Women in STEM: Advocacy Through Action

The best mentor I had sent me a specific fellowship: the link, the reason she thought I would fit, and encouragement to apply. I would not have known to look for it.

That is the kind of support I remember. As a woman from Nepal working in tech in the US, I have needed help making unfamiliar opportunities concrete. I want to talk about the actions that have helped me, rather than add another abstract call for mentorship.

The nomination gap

There is a difference between telling someone to keep an eye out and putting their name forward for an opportunity. Encouragement leaves the search with the person receiving it. A specific recommendation gives them a place to start.

That's the structural intervention, and it's simple enough that it's embarrassing how often it doesn't happen: when you're in a position to recommend someone, recommend women by name for specific opportunities, without waiting to be asked. Not "let me know if you want me to connect you to anyone." An actual email. An actual name attached to an actual opening. This costs maybe 20 minutes and changes the math for the person on the other end.

The problem with generic programs

Being both a woman and an immigrant from Nepal makes generic "women in STEM" programs a fairly poor fit for me, and I think this is worth saying plainly.

Many programs designed to support women in STEM were built around a US-default experience. They assume background knowledge I didn't have: how to navigate American academic institutions, what professional norms look like in US tech companies, how to read and write a US-style CV, what the unwritten rules are around negotiation and self-promotion in an American professional context. These things are knowable, but they require a separate layer of translation work that the program never addresses because it doesn't know it needs to.

Programs designed for "women in STEM" as an undifferentiated category are also implicitly designed for whoever's closest to the center of that category. The more an immigrant woman's experience diverges from the assumed baseline, the more she's doing invisible translation work to adapt general advice to her actual situation. The work exists. The program just doesn't see it.

This is a design problem. The more precisely you can answer "who exactly are we serving, with what specific background, facing what specific barriers," the more likely the program is to actually work for those people. Vague beneficiary definitions produce programs that feel broadly supportive and produce specific results for a narrow slice of the people they claim to serve.

Visibility versus encouragement

I've mentored students who were technically strong but genuinely uncertain whether they belonged in this field. Encouragement is worth something. Telling someone directly that they're good at this is true and should be said. But in my experience, encouragement alone doesn't change much for someone who's uncertain about whether the path is open to them.

What moves the needle is visibility. Proof that people who look something like them are doing the work they want to do, that the path exists and isn't theoretical. An introduction to someone working in the area they want to work in does more than a conversation about believing in yourself. The introduction makes the future concrete. It gives them a person to email, a name to mention, a door that isn't just metaphorical.

This is a small thing. It takes an hour to make a meaningful introduction. It gives the student a concrete next conversation, rather than another instruction to believe in themselves.

The work itself as advocacy

The most direct thing I can do is check my own systems for the gaps I've had to navigate.

On an earthquake damage-prediction project, geographic identifiers were important predictors, but their anonymization limited the spatial context we could inspect. That gave me a reason to ask more careful questions about coverage and performance. It did not establish a measured rural-versus-urban disparity.

Catching a gap like that, if it exists, requires someone who goes looking for it in the first place. You have to know what to look for. You have to treat the model's performance on underrepresented subgroups as a signal rather than as an acceptable error rate. I run this check deliberately: testing on data that represents more than the people I already know, treating edge cases as important information about what the system can't do. When a model performs worse on a particular demographic slice, that's a finding, not a rounding error.

This is a concrete argument for representation in technical roles that sits alongside the fairness argument. Fairness matters. But there's also a quality argument: systems built by people who've been on the wrong side of bad assumptions catch more of the bad assumptions. The two things aren't separate.

What I don't have a clean answer for

I don't have a tidy solution for systemic underrepresentation in STEM. The mentorship helps, but it helps at the margins and it doesn't scale cleanly. Visibility helps. Building better systems helps. None of it is fast, and most of it isn't measurable in a way that satisfies anyone looking for a quarterly metric that proves the problem is being addressed.

The support that has stayed with me combined encouragement with something specific. It was never general support or an open-door policy; it was an introduction, a nomination or an email sent before I asked, something with a name and an action attached.

That is a genuinely low bar. The gap between expressing support and doing something specific is large, and most well-intentioned advocacy lives in it. Crossing it doesn't require a program or a budget. It requires deciding that when you're in a position to nominate someone by name, you do it. That's the whole thing.