Cindy is thrilled to be joining as podcast co-host and believes that showcasing women at all stages of their careers shows that we “share the same fears or experiences every day. It's just that some of us have been on the path a little bit longer than others.”
Cindy is an applied mathematician who is currently working as a machine learning solutions engineer at Cerebras Systems. Originally from Colombia, she loved applied math, and did a master's in civil engineering and mathematics from King Abdullah University of Science and Technology (KAUST), in Saudi Arabia, and a PhD in Computational and Mathematical Engineering from ICME at Stanford. She met Margot at Stanford and has been contributing to WiDS for many years at conferences, workshops and datathons.
After answering some questions about herself, Cindy stepped right into her co-host role to interview Margot. A native of the Netherlands, Margot said her career path was similar to Cindy’s as she started in math, got excited about applied math, and decided to study fluid mechanics. After getting her PhD at Stanford, she became a professor at the University of Auckland in New Zealand and then returned to Stanford where she has been a professor for 20 years. During this time, she has been an accomplished researcher, professor, mentor, and leader in the School of Earth, Energy & Environmental Sciences, the Institute for Computational & Mathematical Engineering (ICME), and Women in Data Science (WiDS).
When asked how she managed to juggle all of these things, Margot said she learned to not worry about making mistakes or striving for perfection, saying, “80% is perfect”, adding “I always felt I can't have it all. So you make choices, and there's always something that's got to give.” Cindy agreed that the busier she is, the better she manages her time, and when you have many balls in the air, often what you learn in one area can help you solve problems in another.
In discussing the “imposter syndrome”, Margot said she had often felt like an imposter, and soon discovered this was a common feeling among students and faculty at Stanford. And it’s even stronger when you stand out, like a woman in STEM. It puts an extra burden on you to succeed to set the example for those who come after you.
The pace of research in AI and deep learning contributes to feeling like an imposter. People publish very quickly and it's hard to understand what really good solid research is and what is just an idea. It gives people this sense that they're not on top. They forget the purpose of school is creating a lifelong interest in learning. “There's a lot of failure on the way to success. My favorite definition of an expert is somebody who's made every possible mistake.”
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