Emily: Navigating STEM at MIT

When I sat down with Emily Gan, an undergraduate and master’s student in EECS (Electrical Engineering and Computer Science) at MIT, I expected a structured discussion about research and academia. What I got instead was a conversation that felt more real, more candid, and more reflective of what it means to be a student navigating one of the most intense academic environments in the world.

She wore a dark green sweater layered over a collared shirt, silver half-hoop earrings glinting as she gestured, her red-painted nails tapping lightly against the table. With an effortless, “whatever” kind of attitude, Emily exuded the self-assurance of someone who had figured things out—but not in a straight line.

Curious about other women in STEM?

I spoke to several of them. It's worth listening to what they have to say.

Check it out

emily gan

The Unexpected Journey from Bio to Computer Science

"Did you always know you’d end up in EECS?" I asked.

Emily shook her head, a small laugh escaping. “Nope. I actually started in bioengineering—Course 20.”

Like many students, she arrived at MIT thinking she had her future mapped out. Synthetic biology, molecular research, and biotech innovation seemed like the dream. But reality hit quickly.

"I realized bio research moved way slower than I expected. The methods I was using to get results were improving faster than the actual biological discoveries themselves. That frustrated me," she admitted.

So, she shifted—to Course 6-7 (Computer Science and Molecular Biology)—a hybrid major that blended computation with bio. But even that wasn’t quite right.

"I realized I liked the CS part way more than the molecular bio part," she said simply. The problem-solving, the efficiency, the speed of iteration in software—it all appealed to her more than waiting weeks or months for wet lab results.

Eventually, she moved fully into Course 6-3 (Software Engineering). The transition wasn’t planned—it was a series of realizations, small shifts that led her to where she actually wanted to be.

When Machine Learning Didn’t Click

Given MIT’s reputation for AI and machine learning, I assumed Emily was drawn to it. But she quickly set the record straight.

"I took an ML class sophomore or junior year, and there was this whole section on why neural networks work. The professor basically went, ‘Here are some theories, here’s why they’re wrong, but we’re not really sure, whatever.’ That turned me off.”

She preferred math with a strong foundation, statistics where she could see the reasoning behind the conclusions. That’s what led her to Professor Devavrat Shah’s lab, where she started working on stochastic systems, inference problems, and data optimization.

"Most of my research is on matrices and data compression. We’re trying to find ways to compress data efficiently and make it run parallel on multiple chips," she explained.

When I asked for more details, she shrugged. “It’s not that I don’t care—it’s just kind of a pain to explain,” she admitted. That response alone summed up her research style: technical, structured, but not overly romanticized.

The Reality of Research at MIT

MIT’s research culture can seem overwhelming, but Emily made it clear that opportunities don’t just fall into your lap—you have to go get them.

"You cold email professors, you ask for a shot. No one’s going to come to you with a research opportunity. You have to be the one who reaches out."

That’s exactly what she did. She emailed multiple professors before finding a spot in Professor Shah’s lab.

And once she was in? It was a lot of work at a computer, not much in a traditional lab space. Her research was focused on matrix completion, singular value decomposition (SVD), and optimizing numerical stability. She spent an entire semester rewriting an algorithm in C to improve parallel computing efficiency—only to realize that numerical stability issues made the whole approach unfeasible.

"MIT research is like that. You try things, fail, pivot, and keep going," she said.

Academia vs. Industry: Choosing Finance & Software

For a long time, Emily thought she would stay in academia.

"I liked research, and MIT makes you feel like that’s the natural next step," she said.

But internships changed everything. She started with software engineering, then tried finance—and surprisingly, she loved it.

"I’m going into software for finance. Goldman Sachs. I know it’s not what most people expect from an MIT research student, but I actually really like it. Quantitative finance is just another optimization problem. It’s very stats-heavy, very data-driven. It makes sense to me."

She paused before adding, “Also, the paychecks aren’t bad.”

She laughed, but it was a real factor. Academia had its appeal, but finance offered fast-paced problem-solving, real-world impact, and financial security.

MIT Culture: Self-Driven, but Not Cutthroat

I asked her about MIT’s student culture—was it as competitive as people say?

"It’s intense, but it’s not cutthroat," she corrected. "Most people are just super self-driven. Even if you don’t know what you want to do, you’re surrounded by people who are obsessed with their work. That energy rubs off on you."

MIT also offers a lot of freedom to mix disciplines. Some of her friends combined CS with entrepreneurship, others with neuroscience, and some even with finance, like her.

"It’s not like you have to know your path from day one. A lot of us figure it out as we go," she said.

What She Wishes She Knew in High School

Before we wrapped up, I asked Emily what advice she would give to high schoolers—especially women—who want to pursue STEM at MIT.

1. Don’t be afraid to pivot. Your first major, research project, or career interest doesn’t have to be your last. It’s okay to change paths.

2. Cold email professors. Research doesn’t just come to you. If you want opportunities, you have to ask for them.

3. Grades aren’t everything. MIT values depth of understanding over a perfect GPA. Employers and professors care more about what you can do than about straight A’s.

4. Try industry internships. Even if academia seems like your goal, testing out software, finance, or other industries will help you make an informed choice.

5. Make time for social life. She started college during the pandemic and missed out on the early social scene. If she could go back, she’d invest more time in extracurriculars and student clubs.