http://www-math.umd.edu/research/seminars.html http://www-math.umd.edu/research/seminars.html Tue, 09 Sep 2025 12:30:00 EDT Where: Kirwan Hall 3206
Speaker: Dr. Jim Yorke (University of Maryland) - https://ipst.umd.edu/people/james-a-yorke
Abstract: • I consider myself a moderately well informed amateur in artificial intelligence. I started
giving talks on artificial intelligence in UMD in 2017.

• I believe one can better understand how people think by discussing how AIs think. Such
understanding might benefit our students. We can’t discuss questions about whether AI
programs can think or have General Intelligence without understanding what these
terms mean for people, without figuring out what we mean when we say people think.
Do people hallucinate as often as LLMs (large language model AIs)?

• I will propose a mini model for testing how people or more specifically
mathematicians/scientists think, and I will describe how AIs process ideas.

I will refer to a paper with B. Hasselblatt entitled Finding Tactics in Proofs- to appear in JJIAM:
https://www.dropbox.com/scl/fi/mvjezmhinpgt9vvyj9cwu/FINDING-TACTICS-IN-PROOFS-B-
HASSELBLATT-AND-JY-JJIAM-AUG-2025.pdf?rlkey=7o6hae0w9isgssuyqm8d1l0d6&dl=0
]]> http://www-math.umd.edu/research/seminars.html Tue, 16 Sep 2025 12:30:00 EDT Where: Kirwan Hall 3206
Speaker: Dr. Salihu Musa (University of Maryland) - https://www-math.umd.edu/people/postdocs-and-visitors/item/1832-ssmusa.html
Abstract: Lyme disease, transmitted by ticks, is endemic in several regions of the United States
(including the Northeast), and the lifecycle of ticks is significantly affected by changes in local climatic variables. In this study, we modeled the dynamics of Lyme disease across the U.S. state of Maryland. We used a mechanistic model, calibrated using case and temperature data, to assess the impact of temperature fluctuations on the geospatial distribution and burden of Lyme disease across Maryland. Our results demonstrate that tick activity and Lyme disease intensity peak when temperature reaches $17.0^{\circ}$C---$20.5^{\circ}$C. We estimate that moderate projected global warming will cause a range expansion of Lyme disease, increasing burden in Central Maryland and extending risk into Western counties, while reducing the

disease burden in Southern and most Eastern counties. High projected warming will cause a westward shift, with new Lyme disease hotspots emerging in Western counties, and reduction of burden in Central, Southern and Eastern regions. Maryland will experience reductions in overall Lyme disease burden under both projected global warming scenarios (with more reductions under the high warming scenario). Disease elimination is feasible using a hybrid strategy, which combines rodents baiting, habitat clearance, and personal protection against tick bites, with moderate coverages.
]]> http://www-math.umd.edu/research/seminars.html Tue, 23 Sep 2025 12:30:00 EDT Where: Kirwan Hall 3206
Speaker: Dr. Kyle Dahlin (Virginia Tech) - https://sites.google.com/view/kyledahlin
Abstract: The risk and intensity of mosquito-borne disease outbreaks are tightly linked to the frequency at which mosquitoes feed on blood, also known as the biting rate. Standard mosquito-borne disease transmission models assume that mosquitoes bite only once per reproductive cycle – an assumption commonly violated in nature. For example, host defensive behaviors or climate factors can increase the occurrence of multiple biting while simultaneously impacting the mosquito gonotrophic cycle duration (GCD), the quantity customarily used to determine biting rates.

We present a framework for incorporating complex mosquito biting behaviors into transmission models, to account for the heterogeneity in and linkages between the biting rate and the multiple biting number. We derive general formulas for the basic offspring number, N0, and basic reproduction number, R0, and introduce specific models arising from empirical, phenomenological, and mechanistic perspectives. Using the gonotrophic cycle duration as a standard quantity to compare these models, we show how assumptions about the biting process strongly affect the relationship between the GCD and R0. This work highlights the importance of behavioral dynamics on mosquito-borne disease transmission while providing a tool for evaluating how individual-level interventions against biting scale up to affect population-level disease risk.
]]> http://www-math.umd.edu/research/seminars.html Tue, 30 Sep 2025 12:30:00 EDT Where: Kirwan Hall 3206
Speaker: Dr. Olivia Chu (Bryn Mawr College) - https://www.brynmawr.edu/inside/people/olivia-j-chu
Abstract: Evolutionary dynamics shape social and biological systems across scales, from the evolution of multicellularity to the emergence of underground fungal symbioses to the formation and maintenance of animal groups and human societies. In these complex adaptive systems, small-scale interactions and associations can lead to emergent, large-scale phenomena. These interactions are often greatly influenced by various forms of heterogeneity, such as personality differences in human populations and variation in altruistic tendencies in animals. In this talk, I will present several models of complex social and biological systems, motivated by real-world phenomena and observations. These models are driven by evolutionary game theory, opinion dynamics frameworks, and agent-based modeling, and employ tools from stochastic processes, differential equations, and dynamical network analysis. I will discuss applications such as the evolution of cooperation, social group formation, the effects of environmental shocks on political opinions and activism, and altruistic tensions in social insect populations.
]]> http://www-math.umd.edu/research/seminars.html Tue, 07 Oct 2025 12:30:00 EDT Where: Kirwan Hall 3206
Speaker: Dr. Bryce Morsky (Florida State University) - https://www.math.fsu.edu/~morsky/
Abstract: Social dynamics are an integral part of the spread of disease affecting contact rates and the adoption of pharmaceutical and non-pharmaceutical interventions. This talk will present behavioural-epidemiological models that feature tipping-point dynamics in which behaviour can undergo rapid changes. Health, economic costs, and social payoffs are all unified into payoff functions that determine changes in behaviour, potentially creating collective action problems. Key findings include: nonlinear responses to key epidemiological parameters, increased public awareness can undermine disease control, and behavioural synchronization. A discussion of optimal public policies in light of these findings will also be discussed.
]]> http://www-math.umd.edu/research/seminars.html Tue, 21 Oct 2025 12:30:00 EDT Where: Kirwan Hall 3206
Speaker: Dr. Dave Levermore (University of Maryland) - https://www.math.umd.edu/~lvrmr/
Abstract: Inflation is a method to select a model from within a large hierarchy of models with the goal of finding the simplest model that economically simulates a small set of objective functions with near optimal fidelity. It begins with an extremely large model, the parameters of which each have an associated intrinsic uncertainty. A hierarchy of smaller models is constructed from a family of reductions of this large model. Starting from a small model in the hierarchy, adjoint sensitivity analysis is used to select a slightly larger model from within the hierarchy that improves the fidelity of a small set of objective functions. This process is repeated until the improved fidelity of these functions becomes comparable to their intrinsic fidelity. This model inflation method will be illustrated on a family of quadratic population models.
]]> http://www-math.umd.edu/research/seminars.html Tue, 28 Oct 2025 12:30:00 EDT Where: Kirwan Hall 3206
Speaker: Farinaz Forouzannia (UT Austin) -https://www.linkedin.com/in/farinaz-forouzannia-a6348996/
Abstract: In this talk I will discuss different mathematical and computational methods to study diseases in cellular level (cancer) and population level. In the first part, I aim to talk about stochastic and deterministic models to investigate the impact of cellular heterogeneity and micro environmental fluctuations on the efficiency of radiotherapy in cancer treatment. For this purpose, a modified Gillespie algorithm for discontinuous time changing rates is applied to explore the impact of plasticity, as well as random demographic factors on tumor control probability. The results show that the random modification of tumor microenvironment influences the efficiency of radiotherapy, leading to an initial increase in tumor control probability, which thereafter drops over time if a tumor is not eradicated entirely.

In the second part of this seminar, I will talk about system level policy models that I developed to support decision making process for Hepatitis B and C care. I will talk about a new back-calculation modeling approach base on Bayesian Markov Chain Monte Carlo (MCMC) algorithm informed by provincial population-level health administrative data to estimate the prevalence and undiagnosed proportion of chronic Hepatitis C infection in Canada. The results can provide evidence to guide decision making about HCV strategies and help meeting WHO elimination target by 2030.
]]> http://www-math.umd.edu/research/seminars.html Tue, 04 Nov 2025 12:30:00 EST Where: Kirwan Hall 3206
Speaker: Dr. Abba Gumel & Dr. Salman Safdar (UMD) - https://math.umd.edu/~agumel/
Abstract: The use of pre-exposure prophylaxis (PrEP), where approved antivirals are administered to uninfected high-risk individuals, is universally regarded as a promising strategy to prevent susceptible high-risk individuals from acquiring HIV infection from their infected partners. A number of antiviral drugs (and their combinations) have been developed and are being used as prophylaxis against the HIV epidemic here in the U.S. and globally. We will first present a risk-structured mathematical model for assessing the population-level impact of PrEP in an MSM (men who have sex with men) population. An extended model, which considers several high-risk populations, will also be presented and used to assess the potential spillover effect, where the administration of PrEP to individuals in one risk group induces a reduction of disease burden in other risk group(s). The central aim is to determine whether the use of the aforementioned strategies can aid the End the HIV Epidemic initiative, aimed at eliminating the disease in the U.S. by 2030.
]]> http://www-math.umd.edu/research/seminars.html Tue, 11 Nov 2025 12:30:00 EST Where: Kirwan Hall 3206
Speaker: Dr. Yihong Du (University of New England) - https://www.une.edu.au/staff-profiles/science-and-technology/ydu
Abstract: Using a reaction-diffusion model with free boundaries in one space dimension for a single population species with density $u(t,x)$ and population range $[g(t), h(t)]$, we demonstrate that the Allee effects can be eliminated if the species maintains its population density at a suitable level at the range boundary by advancing or retreating the fronts. It is proved that with such a strategy at the range edge the species can invade the environment successfully with all admissible initial populations, exhibiting the dynamics of super invaders. Numerical simulations are used to help understand what happens if the population density level at the range boundary is maintained at other levels. If the invading cane toads in Australia used this strategy at the range boundary to become a super invader, then our results may explain why toads near the invading front evolve to have longer legs and run faster.
]]> http://www-math.umd.edu/research/seminars.html Tue, 18 Nov 2025 12:30:00 EST Where: Kirwan Hall 3206
Speaker: Dr. Sabrina Streipert (University of Pittsburgh) - https://www.mathematics.pitt.edu/people/sabrina-streipert
Abstract: Emerging infectious diseases can drive severe host population declines, yet recovery outcomes vary widely across species and communities. To examine the roles of host defense strategies in shaping these dynamics, we developed a mechanistic eco-evolutionary epidemic model that explicitly incorporates an environmental pathogen reservoir and considered heritable variation of disease tolerance in host. In this single-species system, without costs of tolerance, the coexistence of multiple defense classes is possible only when the pathogen ultimately goes extinct, whereas persistent infection selects for maximal tolerance. Introducing a reproductive cost removes such disease-free coexistence and allows for endemic equilibria with multiple strategies. Motivated by discrepancies between these analytical predictions and real-world observations, we extend the model to multi-species communities with habitat overlap and overlap-dependent competition. This reveals that resistant hosts suffer larger population declines and slower recovery under greater overlap, although communities dominated by resistant species rebound faster and more evenly because tolerant hosts promote pathogen persistence. Competition linked to habitat overlap reduces species-level declines, but increases unevenness in community recovery. Our results, supported by classical reproduction number analyses yet driven by transient dynamics, demonstrate how host defense strategies, habitat structure, and inter-specific interactions jointly determine post-outbreak resilience across biological scales.
]]> http://www-math.umd.edu/research/seminars.html Tue, 25 Nov 2025 12:30:00 EST Where: Kirwan Hall 3206
Speaker: Dr. Zhuolin Qu (University of Texas at San Antonio) - https://zhuolinqu.github.io/
Abstract: Density dependence is a key ecological mechanism regulating population growth, yet its influence can vary substantially depending on which life stage it affects. Understanding these effects is critical for evaluating the success of Wolbachia-based mosquito population suppression strategies. We develop an ordinary differential equation model of Aedes aegypti dynamics that incorporates density-dependent regulation in development and mortality processes. Through analytical and numerical investigations, we show that when density dependence acts primarily on juvenile development, release-driven population suppression can produce counterintuitive, non-monotonic responses—where intermediate release rates transiently increase the uninfected adult population. In contrast, when density dependence acts only on juvenile mortality, this phenomenon does not occur. These findings highlight the importance of identifying which life-history processes are density-regulated when designing Wolbachia-based control programs.

Joint work with Alyssa Petroski (University of the Sciences), Lauren M. Childs (Virginia Tech), and Michael A. Robert (Virginia Tech).
]]> http://www-math.umd.edu/research/seminars.html Tue, 02 Dec 2025 12:30:00 EST Where: Kirwan Hall 3206
Speaker: Dr. Joan Ponce (Arizona State University) - https://search.asu.edu/profile/4442225
Abstract: Host genetic structure can significantly alter disease transmission dynamics and long-term disease outcomes. Past work by Beck, Keener, Hoppensteadt, Feng, and others has shown that when pathogen transmission interacts with evolving host traits—such as susceptibility, recovery, or disease-induced mortality—the resulting coupled system can exhibit novel dynamics. These models demonstrated that genetic composition within a host population can shift during an epidemic, and conversely, infection pressures can reshuffle genetic frequencies, producing true feedback between genes and epidemics.

In this talk, I will discuss a specific example of this phenomenon, focusing on the interaction between Plasmodium vivax and the Duffy antigen, a host genetic trait that confers partial protection against infection.
]]> http://www-math.umd.edu/research/seminars.html Tue, 09 Dec 2025 12:30:00 EST Where: Kirwan Hall 3206
Speaker: Dr. Jun-ping Shi (College of William & Mary) - https://jxshix.people.wm.edu/
Abstract: Animal populations often self-organize into territorial structure from movements and interactions of individual animals. Spatial memory is one of cognitive processes that may affect the movement and navigation of the animals. We will review several mathematical approaches of animal spatial movements: (i) reaction-diffusion-advection model with time-delayed memory-based movement; and (ii) reaction-diffusion-advection model with a non-local advection term driven by a cognitive map representing memory of past animal locations embedded in the environment. The well-posedness of models and bifurcation of spatiotemporal patterns will be discussed.
]]> http://www-math.umd.edu/research/seminars.html Tue, 16 Dec 2025 12:30:00 EST Where: Kirwan Hall 3206
Speaker: Dr. Binod Pant (Network Science Institute, Northeastern University) - https://www.networkscienceinstitute.org/people/binod-pant
Abstract: Uncertainty quantification (UQ) is mature in fields such as climate science and engineering, yet rigorous UQ remains understudied and underutilized in mathematical epidemiology. Structural identifiability analysis—which examines whether model parameters can, in principle, be uniquely determined from ideal observations (which could be thought of as noise-free, continuous data for all time)—represents a natural first step toward UQ. However, most studies focus exclusively on parameter identifiability. I will present theoretical identifiability results for the basic reproduction number, demonstrating that structurally unidentifiable models can still yield identifiable quantities of epidemiological interest. This reframes the central question from “Is the model-observation pairing structurally identifiable?” to “Are the quantities that matter for the decision at hand structurally identifiable?” I will also present a rigorous methodology showing how adding even a single data point from complementary data streams can resolve identifiability issues.

Of course, real data are discrete, noisy, and model-data mismatch always persists. I will discuss how working with synthetic noisy data can serve as a bridge between structural identifiability and real data. Finally, through two examples, I will demonstrate the consequences of model-data mismatch: the impact of ignoring undetected cases and the impact of neglecting human behavioral responses during epidemics.
]]> http://www-math.umd.edu/research/seminars.html Tue, 03 Feb 2026 12:30:00 EST Where: Kirwan Hall 3206
Speaker: Dr. Bill Fagan (University of Maryland) - https://science.umd.edu/biology/faganlab/
Abstract: I will discuss results and ideas at the interface of stochastic processes, statistical modeling, and the movement ecology of wild animals. Recently published results demonstrate broad differences in the tendency for carnivores to utilize heavily traveled routeways within their home ranges and ways in which range-residency can be incorporated in dynamic models of population growth. New ideas focus on opportunities for investigating learning from animal movement data and the potential influences of vision on correlated movement.
]]> http://www-math.umd.edu/research/seminars.html Tue, 10 Feb 2026 12:30:00 EST Where: Kirwan Hall 3206
Speaker: Dr. Jackson Champer (Peking University) - https://jchamper.github.io/
Abstract: Gene drive alleles bias inheritance in the favor, allowing them to quickly spread throughout a population. They could combat disease by rapidly spreading a cargo gene that blocks pathogen transmission, or they could directly suppress vector populations. We have developed efficient systems in Anopheles stephensi for both population suppression and confined population modification with reduced resistance allele formation. Yet, questions remain about the performance of gene drives after release in real-world populations. To address these, we developed several frameworks for computational modeling. In an individual-based framework, we predict that Anopheles suppression drives may still not succeed in spatially structured natural populations due to the "chasing" phenomenon that causes long-term persistence of both drive and wild-type alleles. Yet, even without mosquito elimination, local malaria elimination can still be successful. We also used reaction-diffusion models and large-scale hex-based Culex models of Hainan island to predict optimal drive releases of spatially confined drive systems, when larger release sizes make for more challenging deployment. Finally, we assessed new variants of self-limiting “temporary” suppression gene drive systems, which have similar dynamics to mature SIT and RIDL methods but substantially more power. Thus, despite unexpected complexity, gene drive remains a flexible and effective method to protect against vector-borne diseases.
]]> http://www-math.umd.edu/research/seminars.html Tue, 17 Feb 2026 12:30:00 EST Where: Kirwan Hall 3206
Speaker: Dr. Naghmeh Akhavan (University of Michigan) -
Abstract: Mammalian sleep consists of repeated ultradian cycles between non-REM (NREM) and rapid-eye-movement (REM) sleep. Although the timing of these cycles is not fully understood, a key contributor is thought to be REM pressure, a drive for REM sleep that accumulates between REM episodes. Building on prior work in mice, we introduced a REM propensity measure that quantifies the probability of entering REM sleep as a function of accumulated NREM sleep. In mice, REM propensity at REM onset was positively associated with both REM bout duration and the likelihood of short, sequential REM cycles. Here, we extend this framework to human and rat sleep. We show that ultradian cycles in all three species can be classified as either short sequential or longer single REM cycles, and that REM propensity exhibits a conserved dependence on time spent in NREM sleep, rising to a peak and then declining. Across species, higher REM propensity at REM onset predicts longer REM bouts, suggesting a shared role of NREM accumulation in shaping REM duration. Finally, analysis of human sleep reveals systematic variation in the occurrence of sequential and single REM cycles across the sleep episode.
]]> http://www-math.umd.edu/research/seminars.html Tue, 24 Feb 2026 12:30:00 EST Where: Kirwan Hall 3206
Speaker: Dr. Arnaja Mitra (University of Maryland) -
Abstract: Malaria, a parasitic disease spread to humans via effective bite by an infectious adult female Anopheles mosquito, continues to exude a major burden in endemic areas (causing in excess of 600,000 deaths annually, mostly in children under the age of five). Much progress was made over the last two or three decades in the fight against malaria, largely due to the heavy and large-scale use of chemical insecticides (particularly in the form of long-lasting insecticidal nets and indoor residual spraying) to kill the malaria mosquito, promoting a renewed quest for malaria eradication. Unfortunately, such heavy use has also resulted in widespread Anopheles resistance to all the main chemical insecticides used in vector control, posing challenges to the eradication objective. New anti-malaria vaccines have been approved recently and are being deployed in a number of countries in sub-Saharan Africa. In this talk, I will present a new mathematical model, in the form of a system of delayed-differential equations, for assessing the population-level impact of one of the approved vaccines (R21/Matrix-M vaccine) in curtailing the disease burden in the targeted (vaccinated) population.
]]> http://www-math.umd.edu/research/seminars.html Tue, 03 Mar 2026 12:30:00 EST Where: Kirwan Hall 3206
Speaker: Alexandria Volkening (Department of Mathematics, Purdue University) - https://www.alexandriavolkening.com/
Abstract: Many natural and social phenomena involve individual agents coming together to create group dynamics, whether the agents are drivers in a traffic jam, cells in a developing tissue, or locusts in a swarm. Here I will focus on the example of pattern formation in zebrafish, which are named for their dark and light stripes. Mutant zebrafish, on the other hand, feature different skin patterns, including spots and labyrinth curves. All of these patterns form as the fish grow due to the interactions of tens of thousands of pigment cells. The longterm motivation for my work is to help identify the alterations to cell interactions that lead to mutant patterns. Toward this goal, I will overview our work building agent-based and continuum models to simulate pattern formation and make experimentally testable predictions. Because stochastic, microscopic models are not analytically tractable using traditional techniques, I will also describe how we are applying topological data analysis and approximate Bayesian inference to quantify structure in messy, cell-based patterns and identify rules of behavior from data.
]]> http://www-math.umd.edu/research/seminars.html Tue, 10 Mar 2026 12:30:00 EDT Where: Kirwan Hall 3206
Speaker: Dr. Woldegebriel Assefa Woldegerima (York University) - https://www.yorku.ca/professor/waw/
Abstract: The integration of mechanistic modeling and machine learning has the potential to revolutionize the way we understand complex biological systems. Particularly, the development of Informed Neural Networks (INNs), a class of hybrid models that embed domain-specific knowledge, such as differential equations, into the neural network architecture, has attracted many researchers recently. We formalize Epidemiology-informed neural networks (EINNs) as physics-informed neural networks (PINNs) adapted to epidemiological constraints. EINNs represent a burgeoning intersection of dynamical systems theory, machine learning, and infectious disease modeling, offering powerful tools for predicting and mitigating epidemic outbreaks. EINNs incorporate domain-specific knowledge from disease dynamics (e.g., differential equation models, compartmental models like SIR, vector-host interactions, etc.) into their architecture, loss functions, or training process. The loss function that is minimized during training is the combined loss of the data and the DE residuals. This method helps to improve learning, prediction accuracy, interpretability, and parameter estimation, particularly in scenarios where data is sparse or noisy.

In this talk, I will introduce EINNS and describe how epidemiological structure can be
incorporated through constrained loss functionals, embedded differential operators, and
parameterized transmission mechanisms. I will also briefly summarize related literature on to questions of approximation theory: under what conditions can neural networks consistently recover unknown transmission rates, time-varying contact functions, or latent state trajectories from partial and noisy observations? I will discuss recent results on convergence, regularization, and the interplay between model misspecification and overparameterization.

I will then present some results from our recent study that we trained an EINN on synthetic data derived from an SI-SIR model designed for Avian influenza and show the model’s accuracy in predicting extinction and persistence conditions. In the method, a twelve-layer hidden model was constructed with sixty-four neurons per layer, and the ReLU activation function was used. The network is trained to predict the time evolution of five state variables for birds and humans over 50,000 epochs. The overall loss is minimized to 0.000006, characterized by a combination of data and physics losses, enabling the EINN to follow the differential equations describing the disease progression.
]]> http://www-math.umd.edu/research/seminars.html Tue, 24 Mar 2026 12:30:00 EDT Where: Kirwan Hall 3206
Speaker: Dr. Nao Yamamoto (New York University) - https://nao-yamamoto.com/profile.html
Abstract: Many contemporary public health challenges involve substantial heterogeneity in individual characteristics, contact structures, comorbidities, and social determinants of health. Addressing these interacting layers requires modeling frameworks capable of representing individuals, diseases, and interventions within a unified and dynamically evolving system.

In this talk, I will introduce the Model of Inter-Generational Health, Transmission, and Interventions (MIGHTI), a modular multi-disease agent-based simulation platform designed to jointly model infectious diseases, non-communicable diseases, and social determinants of health within a single computational framework.

Using Eswatini as a case study, I will demonstrate how MIGHTI enables evaluation of life expectancy gains, cause-specific mortality attribution, and policy-relevant intervention scenarios. The goal is to illustrate how an integrated, multi-disease modeling platform can support quantitative analysis of complex, multi-layered population health systems.
]]> http://www-math.umd.edu/research/seminars.html Tue, 31 Mar 2026 12:30:00 EDT Where: Kirwan Hall 3206
Speaker: Dr. Pinar Baydemir Dastan (University of Maryland) -
Abstract: Phytoplankton populations are the cornerstone of marine ecosystems, driving atmospheric oxygen production and climate regulation. However, these populations are susceptible to critical density thresholds, where falling below a specific level, the Allee critical value, can precipitate irreversible ecosystem collapse. Addressing this vulnerability, we extend standard oxygen–phytoplankton interaction models by explicitly integrating the Allee effect into both continuous-time and discrete-time frameworks. Through stability and bifurcation analysis, we identify the Allee equilibrium as a strict separatrix; trajectories initiating below this threshold inevitably crash to extinction, while those above possess the potential for coexistence. Beyond classical asymptotic stability, we employ the Resilience Index to quantify the probability of persistence within the basin of attraction and utilize the First Passage Time function to detect signals of critical slowing down. Our simulations uncover a 'ghost of attractor' phenomenon, revealing that the system may linger dangerously in a depletion zone before collapsing. A pivotal insight from this analysis is that systems can be transiently vulnerable to collapse even if they theoretically remain on a chaotic attractor. To further broaden the ecological scope, we introduce an extended model framework that incorporates light limitation and photoinhibition via a Haldane-type growth function. By modeling light attenuation through self-shading, this extension provides a biologically consistent mechanism for density-dependent growth suppression, establishing a robust foundation for future integration of temperature dependence and global warming effects.
]]> http://www-math.umd.edu/research/seminars.html Tue, 07 Apr 2026 12:30:00 EDT Where: Kirwan Hall 3206
Speaker: Dr. Tufail Mohammad Malik (Merck Research Laboratories) -
Abstract: Serotype replacement - an increase of non‑vaccine serotypes after vaccination - is usually attributed to explicit competition in transmission models. I will show that even without such competition terms, standard assumptions on how many serotypes a host can carry (multi‑colonization) can generate strong, competition‑like effects. Using SIS‑type models with two and three serotypes, I derive explicit coexistence formulas where each serotype’s equilibrium prevalence equals its single‑serotype endemic level minus a term reflecting multi‑colonization constraints. When triple colonization is allowed, serotypes decouple and a monovalent vaccine against one serotype does not affect others. When hosts can carry at most two serotypes, double‑colonized hosts become structurally unavailable to the remaining serotype, creating “implicit competition” and potential replacement.
]]> http://www-math.umd.edu/research/seminars.html Tue, 14 Apr 2026 12:30:00 EDT Where: Kirwan Hall 3206
Speaker: Dr. Daniel Cooney (University of Illinois Urbana-Champaign) - https://publish.illinois.edu/danielbcooney/
Abstract: Social dilemmas featuring tension between the individual incentive to cheat and a collective goal to maintain cooperative behavior arise across a range of natural and social systems, from the origins of multicellular life to the sustainable manage of shared natural resources. Evolutionary game theory provides a helpful analytical framework for describing this conflict between individual and collective interests, exploring mechanisms that can help the emergence of cooperative behaviors. In this talk, we discuss several PDE models for evolutionary games featuring diffusion of individuals and directed motion towards either increasing payoff or improved environmental quality. We show that biased motion of cooperators can promote the formation of spatial patterns featuring regions with greater population density and increased average payoffs and environmental quality in regions in which cooperators have aggregated. However, by measuring the average payoff of the population or the average level of environmental quality across the population, we see that these pattern-forming mechanisms can actually decrease the overall success of the population, relative to the equilibrium outcome in the absence of spatial motion. This suggests that payoff-driven and environmental-driven motion can produce a kind of spatial social dilemma, in which biased motions towards more beneficial regions can produce emergent patterns featuring a worse overall environment for the population.
]]> http://www-math.umd.edu/research/seminars.html Tue, 21 Apr 2026 12:30:00 EDT Where: Kirwan Hall 3206
Speaker: Amy Patterson (University of Maryland) -
Abstract: Which species, communities, and ecosystems will persist under intensifying global change, and which will unravel? I will discuss work that develops ecological and evolutionary theory, individual-based models, and statistical approaches to understand and predict (1) how eco-evolutionary feedbacks shape species invasions and range shifts, (2) how ecological communities adapt or collapse in the face of change, and (3) how the many dimensions of environmental change reshape ecological populations and communities. Mathematical ecology techniques are vital for anticipating and managing ecological change, now more than ever. I would like to encourage applied mathematicians to reach out to ecologists: there is a lot of opportunity and interesting work to be done in this area!
]]> http://www-math.umd.edu/research/seminars.html Tue, 28 Apr 2026 12:30:00 EDT Where: Kirwan Hall 3206
Speaker: Marron McConnel and Nicole Abreu (University of Maryland) -
Abstract: Marron: We present a two-producer, one-consumer ecological stoichiometry model to investigate how differences in producer quality, nutrient enrichment, and space limitation interact to shape community dynamics. The model tracks the biomass and stoichiometry of a preferred, higher-quality producer and a less palatable, lower-quality producer alongside a single consumer, in a nutrient-closed system with dynamic producer nutrient:carbon ratios. Under a Holling Type II functional response, nutrient and space enrichment drive the system through a cascade of bifurcations distinct from those observed in classical two-species stoichiometric models, including an abrupt transition to a state in which both the preferred producer and the consumer are lost. Hysteresis analyses reveal that once this collapse occurs, no subsequent manipulation of space or nutrient availability can recover the coexistence state, underscoring the path-dependence of the system. These findings have implications for real-world systems undergoing climate-driven shifts in vegetation community composition, such as Arctic shrubification, where the encroachment of less palatable woody vegetation may push herbivore communities past critical thresholds from which recovery is not possible through environmental intervention alone.

Nicole: Visualizations of COVID-19 hospitalization data often struggle to synthesize temporal, geographic, and hospitalization information, all of which are critical for understanding pandemic impact and informing mitigation efforts. In this talk, I will demonstrate how Mapper, a tool from Topological Data Analysis, effectively captures these multidimensional relationships by representing data as a simplified simplicial complex. By applying the Mapper algorithm to COVID-19 hospitalization data from 2023-2024, I reveal how this topological approach identifies distinct geographical trends over time that traditional methods may overlook.
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