From a young age, I had many passions and interests, including geophysics and meteorology, but I wanted something that would open all doors for me. Since I was good at math, I thought studying it would give me endless opportunities. My brother and cousin were also studying math, and I wanted to prove that girls could do it too, especially at a time when there weren’t many women in math. I applied to Delft University and was accepted.

I also had a strong desire to live abroad. Growing up in Zeeland by the sea, I often dreamed of living in places like Scotland or an island. Later, I married a Scottish man, and I was fortunate to receive a scholarship that took me worldwide. I studied applied mathematics and taught evening classes. Teaching was something I've loved since I was 16. In the US, I found freedom in pursuing my PhD and felt liberated from strict societal norms. I could be myself, which was a powerful experience.

In the U.S., there was much more freedom. As a student, I even led a group, which gave me leadership experience. The US allowed me to create my own courses and teaching methods, which I enjoyed. In the Netherlands, the system was more rigid with a prescribed syllabus, which was fine as a student but limiting as a teacher.

I moved to New Zealand for my PhD with my then-husband, seeking a different lifestyle. The teaching environment there was different from Stanford, and I had the opportunity to develop and teach courses fully.  After my first husband wanted to move to Silicon Valley during a boom, I reinvented myself as a reservoir engineer and we returned to California. I attended a conference at Stanford and, to my surprise, was offered a position. It was a challenge, especially since it was the first female position in that department. But I took it, and after tenure, I became the director of a new department, ICMI, where applied mathematics was highly valued.

In the Netherlands, the freedom for young researchers has been a priority for years, and it’s improving. However, in the US, as soon as you're an assistant professor, you're in charge of your own direction and can guide students. This level of independence wasn’t possible in the Netherlands, though it’s getting better. My colleagues in the Netherlands agree that more freedom fosters innovation.

In the US, if you have a good idea or funding, you can move forward. At Stanford, there are always opportunities for financial support. However, setting up new programs in the Netherlands can take years to be approved. Another difference is the openness of students in the US. In the Netherlands, it was rare for students to ask questions, but in the US, they are much more curious. I think this approach benefits learning, and the Netherlands could learn from it.

In the Netherlands, sector plans were created to increase female representation in STEM, and I was part of a committee focused on this. The goal was to hire more female professors, and we pushed universities to raise their numbers, which were only 6-7 percent.

In the US, I started Women in Data Science. Despite efforts, the percentage remains low, and people celebrate if it reaches 25 percent. In the Netherlands, it’s still the same. When I was younger, there were girls in engineering and STEM, but after 50 years, not much has changed. It’s surprising to people that I’m from the Netherlands, thinking it’s progressive, but the reality is that it’s still quite poor, and it’s the same in countries like Denmark and Sweden.

Data science has always played a large role in addressing global challenges. Collecting and analyzing critical data to monitor and observe systems, understand processes, and inform decisions is an essential part of any endeavor, in research, industry or government. Because the collection of data has become so much cheaper and easier (think of remote sensing, for example), data-driven science and decision making is more prevalent than before. Whether or not we can really address global challenges is not often a question of data science, but more a question of ethics and politics (in my humble opinion!). 

There are trends in all fields. People are focused on AI and what we can do with it. There are also trends in data computational science, especially the integration of new techniques and more synthesis between fields. People are now seeing connections between optimization, physics, astronomy, and medicine. It’s easier to collaborate and work interdisciplinarity. Bringing new disciplines together offers many possibilities. If I had to choose, I’d say healthcare, particularly something simple like clinical trials.

I’ve always thought that as a leader you are first and foremost an enabler: it's your responsibility to create the right environment, processes and tools to enable others to do their best work, whether students, staff or faculty. Often this means making bureaucratic processes disappear or more efficient, allowing people to pursue their interests, supporting great ideas, offering students, staff and faculty opportunities to learn (and fail, and learn again),  mentoring and guiding everyone, creating clarity about expectations and promotions, etc. I’ve also always been interested in building communities, with strong interactions and collaborations. This is not to say that I've succeeded at all of this, but it's been a major driver for me.

In STEM, and certainly in computer math/CS/data science/AI, changes are fast and furious. New approaches, new programming languages, new hardware, new software requirements, more and more data, different and messy date. It's really very hard to be on top of everything and that makes a lot of students (and professionals!) very nervous. The best thing you can do for yourself is to become a life-long and passional learner. To not fear jumping into something new. For that to be comfortable and rewarding, you need to be agile. How do you get the required level of agility? By focusing on the foundational knowledge of the field (many new ideas are understandable if you master core principles), and expose yourself to many different ideas and directions, trying new things, failing (as often as possible) and learning from it. Risk aversion is not good. I always believe that an expert is someone who has made every possible mistake, or at least a lot of them, and really understands and accepts and embraces that failure is not failure, just learning.