This page highlights recent lab activities, student achievements, collaborations, and selected historical milestones. We update it regularly as new projects, publications, and events occur.
2026
Fall 2026 Teaching
Dr. Majid Bani-Yaghoub will teach Math 469 (Mathematical Modeling) in Fall 2026 (on Tuesdays and Thursdays, 11:30 AM–12:45 PM). This project-based course will provide hands-on experience in mathematical modeling.
Spring 2026 Graduate Student News:
- Kiel Corkran is making excellent progress and will defend his PhD thesis on April 24, 2026. He has been very active in interdisciplinary research.
- Barsha Saha has been awarded the Chancellor’s Research Grant and is now preparing for her comprehensive exam.
- Mohammed Alanazi is making excellent progress on his doctoral research and is scheduled to defend his thesis on April 28, 2026. He will soon submit his third research article, which connects traditional methods applied to delay differential equations to the most modern data-driven methods for model discovery.
- Bryan Harris has successfully passed the comprehensive examination and has received an Honorable Mention prior to the BioNexus KC conference. Bryan will also present at SIAM and EEID Vtech.
- Arash Arjmand is making excellent progress on his doctoral research and is scheduled to defend his thesis on May 1, 2026. He will submit his third research paper in the next few weeks, which has been rigorously validated through different methods.
- Priscilla Owusu Sekyere is also ready for her comprehensive exam and is set to present her work at three upcoming research conferences (SIAM, AMS and BioNexus).
Spring 2026 Ph.D. Dissertation Defense (1/3)
Ph.D. Dissertation Defense: Integrated Mechanistic and Data-Driven Modeling of Antimicrobial Resistance and Infectious Disease Across One Health and Hospital Systems
Arash Arjmand, Ph.D. Candidate in Mathematics and Biomedical and Health Informatics
Date: Friday, May 1, 2026,
Time: 11:30 AM–1:30 PM CST
Location: Flaresheim Hall 531
Supervisory Committee:
Dr. Majid Bani-Yaghoub, Division of Computing, Analytics and Mathematics (Chair)
Dr. An-Lin Cheng, Department of Biomedical and Health Informatics
Dr. Bowen Liu, Division of Computing, Analytics and Mathematics
Dr. Shuhao Cao, Division of Computing, Analytics and Mathematics
Abstract: Antimicrobial resistance (AMR) and infectious diseases propagate across interconnected settings, where agricultural reservoirs, community exposure, clinical risk, and hospital operations shape transmission dynamics in distinct ways. This dissertation advances integrated mechanistic and data-driven frameworks that combine clinical and observational data to quantify uncertainty and support health decisions across farms, communities, and healthcare settings. First, a hybrid model of Ordinary and Stochastic Differential Equations (ODE and SDE) is developed and validated to investigate the dynamics of AMR Enterobacteriaceae in dairy-farm systems, with a focus on Salmonella. This framework links the dynamics of the cattle population, the spread of AMR in the environment, and the actions of farmworkers. Farmworkers’ biosecurity compliance is modeled through scenario-based changes in transmission and control parameters within this hybrid framework. Model analysis and simulations show that intervention effectiveness depends strongly on farmworker adherence and that reducing host-to-host transmission in cattle has the greatest impact on lowering infection and AMR spillover risk. Second, supervised machine-learning pipelines are developed to support clinical risk stratification in healthcare settings using electronic health record (EHR) data. In the first study, multiple machine-learning models are applied to local University Health data to distinguish healthcare-associated from community-associated urinary tract infection and to identify key demographic, hospital-operational, and socioeconomic predictors. Across models and years, nurse unit consistently emerges as the dominant predictor of infection-acquisition risk. In the second study, a multicenter Cerner HealthFacts cohort is used to predict concurrent acute pyelonephritis in men, showing that severe presentation is driven primarily by urogenital pathology and structural or functional urinary-tract factors rather than by broad contextual variables alone. Third, hospital MRSA dynamics are studied using ward-level surveillance data. Sparse Identification of Nonlinear Dynamics (SINDy) is evaluated under non-autonomous forcing from admissions, discharges, and demographic stochasticity. This analysis shows that standard autonomous equation discovery is ill-posed under non-autonomous hospital forcing. To address this problem, we developed a hybrid open-system framework that combines an Austin-type mechanistic backbone with Gillespie stochastic simulation and time-varying coefficients. This approach supports mechanistic interpretation, uncertainty quantification, and stable conditional prediction under observed hospital flows. Collectively, these findings demonstrate that effective integrated mechanistic and data-driven modeling requires a clear understanding of data structure, the form of uncertainty, and the epidemiologic question, rather than imposing a uniform modeling strategy across settings.
Bio: Arash Arjmand is a Ph.D. candidate in Mathematics, with a co-discipline in Biomedical and Health Informatics, at the University of Missouri–Kansas City (UMKC). He received his M.S. in Mathematics from UMKC in 2021. His research centers on infectious disease modeling, antimicrobial resistance, and clinical informatics, with a focus on hybrid mechanistic, stochastic, and data-driven approaches across farm, clinical, and hospital settings. He is the first author of peer-reviewed publications in One Health and Journal of Hospital Infection and has contributed to collaborative work on MRSA transmission and agent-based infectious disease modeling. He also has several years of experience teaching mathematics at the college and university levels.
Spring 2026 Ph.D. Dissertation Defense (2/3)
Ph.D. Dissertation Defense: Mathematical Modeling and Sparse Identification of Delay-Driven Dynamical Systems with Applications to Gene Regulatory Networks
Mohammed Alanazi, Ph.D. Candidate in Mathematics
Date: Wednesday, April 29, 2026
Time: 2:00 PM–4:00 PM CST
Location: Royall Hall 305
Supervisory Committee:
Dr. Majid Bani-Yaghoub, Department of Mathematics and Statistics (Chair)
Dr. Paul Rulis, Department of Physics & Astronomy
Dr. Liana Sega, Department of Mathematics & Statistics
Dr. Qiao Zhuang, Department of Mathematics & Statistics
Dr. Elizabeth Stoddard, Department of Physics & Astronomy
Abstract: Delay-driven dynamics arise naturally in many biological and physical systems, where finite processing times, transport effects, and memory mechanisms significantly influence system behavior. Although such delays are often not directly observable, they play a critical role in determining stability, oscillations, and pattern formation of complex systems. This dissertation investigates delay-driven dynamical systems through a combination of mathematical methods and data-driven system identification, with a focus on oscillatory gene regulatory networks. The first part of the dissertation develops and analyzes delayed reaction–diffusion models motivated by the Hes1 gene regulatory system. A spatiotemporal framework is proposed to describe the interaction between Hes1 mRNA and protein, explicitly incorporating nuclear–cytoplasmic structure, diffusion, and transcriptional delay. Rigorous stability analysis is performed, and explicit conditions are derived for delay-induced Hopf bifurcation, demonstrating that time delay is the key parameter governing the transition from stable steady states to sustained oscillations. The second part of the dissertation focuses on identifying delay-driven dynamics directly from data. We develop a novel data-driven framework by extending the Sparse Identification of Nonlinear Dynamics (SINDy) methodology to systems with distributed delay using the method of Linear Chain Trick. This approach enables the simultaneous identification of governing equations, effective delay, and delay distribution from time-series data while preserving interpretability. Numerical experiments demonstrate the robustness of the method under noise and sparse sampling. Finally, the proposed framework is extended to spatiotemporal systems, enabling the identification of delayed reaction–diffusion models from data. This work establishes and tests a systematic approach for discovering distributed-delay dynamical systems and provides a foundation for data-driven modeling of spatiotemporal systems with memory.
Bio: Mohammed Naif Alanazi is a Ph.D. candidate in Mathematics within the Interdisciplinary Ph.D. Program at the University of Missouri–Kansas City (UMKC). His research focuses on the mathematical modeling and analysis of delay-driven dynamical systems, with particular emphasis on gene regulatory networks involving negative feedback mechanisms. His work integrates delay differential equations and reaction–diffusion systems with data-driven approaches, especially sparse identification of nonlinear dynamics (SINDy), to study oscillatory behavior in biological systems. Mohammed has published his research in peer-reviewed journals, including Mathematical Biosciences and Engineering, and has additional work under review in Discrete and Continuous Dynamical Systems–B. He has also presented his research at conferences such as the SIAM Central States Section Annual Meeting and the Stowers Research Conference on AI-driven approaches to emergent protein behaviors.
Spring 2026 Ph.D. Dissertation Defense (3/3)
IPhD Dissertation Defense: Uncertainty Quantification in Data-Driven Modeling of Infectious Diseases Across Diverse Healthcare Settings
Kiel Daniel Corkran, IPh.D in Mathematics with Emphasis in Statistics and Biomedical Informatics Candidate
Date: Friday, April 24th, 2026
Time: 12:00 pm to 2:00 pm
Location: Farshiem 337
Supervisory Committee:
- Dr. Majid Bani-Yaghoub (Chair), Department of Mathematics and Statistics
- Dr. Bowen Lui, Department of Mathematics and Statistics
- Dr. An-Ling Cheng, Department of Biomedical and Health Informatics
- Dr. Steve Simon, Department of Biomedical and Health Informatics
- Dr. Alex Franisco (Honorable member), Chief Science Officer at KCMO Public Health Department
Abstract: This research develops and applies multi-method frameworks for uncertainty quantification (UQ) in mechanistic models of infectious disease transmission across complex healthcare environments. A central contribution of this work is the implementation of uncertainty quantification through two complementary methodologies: inference-based UQ using Bayesian Markov chain Monte Carlo (MCMC) methods and simulation-based UQ using agent-based modeling (ABM). The inference-based component of this research focuses on estimating transmission dynamics of methicillin-resistant Staphylococcus aureus (MRSA) within a safety-net hospital. Using the Delayed Rejection Adaptive Metropolis (DRAM) algorithm, this work estimates transmission parameters, quantifies parameter uncertainty, and compares transmission risks across heterogeneous patient populations, including drug users and non-drug users, as well as across distinct hospital units. The simulation-based component investigates the spread of COVID-19 within and between a network of nursing homes connected through shared healthcare staff. An agent-based model developed in the GAMA platform simulates individual-level interactions and transmission events to evaluate how staff-sharing networks influence outbreak dynamics. Through repeated stochastic simulations, this approach quantifies uncertainty in network-driven transmission and identifies conditions under which outbreaks become sustained across facilities. Together, these two approaches demonstrate how uncertainty quantification can be incorporated into mechanistic infectious disease models through both statistical inference and stochastic simulation. By combining Bayesian parameter estimation with simulation-based modeling, this research provides a comprehensive framework for understanding transmission dynamics, quantifying uncertainty, and evaluating infection control strategies in diverse healthcare settings.
Bio: Kiel Corkran is a PhD candidate in Mathematics with a co-discipline in Biomedical and Health Informatics at the University of Missouri–Kansas City (UMKC), where he also earned a Master of Science in Statistics. He is a trainee in the National Science Foundation (NSF) National Research Traineeship (NRT) program and has also participated in the Centers for Disease Control and Prevention (CDC) Healthcare, Infectious Diseases, Research (HIRe) Modeling Fellowship program. His research focuses on infectious disease modeling, Bayesian statistics, and data-driven methods for understanding disease transmission in healthcare settings. His dissertation develops methods for uncertainty quantification in infectious disease models using both Bayesian parameter estimation and agent-based modeling, with applications to hospital-acquired infections and transmission dynamics in healthcare environments. His work aims to improve understanding of disease transmission and support decision-making in public health and clinical settings.
Spring 2026 Comprehensive Exam (1/2)
PhD Comprehensive Exam: Modeling Spatial Dynamics of Cancer Growth: Reaction–Diffusion Systems and Physics-Informed Neural Networks
Priscilla Owusu Sekyere, Ph.D. Student in Mathematics
Date: Friday, May 8th 2026
Time: 1:00 PM–3:00 PM CST
Location: Royal hall, Room 402
Supervisory Committee:
Dr. Majid Bani-Yaghoub, Department of Mathematics and Statistics
Dr. An-Lin Chen, Department of Mathematics and Statistics
Dr. Bowen Liu, Department of Mathematics and Statistics
Dr. Zhuang Qiao, Department of Mathematics and Statistics
Abstract: We recently developed mechanistic models formulated by reaction–diffusion equations to study tumor progression, therapy interactions, and stability analysis of equilibria. Stability analysis of the cancer-free equilibrium was used to investigate conditions leading to tumor persistence, while numerical simulations explored the effects of combined therapies across different tumor stages. These models offered strong biological interpretability but lacked predictive capability and parameter estimation. Building on this foundation, our current work focuses on integrating data-driven approaches with mechanistic modeling through Physics-Informed Neural Networks (PINNs). The proposed PINN framework embeds tumor–immune interaction dynamics into a neural network to enable the model to learn both tumor growth trajectories and biologically meaningful parameters from data. Using longitudinal lung cancer tumor volume data, the model shows good predictive performance while maintaining interpretability of underlying biological mechanisms. Comparisons with standard machine learning models highlight the advantages of PINNs in balancing accuracy and interpretability. Future work will integrate environmental and occupational influences into the modeling framework to better capture lung tumor development. Overall, our work contributes to hybrid modeling approaches for improved understanding of cancer progression and more effective treatment planning.
Bio: Priscilla Owusu Sekyere is a Ph.D. student in mathematics with a co-discipline in Bioinformatics at the School of Science and Engineering at UMKC . Supervised by Dr. Majid Bani-Yaghoub. Her research focuses on mathematical modeling of cancer dynamics. Her work applies mechanistic models, such as ordinary and reaction–diffusion equations to mathematical modelling of Cancer growth as well as integrating data-driven approaches into cancer growth dynamics. She is interested in bridging mathematical theory with real-world biomedical applications and plans to pursue a career in interdisciplinary research and teaching at the interface of mathematics, data science, and healthcare. She earned her master’s degree in mathematics at the University of Central Missouri and her bachelor’s degree in mathematics with economics at the University of Cape Coast – Ghana.
Spring 2026 Comprehensive Exam (2/2)
PhD Comprehensive Exam: Mechanistically-Informed Neural Dynamics (MIND): A Hybrid Architecture for Interpretable and Predictive Modeling in Sparse-Data Environments
Bryan Harris, Ph.D. Candidate in Mathematics
Date: Friday, March 20th 2026
Time: 2:00 PM–4:00 PM CST
Location: FH 531 - Toyota Room
Supervisory Committee:
Dr. Majid Bani-Yaghoub, Department of Mathematics and Statistics
Dr. Noah Rhee, Department of Mathematics and Statistics
Dr. Bowen Liu, Department of Mathematics and Statistics
Dr. Shuhao Cao, Department of Mathematics and Statistics
Dr. Elizabeth Stoddard, Department of Physics
Abstract: A fundamental challenge in computational modeling is the trade-off between the interpretability of mechanistic models and the predictivity of machine learning (ML). While mechanistic ODEs provide transparency, they often fail to capture complex stochasticity; conversely, "black-box" ML models lack the explainability required for high-stakes decisions. We address this gap by introducing Mechanistically-Informed Neural Dynamics (MIND). Namely we propose a novel hybrid architecture that utilizes Physics-Informed Neural Networks (PINNs) to unify these paradigms, using epidemiological data as a high-dimensional testbed for validation. To address data accessibility, we first developed EpiNCBI_V1, an open-source Python pipeline that automates the extraction and cleaning of 477,000+ genomic records, transforming unstructured metadata into features for constrained learning. Centrally, the MIND framework implements a multi-physics objective function where the network is regularized by a composite loss function of associated with the data and multi-physics. This embeds mechanistic constraints such as compartmental dynamics and environmental forcing directly into the architecture to ensure the model converges on a physically plausible manifold. Our initial results demonstrate that MIND outperforms physics-agnostic neural networks and traditional time-series models such as ARIMA in both accuracy and parameter transparency. By expanding this approach into a One Health framework, we aim to incorporating socioeconomic and behavioral priors. We hope that this work will ultimately establish a scalable methodology for constrained deep learning. The significance of MIND extends beyond epidemiology, and it can provide a robust solution for any physics and engineering domain where data is sparse or underreported. Given the initial results, we hope that MIND can become a transformative tool for data-driven discovery grounded in physical reality.
Bio: Bryan Harris is a Ph.D. student in mathematics at the School of Science and Engineering at UMKC advised by Dr. Majid Bani-Yaghoub. His research focuses on Physics-Informed Machine Learning and its application to the biological sciences, including bioinformatics and epidemiology. Bryan earned dual-master’s degrees in mathematics and statistics/data analytics from the University of Kansas and the University of Kansas Medical Center respectively. Bryan has held positions as a Junior Data Scientist and as a Data Analytics Bootcamp Teaching Assistant. Bryan also served as President of the UMKC Mathematics & Statistics Graduate Student Organization from 2019-2022.
Spring 2026 Research Leave
In Spring 2026, Dr. Majid Bani-Yaghoub will be on research leave and will join the Department of Biomedical and Health Informatics at the UMKC School of Medicine, with the goal of strengthening interdisciplinary collaborations in health informatics, infectious disease modeling, and data-driven healthcare research across UMKC.
2025
Graduate Students & Lab Members
- Barsha Saha (PhD Student)
- Barsha’s 2025 work examines infectious disease modeling under climate-change influences. She has recently submitted an article on differentiating between pathogen evolution and intervention effects.
Related research website: Barsha’s Research Hub. - Arash Arjmand (PhD Candidate)
Arash recently published work on machine learning and infection risk prediction in the Journal of Hospital Infection and a One Health study on biosecurity compliance.
Research website: Arash’s Research Hub | Google Scholar. - Kiel Corkran (PhD Candidate)
Kiel’s work includes Bayesian estimation of hospital MRSA transmission and large-scale modeling of infection spread across nursing home networks, with two recent publications and manuscripts in preparation on hospital epidemiology and antimicrobials.
Research website: Kiel’s Research Hub - Bryan Harris (PhD Student)
Bryan submitted work to the Health Informatics Journal on leveraging genomic metadata to extract epidemiologically meaningful patterns. Bryan's work bridges computational biology and health data science. See the preprint - Julia Pluta (PhD Student)
Julia’s research integrates health equity with mathematical modeling of nursing homes and spatial cluster analysis for public health surveillance. Her work continues to expand on equity-centered modeling methods. - Mohammed Alanazi (PhD Candidate)
Mohammed’s contributions include the analysis of delayed reaction–diffusion models and a novel LCT–SINDy framework for identifying distributed-delay dynamics, which is under review at the SIAM Journal on Applied Dynamical Systems. Mohammed's Research Hub - Priscilla Sekyere (PhD Student)
Priscilla published a reaction–diffusion model of vascular tumor growth and is expanding her research into Physics-Informed Neural Networks (PINNs) for cancer modeling and data analysis.
Research website: Priscilla’s Research Hub
Undergraduate Researchers
- Sam Golladay (Undergraduate Researcher)
In April 2025, Sam presented his research at UMKC’s Symposium of Undergraduate Research & Creative Scholarship (SEARCH). His project, Evaluating the Accuracy of Large Language Models on Mathematics Competition Problems, examined how leading AI systems (such as GPT-4 and Gemini) perform on advanced problem-solving tasks. Manuscript submitted to ACM Transactions on Intelligent Systems and Technology. - Gianna Cado (Math Graduate)
Gianna completed an honors thesis, Using Mathematical Models to Analyze the Spread of Antimicrobial-Resistant Escherichia coli Between Farms and Communities, using compartmental models and simulations to assess transmission dynamics and intervention effectiveness.
Selected News Prior to 2025
Infectious Disease & One Health Research
- Virtual Laboratory of Pathogen Transmission in Healthcare (2022): Launch of a collaborative effort focused on pathogen transmission among healthcare workers and settings.
- UMKC news feature
- One Health Modeling and Antimicrobial Resistance (2022): Research initiative seeking new modeling tools to combat antimicrobial-resistant organisms.
BioNexus KC feature - COVID-19 Research by the Numbers (2020): Math and statistics students at UMKC began studying the potential spread of COVID-19 in January 2020, contributing to early quantitative insights during the pandemic.
UMKC news story
Community-Engaged & Public-Facing Research
- Mathematics and Music (2018): Dr. Bani and his Math 406 students collaborated with the Kansas City Art Institute for an Interdisciplinary exploration of connections between music, sound, mathematics, and art, inspired by Gottfried Leibniz’s observation that “Music is the pleasure the human mind experiences from counting without being aware that it is counting.”
This work included numerical simulations of PDE-based models related to different musical instruments and an Artist-in-Residence collaboration during Folk Alliance International (FAI).
Video highlight 1 | Video highlight 2 - Community Data Analysis Project (2017):
Correlation analysis of water rates and water shutoffs in Kansas City, featured in KCPT’s documentary Public Works – Water Rates & Rivers.
Watch the documentary segment | UMKC feature (archived)
Student Scholarship & Community Platforms
- RooMath News: An ongoing student- and faculty-focused platform highlighting accomplishments in mathematics and statistics at UMKC, including upcoming events, advice for math majors, math-related articles, puzzles, and outreach activities.
Visit RooMath News - Mathematics & Statistics Research Day (MSRD): An annual event providing a platform for students and faculty to publicly present research and scholarly activities in mathematics and statistics.
MSRD Website