What is your educational background?
I earned my bachelor’s degree in civil engineering and my master’s degree in mathematics from Birla Institute of Technology and Sciences, the #1 private engineering university in India. It's an integrated program, and I received both degrees in five years.
What did you do after that?
I was working as an analyst at My QM, a marketing intelligence company. I had started to gain experience with newer, far more sophisticated techniques that can be used to derive much more powerful insights out of data. Knowing that these methods are out there, and people can generate better qualitative insights, gave me that push to pursue data science. I wanted to expand the domain of my analytical thinking so I’m better prepared to help businesses make the decisions they need to succeed.
Why did you choose to come to Stevens?
Stevens has a beautiful campus, and it’s close enough to New York that also opens a lot of exciting opportunities. More importantly, I compared data science curricula, and the core Stevens courses perfectly cover everything that is needed for a data scientist in today's market. The electives are the cherry on the top, and you can customize them based on the direction that you see yourself in the next few years.
What excites you about data science?
You can know about science or mathematics, or even someone with simply a high school diploma can begin studying the fundamentals in data science. It bridges the gap among data engineering, data analytics, and software development. The more I learn, the more it fascinates me, because it's continuously being developed and improved. It’s never repetitive, and it challenges me to keep learning.
What are you researching for your thesis?
My thesis focuses on zero-shot learning. One of the biggest limitations of machine learning is that models are taught to predict output from a given input, but sometimes during testing you might not see the same data that you observed during training. Zero-shot learning helps in designing systems that are robust enough to make good predictions on data that the system has never seen before. It's an up-and-coming niche area within machine learning, so there’s limited literature, and it’s a fascinating area to explore. And I had never written a thesis before, so I’m also learning how to write literature papers. I’m going beyond improving my ability to solve problems, to be able to translate that in an understandable way.
How has your experience at Stevens prepared you for your next step?
The demand for data scientists is on the rise. Here at Stevens, I gained more than I expected, and I am more technically equipped than I was before. It’s helping me when I'm applying for jobs. Investing in my master’s at Stevens was definitely worth it.
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