Alumni Spotlight: Shugong Wang (PhD CEE ’11)

Shugong Wang

As a child growing up in China at the start of intensified industrialization, Shugong Wang watched as factories sprang up around him like flowers blooming after a spring rain. He also watched those factories alter the environment, with the water and air becoming increasingly polluted.

“Since I was a kid, I was very curious about how we can use engineering to thrive our living standards without destroying the environment,” recalled Wang. “I have a very strong interest in protecting water and other related resources. This is what led me to engineering.”

And learn he did. Wang pursued a bachelor’s degree in hydrogeology and engineering geology from Lanzhou University and earned a master’s in geography from the Graduate School of the Chinese Academy of Sciences, all the while taking extra courses in math so he could better understand the theory behind what he was learning.

His journey would then lead him to the University of California Berkeley as a visiting researcher to work with Xu Liang, then an Assistant Professor at Berkeley. When Prof. Liang joined the Department of Civil and Environmental Engineering at the University of Pittsburgh Swanson School of Engineering, Wang followed her and joined Pitt as a PhDstudent in 2006.

Liang’s research includes hydrological modeling, which investigates how the physical and hydrological processes interact along the landscape and within the soil in which water moves from the land to ocean and evaporates to the atmosphere and back to the land and ocean surface in the form of precipitation. It has diverse applications including flood and drought predictions, pollutant transports, stream flow estimates and long-term trend analysis, water resources management, soil erosion, and more. While in Liang’s lab, Wang worked with her on enhancing the Variable Infiltration Capacity (VIC) model. Professor Liang is a pioneer and leading scholar in the field of land surface modeling, which is important for the understanding of the water and energy cycles of the earth.

After graduation, Wang’s experience with the VIC model landed him an out-of-this-world opportunity.

NASA’s Hydrological Science Laboratory was on a mission to combine the VIC model to their other models and Wang’s expertise in the topic makes him extraordinarily valuable to the mission. 

“To better predict the impact of weather, we need a better way to couple together the weather model and the hydrological model,” explained Wang. “Traditionally, model coupling is done by scientists, but most scientists aren't software engineers. On the other hand, if you ask a software engineer to do this work, they don't know the science. And that makes the model coupling work a big challenge.”

After a couple months of investigation, Wang reported to his supervisor that he had indeed found a way to automate this model coupling work. What used to take several years now, with Wang’s tool, would only take a couple of weeks.

When his wife, who earned her PhD from Carnegie Mellon University, got a job at Duke University, he had another difficult decision to make. He could continue working remotely for NASA, with frequent five-hour drives to Maryland, or he could end his 10-year tenure at NASA and pursue another job closer to home. Ultimately, he chose to walk away and soon became a senior scientist at Applied Research Associates.

“I was happy to work with so many smart people at NASA. I learned a lot, and especially about problem solving at a large scale,” said Wang. “I think engineering students should know that it's not a guarantee you'll find an engineering job in your exact field after you finish school. But what can help you have a successful career? Problem solving skills and a solid foundation in math.” 

This theory proved sound in Wang’s next career move. His new role—a Senior Scientist at ARA—was in a field that, at face value, seemed foreign to him: satellites and remote sensing. During his technical interview, however, the questions directed to him were not about satellites, sensing, or even civil engineering—they were all about math. Wang had the building blocks he needed to succeed at this job.

“My supervisor asked me, ‘Do you want to try computer vision?’ I said, ‘I have no idea what this is, I've never done it,’” Wang recalled. But his supervisor pointed out that his research background, heavy in math and optimization, would be perfect for solving the fundamental problems of computer vision. 

Wang said, “If you say so.”

A few weeks and a few textbook reviews later, Wang was up to speed and charging ahead on his new project—work he continues today while at the same time he also mentors junior scientists at ARA.

“In the beginning, you may not think that the math you’re learning is useful, but later, when you go to do a real job, those dots might connect into lines, into a surface, into an object,” said Wang. “This is the key for many people: Math is the common language of engineering and applied science. It builds a great foundation for all kinds of opportunities”