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
$860K+External funding
5xGolden 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)

2018–26

Five 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