Dr. Margot Gerritsen at Stanford University in California. Photo: Linda A. Cicero / Stanford News Service

Dr. Margot Gerritsen uses mathematics to solve a wide variety of complex problems: identifying how to produce oil and gas in a more environmentally friendly way, studying coastal ocean flows, improving sail design for America’s Cup yachts, and designing search engines for digital archives.

She has even used math to model the wings of a pterosaur, a flying reptile in the dinosaur era, for the National Geographic documentary Sky Monsters.

Margot is a computational engineer and professor of energy resources engineering at Stanford University in California.

Aside from her scientific work, Margot is recognized as one of the top teachers in her field. She teaches courses in computational mathematics, energy and sustainability, and has received several awards for her excellence in teaching, such as the Tau Beta Pi award at Stanford.

She is also passionate about the role of women in STEM, and is co-founder and co-director of the global Women in Data Science (WiDS) conference and host of the WiDS podcasts, inspiring women all over the world to enter the field.

Biking to the sea

Dr. Gerritsen loved mathematics from an early age. In sixth grade, her teacher started every morning with a five-minute head-calculation competition, which she often won, and in high school she viewed math problems as fun, complex puzzles. Overtime, math became much more applied and exciting. “I always wanted to understand the world around me a little bit more,” Margot said. “I am intrigued by that. Math is a wonderful language that can be used to express ideas and deepen understanding.”

Born and raised in a small town near the coast in the Southwest of the Netherlands, she often biked to the sea as a little girl. She would stare mesmerized, wondering what would be beyond it.

Years later, when she had finished her masters in applied mathematics in Delft, Dr. Gerritsen won an international graduate scholarship. Eager to explore what was on the other side of the ocean, she chose to study at the University of Colorado at Denver. While in Colorado, she missed the ocean badly, and after one year she left to pursue her Ph.D. in scientific computing and computational mathematics at Stanford University, much closer to the water than landlocked Denver.

First female faculty member

She felt an enormous buzz at Stanford. The environment was competitive, entrepreneurial, and provided a wealth of opportunities. Anything seemed possible.

Stanford was also very intense, Margot said, especially since she was one of the very few women in her field. “People notice you and you are being scrutinized, and I always felt this pressure to perform,” she said. “It is a bit exhausting.” After she finished her Ph.D., she sought a less competitive environment and moved to New Zealand, where she worked for five years as a lecturer at the University of Auckland.

Yet, she began to miss the buzz of intensity that was pervasive at Stanford. Five years later, when she was offered a job as a professor in Stanford’s energy resources engineering, she did not hesitate and became the first woman faculty member in the department.

A virtual laboratory

But how is math used in such diverse areas as petroleum engineering, ocean modelling, pterosaur flight mechanics, and sails design?

Margot uses math to build computerized simulations to better understand or optimize complex physical or engineering processes. She begins by gaining an understanding of the physics of a process.

For example, when helping designing sails for America’s Cup yachts, she first talked to experts in the field, including sailors and sail designers, to understand the physics of the sails and sailflows.

Once she understands the physics, she develops a mathematical model from which she can build computer programs that simulate the process, “like a virtual laboratory,” she said.

For yachts, she simulated the air flow over sail shapes to help design better sails and in the case of petroleum engineering, she uses math to build simulations to identify how to produce oil and gas in a way that it has less impact on the environment.

Saving CO2 emissions

One of the fossil energy projects that Margot is working on today is finding out how to extract heavy oil from underground in a less environmentally harmful way. Heavy oil, Margot said, is sticky and hard to move unless it is heated.

There are several ways to heat heavy oil, but current approaches to burning fossil fuels create high levels of CO2 emissions. Dr. Gerritsen is trying to change the process, and in the approach she is studying, the oil is burned under the ground so that the CO2 is generated in the reservoir, and does not create surface-level CO2 emissions.

However, this process, called “in-situ combustion,” is harder to predict and control than the surface-level process. Through her work, Margot is able to simulate in-situ combustion, which offers better insight into how the oil in the reservoir will burn. With this data, she hopes to increase confidence so that companies are more inclined to use in-situ combustion rather than others that are more harmful to the environment.

100 percent clean energy law

Besides fossil energy production, Margot is an expert in renewable energy and has worked on many projects, such as tidal energy production and the assessment of large scale solar and wind energy projects.

How does she see the future? Will renewable energy be our largest source of energy in 30 years?

“It is quite complex,” Margot said.

Previous reports that addressed these questions, even those from the International Energy Agency, made predictions that turned out to be wrong. There were developments that could not be foreseen, such as major shifts in technology and population and economic growth.

“The thing is, we don’t really know. What I’m hoping is that oil and gas will be phased out. We already see big changes in some places,” Margot said. “In California, for example, we set a renewable portfolio standard that seemed very aggressive even 15 years ago. Our first ambitious goal, set in 2002, was to generate 20% of electricity using renewable sources. In 2015, this was adjusted to 50% by 2030, and we are already nearly there. Last year, California raised this to 100% by 2045.”

Margot said she believes this goal is feasible, but that there will always be niche applications for fossil fuels, such as emergency power, and uses outside of the transport sector.

A shift will depend on effective large scale clean energy storage, but she believes that this hurdle will be overcome. History has shown, she said, that when there is an enormous economic stress in the world because of energy, everybody starts investing in energy.

When she started teaching energy courses 20 years ago, everybody agreed it would take a long time for solar and wind to become cost competitive. Yet, today they have become cost competitive, because some countries, such as China, were economically growing exceedingly fast and needed energy.

China relied heavily on coal, but this had severe health consequences for the population and it became an absolute necessity for China to invest in renewable energy.

As a result, Dr. Gerritsen said, “Solar PV is incredibly cheap right now because markets have a lot of cheaper produces Chinese PV. These things have made a huge change and are incredibly hard to predict.”

Risk-taking culture

After working in the field of energy resources, coastal ocean simulation, and data science for almost 20 years, Margot is looking for some other areas to pursue, and a field that has piqued her interest is wildland fires. Wildland fire mitigation has become increasingly critical in California and the West (of the US).

Margot has many ideas for using simulations in this field, such as creating a better understanding of the fire-induced weather changes, smoke dispersion and smoke associated health risks. She feels that the Stanford research environment provides her the opportunity to pursue these new projects.

“The nice thing about being a professor in a place like Stanford is that that you can set your own research agenda and will most likely find colleagues and students to work with, so I’m very excited about this,” she said.

She appreciates the freedom she has as a professor at Stanford. For example, it took her only three months to start a new master’s in data science program at Stanford ICM. Starting a master’s program in the Netherlands would generally take much longer, Dr. Gerritsen said, as it needs to be discussed and approved at more levels and there are typically more bureaucratic obstacles.

“Here you can often just build what you want. It is a totally different thing,” she said.

She likes the risk-taking culture in the United States, especially in Silicon Valley, where if she has an idea, she can try it.

The Dutch are a bit more risk averse, she said.

“We (the Dutch) try not to upset too many people. We still have a really hard time picking winners and losers,” she said.

When she was in school in the Netherlands, there were no awards for the best students. It was a foreign concept. She understands the sentiment behind it, but at the same time, she believes, it is important to recognize and reward excellence, which is needed to drive innovation.


“I’m an unbelievably fortunate person,” Margot said. Jobwise, Dr. Gerritsen’s dream was to be useful to people, and in her job she can be, by mentoring and teaching people and as the co-founder of Women in Data Science (WiDS), an annual conference with women experts in data science as speakers.

When she started in computational mathematics 35 years ago, it was a field comprised of roughly 15 percent women. “It is very frustrating to see that this percentage today has even gone down a bit,” Dr. Gerritsen said.

Many important decisions (e.g. in healthcare, industry, and politics) are made based on data analyses. Data science teams are influential, as they interpret data and make predictions based on this data. Most of the data science teams consist of men and are not diverse. Diverse teams are necessary because they ask different questions and have different perspectives, which can lead to different conclusions.

In addition, there are not enough qualified people in this field at the moment. To inspire more women to work in this field, she started the Women in Data Science Conference, which showcases excellent female experts in data science, the WiDS datathon and a WiDS podcast.

The podcast, in which leading women in data science from all over the world share their work and expertise, was necessary, as articles and interviews on AI up until five years ago were mostly all written or given by men.

Today, in its fifth year, the WiDS conference has gone global with online and satellite events, inspiring women worldwide to enter the field.

“Through the conference we reach over 120,000 people per year,” Dr. Gerritsen said. “A lot of them are women, and if you look for talks now in data science online, you probably will find a talk related to WiDS, which is great!”

Read about other Dutch researchers in the United States.