In This Story
When most people think of industrial and systems engineering, they picture assembly lines, supply chains, or power grids. For Professor Lizhi Wang, a new faculty member with a joint appointment in George Mason University’s Department of Bioengineering and Department of Systems Engineering and Operations Research, the field is much broader, and much more alive.
“Nature is the most complex system there is,” said Wang. “If you compare what we know about biology to what there is to learn, our knowledge base is still close to zero. That’s what fascinates me.”
Trained as a systems engineer, Wang has built a career applying mathematical modeling to real-world challenges. After 16 years at Iowa State University and a recent post at Oklahoma State, he joined George Mason this fall to pursue interdisciplinary research connecting systems engineering with biology, agriculture, and medicine.
His research first focused on plants. By combining biology with mathematical simulations, he and his collaborators created models that predict how seeds develop into crops under different environmental conditions, down to hourly shifts in temperature, sunlight, and humidity. These models allow farmers and greenhouse operators to optimize yields while cutting energy and water use. The work was so promising that Wang co-founded Crop Convergence, a startup company that now works directly with growers. “If crops aren’t photosynthesizing, there’s no reason to run the lights or adjust the temperature,” he explained. “Our models help farmers save money and resources.”
From plants, his systems engineering lens expanded to animals. Using similar simulation methods, his team is modeling poultry and dairy cow growth, studying how feed choices, genetics, and health monitoring affect outcomes. The goal is to prevent disease outbreaks, improve animal welfare, and enhance efficiency for producers.
Now at George Mason, Wang is pushing the boundaries further into human health. His current focus is building models of the human heart, using equations grounded in physiology and validated with electrocardiogram data. The approach could one day provide early warnings of heart problems and new insights into disease prevention. “I see great opportunity at the intersection of bioengineering and systems engineering,” he said.
At the core of his work is a belief in the systems engineering mindset: solving problems by breaking them down into models, algorithms, and solutions that adapt to new technology. Unlike black-box artificial intelligence, like that used in ChatGPT, his models are grounded in biology and physics. “Every parameter has meaning,” he emphasized. “If something is wrong, we can trace it, fix it, and improve the model.”
This balance of domain knowledge and data science, he argues, is what makes systems engineering so powerful. Domain experts may not know how to use big data, while data scientists may miss critical biological details. “Systems engineering brings these worlds together,” he said.
Beyond research, Wang is eager to bring this mindset into the classroom. He and his colleagues are developing courses to expose bioengineering students to systems engineering approaches to broaden how future engineers see and solve problems. His philosophy boils down to four steps: understand the problem, build a model, design an algorithm, and implement it in software. “If students can internalize that approach, they’ll be able to tackle any challenge, even in human health.”
For Wang, the journey from lettuce fields to human hearts is just beginning. But his vision is clear: harnessing the tools of systems engineering to better understand life itself and bring these solutions to the world.