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Persistence in technology is not simply the ability to endure bias. It can mean continuing a line of research, recovering from a failed project, changing fields, building a company, or leaving a workplace that will not change. The most useful stories show both what women made and what made their work possible.
This updated companion to EE Times’ November 17, 2017 feature spans computing history, semiconductors, AI, security, and access to technology. These 25 profiles are not a ranking or a claim that one career path fits everyone. Together, they show why individual determination matters—and why it cannot substitute for fair institutions, sponsorship, resources, and credit.
Why these stories matter now
Women’s participation remains uneven across technology. The World Economic Forum reported that women represented 28.2% of the global STEM workforce in 2024, up from 26.1% in 2016; that measure is not the same as the share of all technology workers. UNESCO reports that women make up about 30% of AI professionals, with lower representation in AI leadership. UNESCO also estimates women’s participation in AI-inventor activity at about 37% of patents filed in 2022–23. That is participation in patenting, not a claim that women were sole inventors or patent owners. These figures cover different populations and should not be compared as if they were one measure. (WEF report; UNESCO.)
Company data also need context. AnitaB.org’s 2023 Top Companies analysis covered 40 participating companies and 198,049 technologists; it is a defined sample, not a census of the industry. Its findings illustrate why representation, hiring, promotion, and retention must be considered together. (AnitaB.org methodology and findings.)
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“Persistence” here does not mean putting up with discrimination indefinitely. Resilience is coping with difficulty; persistence is continuing toward a goal; endurance can become mere tolerance of harm. Structural change means reducing the harm for people who come next. Sometimes persistence means staying. Sometimes it means redirecting a career, starting something new, or leaving.
25 profiles across technology
Foundations: computing was never a one-person story
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Ada Lovelace — imagining computation beyond arithmetic
In her notes on Charles Babbage’s proposed Analytical Engine, Lovelace described a method for calculating Bernoulli numbers and considered how a general-purpose machine might manipulate symbols. Calling her simply “the first programmer” can obscure the distinction between writing an algorithm for a proposed machine and running software on a modern computer. Her significance lies in the breadth of her thinking: computation could be used for more than numerical calculation. Her example asks technology history to recognize conceptual work, not only machines that were ultimately built.
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Grace Hopper — making programming more legible
Hopper helped develop early compiler technology and became an influential advocate for programming languages that let people express instructions at a higher level than machine code. Her work helped establish a direction for software development: make computers easier to instruct, maintain, and use. Her career also demonstrates that technical contribution and institutional leadership can reinforce one another. The lesson is not that every engineer must become a public figure; it is that technical standards and tools are shaped by people willing to argue for a different way to work.
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Katherine Johnson — mathematical computing in aerospace
Johnson’s calculations at NASA contributed to the analysis of aircraft and spaceflight trajectories, including work associated with early crewed missions. Her story is a reminder that computing has long included mathematical labor carried out by people whose work can be hidden behind a mission’s public-facing hardware. Recognition matters, but so does accurately naming the expertise involved: rigorous mathematics, checking, and the ability to reason about complex systems under demanding conditions.
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Annie Easley — computation, energy, and public service
Easley worked as a mathematician and computer scientist at NASA and its predecessor organization, contributing to projects involving launch vehicles and energy technologies. She also became known for encouraging students and advocating for equal employment opportunity. Her career crosses disciplines that are too often separated in popular accounts: software, numerical analysis, aerospace engineering, and energy research. It shows how a technical career can deepen over time while making room for public service and advocacy.
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Evelyn Boyd Granville — mathematics across computing and education
Granville’s work included mathematical computing and aerospace-related projects, followed by a substantial career in teaching. She represents a route through technology that does not end at one employer or one job title. Research, applied mathematics, and education all contribute to the technical workforce. Her path also challenges the assumption that teaching is a retreat from technical work: preparing others to reason quantitatively is part of sustaining a field.
Engineering, hardware, and systems
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Margaret Hamilton — software as mission-critical engineering
Hamilton led the software engineering work at MIT Instrumentation Laboratory associated with the Apollo missions. Apollo’s flight software had to handle limited resources and demanding operational conditions; software could not be treated as an afterthought to hardware. Hamilton’s work helped make the discipline of software engineering visible as engineering. Her profile is a useful corrective to stories that celebrate a launch but leave out the teams designing, testing, and safeguarding the systems that made it possible.
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Radia Perlman — designing networks that can scale
Perlman’s work on the Spanning Tree Protocol addressed a practical problem in bridged networks: how to prevent loops while allowing resilient connections. Her contributions to networking and network design are often reduced to a catchy label, but the deeper achievement is a body of work that helped make large, dependable networks possible. The lesson for aspiring technologists is that foundational infrastructure may be less visible than consumer products while shaping how nearly everything else operates.
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Sophie Wilson — processor architecture and efficient computing
Wilson co-designed the architecture behind the Acorn RISC Machine, the lineage that became ARM. Processor design connects abstract instruction sets to real constraints such as power, performance, and manufacturability. Wilson’s contribution is a reminder that the devices people carry depend on decades of engineering choices made far below the application layer. Hardware innovation often comes from disciplined attention to constraints, not just the pursuit of more computing power.
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Lisa Su — semiconductor engineering and company leadership
Su trained as an electrical engineer and built a career in semiconductor technology before becoming a prominent corporate leader. Her public role illustrates the importance of technical credibility in industries where executive decisions shape research priorities, product road maps, and manufacturing commitments. Leadership is not itself proof of technical achievement; the useful question is how engineering knowledge informs strategy and how an organization sustains difficult product and technology cycles.
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An Steegen — turning semiconductor research into manufacturing capability
Steegen’s career in semiconductor research and technology leadership highlights the bridge between scientific advances and processes that can be manufactured reliably. Chipmaking depends on long, collaborative chains of materials science, equipment, process control, and design. This kind of work is easy to overlook because its success is measured by what can be produced consistently at scale. Her profile makes visible the technical leadership required to move discoveries out of the lab and into industrial systems.
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AI, data, and accountability
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Fei-Fei Li — building the data foundations of computer vision
Li’s research leadership in computer vision includes the creation of ImageNet, a large labeled image dataset that helped accelerate progress in visual recognition. Data sets do not merely feed algorithms: their scope, labels, and omissions influence what systems can learn and how performance is assessed. Li’s work and public engagement also underline that AI is not a disembodied mathematical breakthrough; it depends on research infrastructure, human labor, and choices about how technology should serve people.
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Daphne Koller — connecting machine learning, biology, and education
Koller’s work spans machine learning, computational biology, and online education. In computational biology, statistical methods can help researchers find patterns in complex biological data; in education, online platforms can widen access while raising questions about completion, support, and quality. Her cross-disciplinary career shows that technology’s boundaries are porous. A technical foundation can open paths into fields where the central problem is scientific, social, or educational rather than a conventional software product.
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Joy Buolamwini — testing who facial analysis works for
Buolamwini’s research and advocacy called attention to disparities in commercial facial-analysis systems, helping bring algorithmic bias into broader public debate. Her work demonstrates the value of asking not only whether a model performs well on average, but on whom it fails and who bears the consequences. Auditing is not a substitute for responsible design or governance, but measurement can make otherwise hidden failures harder to dismiss.
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Timnit Gebru — asking how data and research power are governed
Gebru’s research has addressed computer vision, dataset bias, and the social effects of AI systems. Her work helped broaden debate about the documentation of training data and the responsibilities of institutions that build large models. Her departure from Google in 2020 became a public dispute; accounts of the events differ, so it is better understood as a contested episode than reduced to a simple causal story. The enduring issue is how research organizations handle dissent, publication, and accountability.
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Rediet Abebe — algorithms and social impact
Abebe’s research examines how algorithms and computational methods can address social and economic problems, including questions of inequality and access. This work pushes computer science to evaluate systems in context: an optimization objective may be mathematically clean while producing unequal outcomes in the world. Her career is a model for researchers who want technical rigor and social relevance to coexist rather than treating fairness as an afterthought appended to a finished system.
Security, reliability, and digital trust
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Katie Moussouris — making vulnerability disclosure a process
Moussouris has worked on vulnerability disclosure and bug-bounty programs, helping organizations create clearer ways for security researchers to report flaws and for companies to respond. Security depends not only on finding vulnerabilities but on building a channel through which findings can be handled constructively. The wider lesson is institutional: a policy that rewards responsible reporting can reduce friction between defenders and independent researchers, while vague or punitive processes can discourage useful disclosure.
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Parisa Tabriz — security engineering for everyday browsing
Tabriz is known for browser security work and engineering leadership. A browser is an everyday gateway to banking, communication, work, and public services, so its security affects people who may never think of themselves as technology users. Her field illustrates a distinctive kind of persistence: continually anticipating how systems can fail as software, attackers, and user needs change. Security improvements are often invisible when they work, which makes clear credit and sustained investment especially important.
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Niloofar Howe — cybersecurity entrepreneurship and investment
Howe’s career has included cybersecurity entrepreneurship and investment. Security products must address real operational risks while fitting into organizations’ budgets, workflows, and threat environments. The intersection of technical understanding and capital allocation matters because promising security work can stall without customers, funding, or a path to deployment. Her profile points to a broader ecosystem lesson: the people deciding what gets financed and adopted shape the security of systems used by everyone.
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Kimberly Bryant — expanding access to computing education
Bryant founded Black Girls CODE to create technology learning opportunities for Black girls, addressing an access problem before it becomes a hiring statistic. The organization’s work treats early exposure, peer community, and visible role models as parts of technical opportunity. Education initiatives cannot by themselves repair inequities in hiring or workplace culture, but they can help ensure that talent is not overlooked simply because a student did not encounter coding, mentors, or a welcoming learning environment early enough.
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Reshma Saujani — making participation a public priority
Saujani founded Girls Who Code, building a broad effort to increase girls’ participation in computing. Her contribution is organizational and civic as well as educational: she helped put the question of who gets to learn computing into public conversation. Programs that teach coding are most effective when connected to sustained support, good instruction, and routes into further study or work. The lesson is that access is not a single workshop; it is a pathway that institutions have to maintain.
Building fields, companies, and pathways
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Adele Goldberg — shaping ideas about interactive computing
Goldberg’s work at Xerox PARC contributed to Smalltalk, an influential programming language and environment associated with object-oriented programming and graphical interfaces. The significance is not a simplistic claim that one person invented modern personal computing, but that research environments generate ideas that later travel through products, teams, and institutions. Her story invites a fuller accounting of innovation: who develops concepts, who adapts them, and whose contribution is remembered.
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Karen Spärck Jones — language technology and information retrieval
Spärck Jones made foundational contributions to information retrieval and computational linguistics, including the inverse document frequency idea used in assessing how informative a term is across documents. Search and language systems depend on methods for representing relevance, not just on faster machines. Her work shows that ideas from mathematics and linguistics can become core infrastructure for how people find information, and that foundational contributions deserve visibility even when they are embedded in later systems.
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Arlan Hamilton — widening who can fund technology companies
Hamilton built Backstage Capital with a focus on founders from groups often overlooked by conventional venture funding. Investment is not a technical contribution in the narrow sense, but it affects which technical ideas receive time, teams, and a chance to reach users. A fair account should distinguish access to capital from product engineering while recognizing that both shape the technology ecosystem. Her profile raises a practical question for investors: which promising founders never make it into the room?
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Mira Murati — product leadership during a major AI transition
Murati became widely visible through AI product leadership at OpenAI during the rapid public adoption of generative AI. The significance of this kind of role lies in translating research into products while confronting questions about deployment, safety, reliability, and governance. Titles and affiliations change quickly in this sector, so her profile is about the responsibilities of AI product leadership rather than a claim about a current position. Technical progress alone does not answer how systems should be released or used.
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Mary Jackson — aerospace engineering and opening doors
Jackson became NASA’s first Black female engineer and worked in aeronautical engineering, while also advocating for women and Black employees. Her career connects technical analysis with the institutional conditions that determine who can advance. The phrase “first” should be read in its specific institutional context, not generalized beyond it. Jackson’s story also demonstrates that changing a system can require both doing the engineering work and challenging the rules that restrict access to opportunity.
What the profiles have in common—and what they do not
- Technical work takes many forms. It includes chip processes, mathematical analysis, software, security policy, datasets, education, and product decisions. A narrow definition of technology as app development misses much of the work that makes digital systems possible.
- Credit is part of the technical environment. Work can be hidden in teams, infrastructure, or data labor. Clear attribution affects who gets invited to lead the next project and whose expertise is trusted.
- Mentors and sponsors do different jobs. Mentors offer advice and perspective. Sponsors use influence to nominate someone for an assignment, promotion, funding, or visibility. Career support is stronger when it includes both.
- Access is cumulative. Education, early encouragement, networks, money, location, and time influence who can enter and remain in a field. A single success story cannot reveal how many equally capable people were blocked or chose another route.
- Women do not share one experience. Race, disability, sexuality, gender identity, caregiving, immigration status, geography, and career stage can change how a workplace is experienced. AnitaB.org’s Technical Equity Experience Study explicitly examines such intersecting factors. (Study overview.)
- Leaving can be a sound decision. A move to another employer, a new venture, academia, or another field is not automatically failure. Calling every departure a lack of persistence places the burden on individuals instead of examining workplace conditions.
What employers can change
Employers should not turn these profiles into a checklist for women to work harder. The institution has the greater power to change the conditions of work. Useful measures include:
- Publish role expectations and promotion criteria, then audit whether people doing comparable work are evaluated consistently.
- Track hiring, assignment to high-impact technical work, pay, promotion, and departures by role and level. Where lawful and responsibly collected, disaggregate results enough to find patterns without compromising privacy.
- Make sponsorship concrete: identify who will nominate and advocate for people, and monitor who receives stretch assignments, customer exposure, and leadership opportunities.
- Recognize maintenance, documentation, mentoring, incident response, and team development rather than rewarding only visible launches.
- Provide credible reporting channels and protect employees from retaliation; investigate concerns instead of assigning the work of cultural repair to those who raise them.
- Make flexibility usable without quietly penalizing remote workers, caregivers, or people with disabilities in performance reviews and promotion decisions.
- Measure inclusion through experience, advancement, and retention as well as headcount. Representation alone does not establish equitable pay, psychological safety, or access to influence.
Practical ways to use these stories
Students: Try real technical projects, seek more than one mentor, and look for communities where questions are welcomed. A computer science degree is one route, not the definition of technical ability.
Career changers and early-career technologists: Translate prior experience into technical evidence—projects, analysis, operations, research, or domain expertise. Ask prospective teams how they define success and how work is assigned, not only what tools they use.
Managers and colleagues: Give specific credit in meetings and documentation, interrupt repeated credit-taking, and make sponsorship visible. Do not assume that mentoring a person is enough if the organization still withholds meaningful assignments.
Educators: Pair introductory instruction with sustained practice, role models, feedback, and routes to advanced work. Invite students to see hardware, infrastructure, security, and scientific computing as well as software products.
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Readers seeking community: AnitaB.org provides information about professional community and career resources at its official site. Access, eligibility, program schedules, and any costs vary; check the relevant current program details rather than assuming a particular pathway is available.
A better measure of persistence
Twenty-five visible careers can offer examples, but they cannot prove that persistence alone produces success. Profiles naturally emphasize people whose work became legible to institutions and the public; many people who face similar barriers are not included in such lists. The more useful question is not only how women persisted, but what support made persistence possible—and what changed because they did.
The goal should be a technology workforce in which technical contribution is recognized, advancement is fair, and no one has to endure avoidable harm just to remain in the field. What would technology look like if persistence were no longer the price of admission?
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