Cleveland State University · Dept. of Mathematics & Statistics

Math that explains how living systems move together.

I build agent-based models and differential equations that turn the behavior of bacteria, ants, and cells into predictive theory, and I'm teaching those models to discover their own governing equations.

44Publications
$1.2M+External funding
4xGolden Apple Teaching Awards
2024Distinguished Teaching Award (College and University)
The work

Interactions, and the collective behavior they produce.

The thread running through everything I do is interaction. Bacteria pushing fluid, ants laying pheromone, chromosomes finding their pairs. Local rules, followed by many agents, produce behavior no single agent contains. I write the models that predict when that behavior emerges and what it changes about the system.

01 Collective motion

Active biosystems

Swimming bacteria, swarms, and social insects. Coupled PDE/ODE models that reproduce lane formation in ant raids and the viscosity collapse in bacterial suspensions.

02 Active matter

Self-propelled particles

Active rods accumulating at the walls of microchannels, self-propelled Janus colloids, and how confinement and propulsion set the structure of synthetic active systems.

03 Mathematical biology

Cells & clinics

Agent-based chromosome pairing in meiosis, synthetic microbial consortia, and clinical models estimating postoperative urine output after pediatric cardiac surgery.

04 Food safety

Poultry contamination

Individual-carcass and spatial models of Campylobacter and E. coli through poultry chilling. USDA-NIFA funded, built with experimentalists and public-health partners.

05 New direction

Equation learning

PINNs and BINNs that recover governing equations directly from agent-based simulations.

Where the group is heading

From agent-based simulations to discovered equations.

Agent-based models capture the biology, but they don't hand you a governing equation. These questions are what the research group is excited about. Physics-informed and biology-informed neural networks (PINNs / BINNs) can learn the continuum PDE hiding inside an ABM, turning a simulation into a theory you can analyze.

Pipeline

ABM → data → BINN → PDE

Agent-based model Simulation data BINN training Governing PDE
PyTorch / JAX Reaction–diffusion Foraging dynamics Loss design Equation discovery
Recognition

Teaching and Research Accomplishments

Full record
2024

Provost's Distinguished Teaching Award (University)

2024

Jearl D. Walker Teaching Award (College of Arts and Sciences)

2018–25

Four CSU Golden Apple Teaching Awards (alumni-nominated)

2014

Penn State Pritchard Dissertation Award

2008

Barry M. Goldwater Scholar

Editorial

Associate Editor, Involve: A Journal of Mathematics

Editorial

Review Editor, Frontiers in Systems Biology

Selected work

Recent papers.

All 44
Ryan Research Group Work with the group

Students and collaborators welcome.

I mentor students through real research, from foraging models to food-safety projects to the new equation-learning work. If you want to build models that connect to experiments, get in touch.

Get in touch Meet the group