At Atlas AI, we believe that in order to achieve our mission of improving lives in the most vulnerable parts of the world, we need to build an inclusive and diverse team. We’re thrilled to have five women to date on our data engineering team who are driving our innovation and projects forward. To celebrate them and in hopes of inspiring other women in this field, we are sharing a little bit of their story with you.
Introducing Priyadharshini Tamizharasi Suresh
Priya is a Machine Learning Engineer on the Innovation team where she works on machine learning and computer vision problems that help track and analyze socio-economic growth in the developing world. She loves to focus on technologically challenging problems that are driven by public benefit and sustainable development goals. Before joining Atlas AI, she worked on computer vision problems at a non-profit called Greenstand. Prior to that, she pursued her Masters in Electrical Engineering at Pennsylvania State University where she specialized in AI and computational science. She earned her Bachelor’s degree in Electronics and Communication Engineering from Anna University, India.
How and why did you choose to become a ML Engineer?
I started out as an electronics and communications engineering student. While I always knew I wanted to pursue a field that’s founded in mathematics, it was during my undergraduate degree that I began to really enjoy programming. It was not until my third year, when I took courses on advanced statistics and calculus, that I realized data science was a field that was an amalgamation of all things I loved. Even though I got some foundational knowledge of the field during my undergraduate studies, it was during my masters at Penn State that I started to develop an in-depth understanding of the field and it’s wide range of applications.
What is a typical day like for you?
I spend most of my day coding our machine learning pipelines. We use various tools and technologies like Google Cloud services, Earth Engine, geospatial packages like gdal and rasterio. There is a learning curve associated with each of these tools and that takes up some of my time. I also spend a significant amount of time perusing relevant scientific works and thinking about how we can adapt those ideas to improve our methods.
What’s something you recently learned in the field of Data Engineering?
I’m constantly learning on my job, such as how to better utilize a certain tool in order to achieve better performance or enable more efficient memory usage or best data science practices. On a much broader level, the very idea of using geospatial data to make more informed economic decisions and realize sustainable development goals is something I have learned on the job and find very interesting!
What were the biggest obstacles you had to overcome because you are a woman?
During my limited experience over the past 1.5+ years, I have been fortunate to be in very inclusive and welcoming environments where my colleagues treat me as their equal. Despite being in such positive environments, I do find myself hesitating to speak up and voice my opinions. This could change as we start to have more gender balanced teams. It’s important to remind ourselves that our opinions matter. My dad often tells me “confidence matters just as much as competence”.
Which changes are needed to be more inviting to women to join and stay in science?
It would be helpful to have more women as keynote speakers and presenters in conferences and seminars. Having them talk about their journey and their technical expertise, could inspire and give younger women the confidence to pursue similar fields. Having more women come forward and talk about their technical findings could also change this general perception that men are better suited for data science jobs and hence help influence hiring decisions and workplace cultures. Another great motivator is seeing women in leadership positions. It’s a sign of an inclusive and diverse workplace and assures you of an opportunity for growth and development.