Women in Quantum Machine Learning – Anais S. N. Guiawa

According to Wikipedia, Quantum Machine Learning is the integration of quantum algorithms within machine learning programs. Our guest for the day is a researcher in the field of Quantum Machine Learning;

Hi Sandra, thank you for sharing your story with the world today. I am personally excited to learn about Quantum Machine Learning
Hi Winnie. Thank you for the invitation. I am glad to participate in this discussion.

Thank you! Briefly introduce yourself to our audience
My name is Anais Sandra Nguemto Guiawa from Cameroon. I am currently a Ph.D. candidate in Mathematics at the University of Tennessee Knoxville.

Quantum Machine Learning is one of the “hybrid” careers we have learned about. How did you join this career path?
I was born and raised in Douala, Cameroon. In high school, I took an interest in Math because I wanted to have a career in finance. I believed that being proficient in Math would make me better at finance. At university, I decided to do a bachelor’s degree in Math, with the hope of doing a master’s degree in financial engineering afterwards.

In 2014, I graduated with my bachelor’s degree from the University of Buea, as the top student in my class and the third-best graduating student in the whole university. During my bachelor’s degree, I became more curious about the applications of math in the “real world”. I thus went on to pursue a master’s degree in Applied Mathematics at Columbia University in the city of New York in January 2015.

I graduated with my master’s degree in May 2016 and worked as a financial analyst at Bayerische Motoren Werke AG (BMW) in North America until August 2017. After a while, I decided to go back to school and pursue a Ph.D. in Mathematics (applied mathematics), to further explore my interests in the applications of math. During my Ph.D., my interests have evolved from financial mathematics to the fields I am now working in, namely data assimilation and quantum machine learning.

What challenges have you faced along your journey in this hybrid career path of quantum machine learning?
My biggest challenge has probably been to pick something interesting to work on and stick with it. My interests tend to change quickly, but I am happy with what I am working on now.

Another challenge has been adjusting to a different country and culture while navigating life as a student and as an individual in general. I cannot say I am perfectly used to it yet, even after 7 years of living in the US.

How do you find the motivation to move forward when faced with challenges?
My desire to build a good career for myself (one where I get to work on things I am genuinely interested in) is what inspires me to move forward.

Tell us about your achievements, awards, and moments of recognition along this journey
I received both the Graduate Student Achievement Award (2022) and Graduate Student Award for Excellence in Qualifying Exams (2020). Before that, I received the prize of the Minister of Higher Education for the best student in Mathematics (2014) and the prize of the Vice-Chancellor for the best student in Mathematics (2014). In 2013 and 2014, I received the Presidential Excellence Grant.

What do you enjoy doing during your spare time?
Outside work, I like to nap, eat nice food, go out with friends when possible, and experience new things. I am also a big anime and Korean drama fan.

What’s your favorite quote?
Motivation will get you started; discipline will keep you going” ~ John C. Maxwell

How would you encourage a young African girl who is interested in Quantum Machine Learning?
You are absolutely qualified to be doing what you want to do, keep at your work, diligently, and great things will happen. Also, find yourself a community of like-minded and supportive people, it helps a lot.

Thank you, Sandra, for taking the time out of your busy schedule to speak with me. It is educative to learn about Quantum Machine Learning. Wishing you the very best in the remaining time of your Ph.D.

Share this article

Leave a Reply

Your email address will not be published.

You may also like