"At Hopkins, I can connect people, data, and purpose"

Sristi Bafna, an M.S.E. student in Data Science at the Whiting School of Engineering, shares how her interest in language, cognition, and social equity led her to Johns Hopkins.

Why did you choose to study Data Science?

Data science is a natural fit for me because it brings together my interests in cognitive science, language technologies, and equitable systems design. I’m drawn to questions at the intersection of people, data, and infrastructure – how we can use technology to improve access, representation, and decision-making in complex real-world systems. With prior experience in impact measurement, data engineering, and socioeconomic analysis, I’m now looking to deepen my technical foundations and work on high-impact problems.

Why did you choose Johns Hopkins for your master’s?

Besides its global reputation, something specific that stood out to me about Hopkins was the dedicated session on Diversity and Inclusion for prospective applicants. As an international student and a woman in STEM, this session demonstrated how deeply they value every voice, and made JHU feel like the right fit for me.

I was also drawn to Hopkins’ strong research culture. I believe good research skills build acumen for driving high impact in the real world. Given my specific interests in language acquisition studies, I was eager to work at the Center for Language and Speech Processing, undoubtedly at the forefront of global research initiatives in this space. 

How does your past work connect with what you hope to do at Hopkins?

First, my capstone project during my undergrad focused on investigating whether language learning is driven by innate mechanisms (as proposed by Noam Chomsky) or statistical patterns. I experimented with acceptability judgements (an aspect of grammar acquisition) in artificial neural networks (ANNs). I am really excited to explore more aspects of this question and broaden my understanding of language at Hopkins. 

Second, my work at Karya as an Analyst in their Research and Impact team. Incubated at Microsoft Research, Karya is the world’s first digital data cooperative, aiming to bring dignified digital work to marginalised communities in India. I built a multidimensional index mapping socioeconomic background and well-being of gig‑workers in India.

I am eager to utilise my experience with applying data science for social impact and designing pipelines for citizen-facing data from marginalised communities in learning how to build scalable digital public goods during my time at graduate school. 

What areas in tech and data science are you most excited to explore?

I’m particularly excited to explore:

  1. Digital Public Goods and Infrastructure – Computer Science is positioned very uniquely as a discipline – principles in cybersecurity and cryptography inform the design and delivery of public goods and services. The Unified Payments Interface (UPI) in India is a golden standard in how such services and goods can transform financial independence and democratise payments in one of the world’s largest economies. I’m excited to further my understanding of what it takes to build such products that drive large scale change across economies.
  2. Understanding the language acquisition process – By comparing human language acquisition to LLM behavior, I aim to take on the challenging task of uncovering the cognitive processes that underlie both biological and artificial learning.

India’s tech landscape is changing fast. As a young woman in STEM, what systemic shifts do you hope to see in the next five years?

Women and non‑binary individuals remain underrepresented in STEM partly because they have fewer opportunities to share research and build networks – critical for career advancement. In India, 1 in 3 conferences had zero women speakers (Muralidhar & Ananthanarayanan, 2023). To address this, some essential steps in my opinion would be:

  1. Increase funding through targeted fellowships and grants to lower financial barriers and empower women‑led labs.
  2. Reform curricula at the school level to include hands-on, inquiry‑based STEM from early schooling and debunk notions about gender-linked abilities in certain streams.
  3. Create structured mentorship programs that pair non-cis male students with non-cis male role models, demystifying pathways into male-dominated fields like math, engineering, and more.

Building an equitable research culture requires both systemic support (funding, policy) and grassroots change (classroom practices, mentorship) in the country – important for all genders to thrive in STEM.