Tushar Agrawal completed his undergraduate degree in computer science at Birla Institute of Technology and Science in Pilani, India before enrolling in the M.S. in Data Science program at Columbia University. Today, he is a data scientist with IBM. Here, the 2022 alumnus discusses his interest in data science and his Columbia experience.
How did you become interested in data science?
My interest in data science stems from my curiosity to develop data-driven products to have a positive social impact. There is a huge availability of data, and I am passionate about how this information can be leveraged to identify and develop solutions for problems in health care and transportation and reduce inequality.
How did your undergraduate experience prepare you for DSI?
During my undergraduate studies at the Birla Institute of Technology and Science, I supplemented my core curriculum in computer science with electives—data mining, operations research, and linguistics. The interdisciplinary interaction between computer science and linguistics ignited my interest in natural language processing (NLP). Under the guidance of Siddhaling Urolagin, I utilized deep learning architectures along with NLP to begin developing a two-way sign language translation software for the deaf. Participation in such projects has helped me cultivate my creative and strategic thinking, along with the knowledge of developing data-driven products from concepts based on a user-centric design process.
Why did you choose to come to Columbia for the M.S. in Data Science program?
Primarily, I chose Columbia because its mission statement deeply resonates with my goal of leveraging data-driven strategies and products to have a positive social impact. It also has a great data science curriculum, which covers the breadth of the data science field and the option to dive in-depth into topics such as NLP, deep learning, computer vision, and statistical and causal inference. Programs like the DSI Scholars Program and Campus Connections are great opportunities to utilize learned knowledge in projects across schools. I had the pleasure of working with Indrani Pal on Hydrodetectus to forecast water over long-term timetables by using artificial intelligence approaches to monitor surface water across California.
Which internship opportunities have you had during your DSI studies? How did your internship experience(s) change or challenge you?
In Summer 2021, I worked at the United Nations to drive on-ground impact and execute data-driven strategies such as hotspot analysis to resolve electoral conflict. I devised the roadmap for redesigning the Kenya Crisis Risk Dashboard (CRD) data product, to develop CRD as an analytical tool to strengthen early warning and early response. Incorporating feedback from multiple stakeholders, including the United Nations Development Programme and the Office of the High Commissioner for Human Rights, taught me how to manage the expectations of stakeholders while using machine learning to answer questions and drive the product vision.
Tell us about your capstone project.
I worked with Johnson & Johnson, DSI’s industry affiliate. We focused on two important problems: predicting loss of exclusivity (LOE) in the drug life cycle and quantifying impact of LOE on sales, market share, and future cash flows. We explored multiple modeling approaches and worked on a natural language information extraction pipeline to allow the company to transform unstructured PDF reports into structured datasets. We came up with an extensive quantitative analysis framework that can be used to analyze LOE in future drugs. This was a steep learning curve and a great collaborative learning experience with my peers, including Saloni Gupta, Smarth Gupta, and Moulay-Zaidane Draidia.
How did the pandemic impact your experience during the M.S. in Data Science program?
I lived in New York, right next to campus, and attended certain classes that were offered occasionally in-person. Through virtual hackathons, online meet-ups, and reaching out over social media platforms, I connected with peers and made good friends. The professors also provided a flexible schedule and were available throughout the day, which enhanced the learning experience.
What did you gain during your Columbia experience?
Curiosity and Growth Mindset: There is always something new to learn at the DSI with interactions with new people and opportunities to volunteer and contribute to Data for Good projects.
Leadership and Responsibility: Columbia DSI provided me with a plethora of challenges and opportunities to grow as a leader, including co-chairing the Interschool Governing Board to provide equal funds and recognition access to student clubs across the university and representing the university in the Humana-Mays Healthcare Analytics Case Competition.
A network of friends and mentors: This is something that I’ll cherish most. Columbia DSI has provided me with an opportunity to form lasting relationships with a wide variety of people. I value the immense learning that I have gained and will continue to gain over the upcoming years through my interaction with these people.
This interview has been edited and condensed for clarity.