Most graduate students dive deep into their chosen field, striving to become an expert in a specific subject or process. But for Johns Hopkins alumna Shreya Chakraborty, Engr ’21 (MSE), it was the variety of her academic pursuits that paid off in her industrial career.
The broad array of research experience she gained during her master’s training—from pandemic response to orthopedic surgery—enabled her to pivot between professional interests, including cancer research, defense, and supply chain optimization.
“The freedom to be curious was one of the most treasured parts of my Hopkins experience,” Chakraborty asserts.
Today, she works as a machine learning engineer at CHAOS Industries, a startup company that develops defense technology. However, her research roots lie broadly in computational medicine. As a member of the Advanced Robotics and Computationally AugmenteD Environments Lab run by John C. Malone Associate Professor Mathias Unberath, Chakraborty helped build a comprehensive, county-level dataset of U.S. COVID-19 cases to model how countermeasures influenced the virus’s reproduction rate. For her master’s thesis, she explored the use of machine learning in computer-assisted surgery to predict surgical risks as part of a larger effort to improve the safety of complex orthopedic procedures.
Chakraborty’s fondest memories of Johns Hopkins involved exploration, from finding the perfect place to study on the quad as the seasons changed, to working part-time as a computer science student in a neuroscience lab, or even taking unconventional courses on psycholinguistics—the study of how humans perceive, produce, and acquire language.
Using her background in medical research, Chakraborty began her career after graduation developing machine learning models to detect cancer in tissue samples. Then, she made the leap across the country to Washington, where she found a passionate team at CHAOS Industries.
The jump from curing cancer cells to tracking aerial threats with radar might seem daunting, Chakraborty says, but she found a common thread in the work that motivates her: Both fields involve technically challenging problems with real-world constraints and high stakes.
At CHAOS, Chakraborty led the machine learning effort on a project that tackled a pressing supply chain shortage at a time when replacement parts are expensive and hard to find, using AI algorithms to identify and recover valuable components from discarded or damaged electronic systems. The project’s success in identifying damaged car parts from pictures and using retrieval-augmented generation to suggest alternate components earned the company a $1.7 million Small Business Innovation Research grant from DARPA.
Chakraborty’s success as a leader in part derives from her history of working on group projects, which she always viewed as an opportunity to work with students from other departments and gain their valuable perspectives on interdisciplinary problems.
She also credits her Johns Hopkins master’s experience with providing two vital tools: a strong technical foundation and the confidence to keep learning.
“Technology and the problems we work on are constantly changing,” she says. “Knowing how to adapt, stay curious, and find pockets of joy in your work is what ultimately helps you build a sustainable career in this field.”