Interviewer: Akanksha Agarwal
What is your background?
I am Pravallika Padyala, a 16-year old who is really passionate about medicine and data science. My Omdena journey consists of working on a Malaria project with startup Zzapp. I am currently working on a cancer project with RebootRx. On the same note, I have presented and published Alzheimer’s research and Northwestern University and Illinois State University. This is my second year conducting research related to medicine at Argonne National Laboratory. I hope to pursue a career in the medical field, helping the impoverished reach quality medical care. I believe that our future holds so much potential to develop medicine with AI and Machine Learning.
When did you first realise your passion for data science?
I realized my passion for data science after I took college level statistics. Furthermore, I understood that the concepts are used everywhere in our real world. From counting jelly beans in a jar to saving many lives from dreadful diseases. At first, I really struggled to understand how statistical data is calculated and used. However, now I can easily be able to identify what the terms are. As well as when to use the models and math towards practically anything and everything.
Did you face any setbacks in your journey thus far? How did that affect you?
I have faced many setbacks in my journey. From learning about what I have interests in, to communicating effectively to make a difference in our world. I remember my first Omdena project was really tough for me because I have never worked on any prior AI/ML project. Nor had domain expertise. Through struggles and various helping hands I was able to pick myself up and learn so much. It truly has shaped me to be who I am and what I do. Without these experiences and struggles, I would not be the independent and perceptive ML Engineer I am.
What has been your most memorable project so far? Why?
My very first project was “Preventing Malaria Infections Through Topography and Satellite Image Analysis.” Currently I am involved in “Improving the Lives of Cancer Patients by Identifying Existing Non-Cancer Generic Drugs.” I feel that the Malaria project was my most memorable project because it really challenged me to be able to step out of my comfort zone. It taught me to ask questions, understand technical skills, and get used to failure. Yes, get used to failure. Being a task manager and guiding collaborators to a consolidated solution towards the project was very challenging, but with the help from Lead ML Engineers and Lucie Schnitzer, I was able to learn from my mistakes and improve upon them.
How have you approached your previous projects?
My pet peeve when it comes to projects is to be able to absorb the information given and ask lots of questions. No question is a dumb question, we are all learning and understanding what the deliverables are. Another approach is to always get to know your collaborators. They are the ones you will be bouncing ideas with, understanding the problem in the project and having a fun time with. I really like to get to know who I am working with so that I know what makes them comfortable, their skills, and how we can help each other through the 8 weeks. I also like getting in touch with the Project Manager because if anything arises and I need non-technical help, they lend a hand in tackling the situation.
Where do you see yourself in the future?
Ever since I was a young child I have always wanted to have a career in medicine, precisely cardiology or neurology. I also hope to build AI solutions for medicine. In specific, change the way our world looks at the health of the human body. Additionally, I want to aid the impoverished with medical needs by travelling and working with a team to save one life at a time.
What is the youth population spread like in Data science?
I am the youngest to work in both projects. I see many collaborators are in college and are participating in these projects to gain knowledge and expand their understanding in AI. Moreover, I feel like a lot more adolescents should get involved in Data science and understand the potential it has in the next 10, 15, 20 years! Many jobs and academic studies will be oriented with data and how to create solutions with the statistics.
Why do you think it’s important for more young people to explore data science?
It is critical for the youth to explore data science because it could be used practically in every field, everything uses data and mathematics to reach quality results. Data science is an addendum of a multitude of data analysis fields such as statistics, predictive analysis, data mining, and many more. The growth and functioning of major corporations all rely on data science to fascinate our minds.
What would be your advice for young people that want to pursue data science?
Data science is more than just numbers in a table or a graph. Sometimes what you are dealing with is qualitative rather than quantitative, understanding the difference and being able to still apply what you would do the numbers is critical. The field requires one step at a time to reach the level you are expected to reach, it is not something that will come overnight or in a jiffy. Putting yourself in interesting and challenging analytic work will motivate you a lot. Thus you are more likely to want to reach your goals and solve the underlying issue.
What has been your experience with Omdena, and how has that impacted you?
My experience with Omdena can not be described by a few words on a paper, I would rather say it is an experience and emotion by itself that is very different from any other. The number of people I met, the number of opportunities I have come across, and the number of skills I have gained is more than a lot. I am glad I have worked hard to gain this position and express who I am through the projects I partake in, and the connections I build.
Building AI Solutions for Real-World Problems