occur and researchers such as Nobel laureate Elinor Ostrom suggested that mutual restraint by individuals can be the preventing factor. In mean field games (MFGs), since individuals are insignificant and fully non-cooperative, the TOTC is inevitable. This suggests that MFG models involving CPRs must incorporate mixtures of selfishness and altruism to better capture real-world behavior. Motivated by this, we discuss equilibrium notions that blend cooperative and non-cooperative actions. We first introduce mixed individual MFGs and mixed population MFGs, along with modeling aspects of CPRs. The former captures altruistic tendencies at the individual level and the latter represents a population composed of fully cooperative and non-cooperative individuals. For both, we briefly outline equilibrium definitions and their characterization via forward–backward stochastic differential equations. We then present a fisheries-inspired example where the fish stock is the CPR, discussing existence, uniqueness, and experimental results. Finally, we discuss the challenge of learning the altruism levels from observed data.

Friday March 13, 2026

Data Science and Machine Learning Seminar [url]
Decentralized Constrained Sampling via Proximal Stochastic Langevin Dynamics
- Rafiq Islam, FSU
Time: 1:20 Room: Lov 106
Abstract/Desc: We propose Decentralized Proximal Stochastic Gradient Langevin Dynamics (DPSGLD), a novel algorithm for constrained sampling over multi-agent networks. In this framework, agents process local data subsets and communicate only with neighbors to jointly sample from a posterior distribution supported on a compact convex set. We handle constraints using a shared proximal regularization via the Moreau–Yosida envelope, which enables unconstrained stochastic updates that approximate the target Gibbs distribution. We establish non-asymptotic convergence guarantees in the 2-Wasserstein distance for both individual iterates and the network average, explicitly characterizing the effects of network topology, gradient noise, and regularization bias. Experimental results on Bayesian linear and logistic regressions demonstrate that DPSGLD achieves faster posterior concentration and higher predictive accuracy than centralized SGLD. We further show that performance scales positively with increased network connectivity, confirming the algorithm’s efficiency for distributed Bayesian learning.

Special Lecture
ADA Compliance and Accessibility
- Ettore Aldrovandi, FSU
Time: 3:05 Room: 101

Problems? Email webmaster@math.fsu.edu.

Department of Mathematics

This Week in Mathematics