When I sat down for a Zoom conversation with Jessy Han, a PhD student in Electrical Engineering and Computer Science (EECS) at MIT, I was immediately drawn into the depth of her research. Unlike many computer science researchers focused purely on theory, her work sits at the intersection of technology, social science, and policy—tackling issues of racial disparities, police surveillance, healthcare inequities, and ethical AI.

Jessy’s journey to MIT was shaped by an early interest in both computer science and applied mathematics. Her undergraduate years at Columbia University, where she majored in CS with minors in economics and applied math, reinforced her interdisciplinary approach to technology.

"I started research in my junior year, working on social network algorithms—how to diffuse information more effectively and fairly," she explained. "But I realized I was more interested in applying CS methods to real-world datasets, particularly to problems related to inequality and social justice."

That realization led her to pursue a PhD straight after undergrad, shifting her focus from social networks to causal inference in social sciences, criminal justice, and healthcare.

From Social Networks to Causal Inference

One of the biggest transitions in Jessy’s research journey was moving from studying information diffusion on social networks to applying causal inference to social science.

"Even though my research focus changed, the underlying theme remained the same," she told me. "I’ve always been interested in using computer science to address real-world inequalities—whether that’s racial disparities in law enforcement, healthcare treatment differences, or resource allocation."

Causal inference, which helps establish cause-and-effect relationships from data, became her key research tool. She now applies it across multiple domains:

Criminal Justice – Studying racial disparities in police interactions, surveillance, and recidivism.
Healthcare – Examining the effectiveness of different cancer treatments and patient survival rates.
Ethical AI & Decision-Making – Investigating how AI can be used responsibly in high-stakes policy areas.

Her work isn't just about building algorithms—it’s about ensuring that these algorithms make fair and equitable decisions.

From Industry to Academia: Choosing the Research Path

While many computer science students aim for lucrative industry roles in tech companies, Jessy took a different route.

"I did some software engineering internships, but I realized I didn’t enjoy them as much," she admitted. "In industry, you’re assigned a project based on the company’s priorities—usually profit-driven—and you don’t have much say in what’s important."

The fast-paced, results-driven nature of corporate work didn’t align with her desire to deeply explore questions and work on problems with long-term social impact.

"In research, I have the freedom to choose my projects and shape their direction. If something doesn’t work in a few months, I don’t just drop it—I can pivot and refine the approach."

That sense of intellectual freedom, she explained, is what draws her to academia and fuels her passion for research.

Tackling Racial Disparities in Policing

One of Jessy’s most notable projects involved studying racial bias in law enforcement, particularly in police interactions.

"Traditional research in this area mostly focuses on the ‘stop-and-frisk’ stage—when an officer stops and questions a civilian," she said. "But we realized that’s only part of the picture."

Her team developed a multi-stage framework that analyzes law enforcement interactions from the very first point of contact—911 calls.

"If we only look at stop-and-frisk data, we might miss the fact that racial bias starts even before that—when someone decides to call 911 in the first place," she explained.

Their framework allows researchers to track interactions from emergency calls to arrests, helping capture systemic biases that might be invisible in traditional studies.

Her work involved analyzing police data from New York City, Seattle, and New Orleans, leading to multiple research papers and ongoing projects.

Interdisciplinary Challenges: Communicating Across Fields

Because her research spans computer science, political science, and healthcare, Jessy constantly collaborates with experts from different fields—which, she admitted, comes with challenges.

"When working with doctors on cancer research, for example, they’re experts in medicine, but they don’t always understand machine learning," she said. "And I don’t have medical training, so I have to explain my methods in ways they understand while learning from their expertise."

That ability to bridge disciplines is what makes her work impactful—but also what makes it difficult.

"Interdisciplinary research requires you to step outside your comfort zone and learn how to speak the language of different fields," she reflected. "It’s not just about writing code—it’s about understanding the context behind the data."

Ethical AI and the Future of Technology

Given her focus on AI and decision-making, I asked Jessy about her views on the ethical challenges of artificial intelligence.

"AI is increasingly being used in high-stakes decisions—hiring, criminal sentencing, healthcare," she said. "If we’re not careful, biased AI systems can reinforce existing inequalities."

One of the biggest challenges, she noted, is the gap between research and real-world policy.

"As researchers, we develop algorithms to make decisions fairer—but how do we convince policymakers to actually use them?"

Bridging that gap, she believes, is one of the biggest challenges in ensuring AI is used responsibly.

Advice for Aspiring Researchers

For high school and undergrad students interested in CS research, Jessy offered two key pieces of advice:

Build a Strong Foundation "Math and coding skills are critical. It takes time to build them, but they form the foundation for everything else."
Be Curious and Open-Minded "Try different things. Take courses in different areas. You won’t know what excites you until you explore."

She also encourages students to experiment with both research and industry internships before deciding on a career path.

"I tried both research and industry. That’s how I realized I preferred academia," she said. "You don’t know what you like until you try."

Navigating STEM as a Woman and Building a Support Network

As a Chinese woman in a male-dominated STEM field, Jessy has encountered subtle forms of exclusion.

"It’s not overt discrimination, but there are moments—like being the only woman in a room full of men—where you feel left out of conversations," she said.

To combat this, she helped create a women-led support group for Chinese women in STEM, providing a space for mentorship and shared experiences.

"Having a support network is crucial. Sometimes, you have to be proactive—speak up, reach out to others, and create your own community."

Looking Ahead: The Future of AI, Research, and Teaching

Now nearing the end of her PhD, Jessy is thinking about the next step.

"I’m considering faculty positions, but I know they’re competitive. My main goal is to continue doing research."

Her thesis focuses on using causal inference to improve decision-making across criminal justice, healthcare, and business.

As she wraps up her PhD, she remains deeply committed to research that not only advances technology but also benefits society.

"At the end of the day, I want my work to have real-world impact," she said. "Technology isn’t just about algorithms—it’s about how those algorithms shape people’s lives."

Jessy Han’s story is one of intellectual curiosity, interdisciplinary collaboration, and a commitment to using technology for social good.

Her journey is a reminder that computer science isn’t just about writing code—it’s about solving real-world problems that affect real people.