My pursuit of STEM began with curiosity that grew into something closer to defiance. A deep wonder about how the world works, how things are built, analyzed, and discovered, led me to books on science and math long before any classroom required it. Academic interest alone wasn't enough to contain me; I self-studied and experimented purely for the joy of it, chasing questions my imagination refused to let go of. Growing up without reliable internet access or a computer of my own, I pursued self-education in fragments: borrowed time at libraries, shared screens, and whatever free resources I could find. I taught myself to code in the gaps between everything else life demanded. What motivates me now is the realization that numbers, properly interrogated, can expose truths that narratives try to hide. As a Data Analytics student, I'm training to read the stories embedded in data, and I've come to believe those stories demand a response. Biased data produce biased outcomes, and the people most affected by them have too often had the least say in shaping those outcomes. If I could do anything with my life, I'd work at the intersection of data and equity: designing analytics frameworks that center underrepresented communities instead of erasing them. I want to be the kind of analyst who asks not just what the data says, but whose story is missing from it. Every dataset I interrogate is part of that larger goal. What excites me about STEM is what I discovered when I hit a wall on a complex project and turned to the internet for answers. What I found was more than solutions — it was distortion. Buried beneath forums and comment threads was a pattern of erasure: the contributions of people of color to the history of computing and coding, quietly minimized or credited elsewhere. The pioneers who looked like me had been written out of the story. That discovery was disheartening, but it also clarified that data and statistics, when properly interrogated, can expose the truths that narratives try to hide. That's the impact I want to have in information technology. The numbers on representation are stark: Black, Latina, and Asian women together make up less than 10% of the STEM workforce, and women of color hold fewer than 1 in 10 computing jobs in the United States. Those numbers aren't abstract to me; they describe rooms I've had to walk into and systems I want to help rebuild. I'm drawn to data analytics because the systems we build are only as equitable as the people who build them. I want to work at the intersection of data and equity, designing analytics frameworks that center the communities technology has historically ignored, rather than replicate the erasure I found online. Every dataset I analyze is a chance to surface a more accurate, more inclusive story. That possibility is what excites me most about this field. The greatest challenge I've faced has been believing I belonged in STEM at all. Growing up low-income, attending under-resourced schools, and lacking the financial access that opens so many doors in this field, I long avoided formal STEM education, quietly convinced my circumstances had already decided my ceiling. Being a woman of color felt less like an identity and more like a set of odds stacked against me. Discovering how often people who looked like me had been erased from the history of computing only deepened that doubt — as a triple minority, it was discouraging to see the pattern confirmed in the record itself. That doubt came to a head when I sat for standardized placement exams. Surrounded by peers who'd had test prep, tutors, and years of structured instruction, I was working from a deficit I hadn't chosen. I had taught myself to code in fragments — borrowed time at libraries, shared screens, whatever free resources I could find, since I didn't have reliable internet or a computer of my own. Sitting through those assessments, I wondered if gaps were all I was made of. What got me past it was reframing the story. Loving something and believing you deserve it are two different things, and I had to choose to believe the second. I stopped treating my background as a disqualifier and started treating it as the reason my perspective matters. I chose STEM not despite who I am, but because of it. Stats & Sources: 1. BIPOC women < 10% of STEM workforce National Girls Collaborative Project / NSF 2023 2. Hispanic = 8%, Black = 9% of STEM workers Pew Research Center 2021 3. Women = 34% of S&E workforce NSF Science & Engineering Indicators 2022

As a Chinese-Jamaican/Latinx woman, growing up between those identities in Florida already meant both beating expectations and living up to them. I had to be hardworking and academically successful, but I left high school at 16. I had to be good at math according to stereotypes, but despite my passion for it, it was one of my weakest subjects throughout my teenage years. And speaking of teenagerhood, homelessness is why I could not complete high school. Not because I stopped wanting to learn, but because survival came first. The instability led me to accept that graduating might not be in the cards for someone in my circumstances at the time. I fell behind. Then I fell out. By the time my family's search for stability brought us to Gainesville, Florida, I had already accepted that a diploma might not be in the cards for someone like me. Gainesville is where that changed. Through Santa Fe College, I found a path back. There was an adult education program that didn't care how many months I'd missed or how many addresses I'd had that year. It cared whether I showed up and did the work. So, I did. I borrowed rides to get to class. I finished what I'd started years earlier and walked away with my high school diploma, earned in a building that was also a college campus. Standing in that space, surrounded by other WOC pursuing degrees, was the first time college felt like something that could belong to me, too, not just something that happened to privileged people. I had to live in survival mode until the moment I received my diploma. That moment is still one of the proudest of my life. Not because a piece of paper changed my circumstances overnight, but because I proved to myself that the years I had lost were not years wasted. I had learned resilience, resourcefulness, and how to make something out of very little. Skills that, as it turns out, translate directly into the work I am passionate about. I am driven to tell stories using data. In a similar way, there are stereotypes around women of color; there are similar stereotypes surrounding the homeless population. Not everyone is addicted to drugs, gambling, etc. Sometimes life happens, and even when you make the best choices with the information available to you, it pushes you to the ground. And every time, you are the only one who can pull yourself up. And that is the only thing you are obligated to do as a woman of color.

For most of my teenage years, home wasn't a house. It was the back seat of our car, parked wherever felt safe enough for the night: a Walmart lot, a rest stop, a beach. My family lost our apartment when I was in high school, and instead of falling apart, we just kept moving. We packed what fit in the trunk and drove, chasing work, cheaper cities, anywhere that might give us a foothold. That instability didn't stay contained to nights in the car. It followed me into homework I couldn't finish without a table or a light, and eventually into the reality that I wasn't going to graduate on a normal timeline. I fell behind. Then I fell out. By the time my family's search for stability brought us to Gainesville, Florida, I had already accepted that a diploma might not be in the cards for someone like me. Gainesville is where that changed. Through Santa Fe College, I found a path back. There was an adult education program that didn't care how many months I'd missed or how many addresses I'd had that year. It cared whether I showed up and did the work. So, I did. I borrowed rides to get to class. I finished what I'd started years earlier and walked away with my high school diploma, earned in a building that was also a college campus. Standing in that space, surrounded by people actually pursuing degrees, was the first time college felt like something that could belong to me, too, not just something that happened to other families. That moment reframed everything. I stopped seeing my circumstances as proof that college wasn't for people like me and started seeing my own persistence as evidence that it was. My ambition isn't abstract. From the car to the classroom, it was forged in the specific, practical need to build a life more stable than the one I grew up in, and to make sure that stability isn't just for me, but for my family as well. This scholarship would remove one more obstacle between where I am and where I'm going. I've already proven I can do the work under conditions most students never have to face. What I need now is the chance to keep doing that work without tuition costs deciding how far I'm allowed to go. Given that chance, I don't intend to waste it — I intend to turn it into an impact that reaches well beyond my own life.

There's an old saying that success is what happens when preparation meets opportunity. We don't choose when the right job opens up, when the market shifts in our favor, or when someone influential happens to be listening at the right moment. What we can choose, however, is whether we're ready when that golden moment arrives. That readiness is what education means to me. I don't think of education as a credential to collect or a box to check on the way to something else. I think of it as the deliberate act of building capacity before you know exactly what you'll need it for. A class teaches you to weigh evidence and spot a bad argument long before you realize you'll need that skill to evaluate a contract, a diagnosis, or a political promise. A statistics course seems abstract until the day comes when you're staring at a spreadsheet trying to decide whether a trend is real or noise. Education rarely announces which of its lessons will matter. It just insists that you learn broadly and deeply enough that when the moment comes, you already have the tool in hand. This is why I see higher education specifically as the preparation. Where earlier schooling gives you the basics, higher education is where you start doing the actual work of a field: writing real arguments, running real experiments, solving problems that don't have an answer key. It's where you fail in low-stakes ways, so you don't fail in high-stakes ones later. It's where you're pushed to defend your thinking to people who disagree with you, to sit with material that doesn't come easily, and to build the discipline of finishing what you start, even when no one is forcing you to. None of that guarantees an opportunity will appear. But it guarantees that if you do, you won't be the one scrambling to catch up. You'll be the person who's already ready. I think that's the quiet dignity of education. It doesn't promise you six figures overnight. It doesn't promise you a job immediately after graduating. It doesn't promise you a lucky break. It promises that when the break comes, whenever and wherever it comes, you'll have already done the work that makes you worth choosing. Preparation is possibly the only part of success we truly control, and higher education, at its best, is where that preparation is built, brick by brick, long before we know which door it will eventually open.

I have spent most of my life happily and comfortably buried in books and writing short stories of my own. My favorite assignments were always book reports and literary analyses. So, when I decided to pursue a data analytics program, I wasn't just adding a skill. I was rewriting how I saw myself. I hadn't taken a real math class in years, and I worried that my brain simply wasn't built for it. Then I found “Math for English Majors: A Human Take on the Universal Language” by Ben Orlin, and something shifted. Orlin doesn't treat math as a separate language you either speak or don't speak. He treats it as an extension of the same instincts I already had as a writer: pattern recognition, metaphor, the search for the clearest way to say something true. Reading his explanations felt less like translation and more like recognition. I realized I hadn't been avoiding math because I lacked the ability. I'd been avoiding it because no one had ever explained it to me in a way that respected how I already thought. Steven Strogatz's “The Joy of X: A Guided Tour of Mathematics, from One to Infinity” pushed that further. Strogatz writes about calculus and probability, the way a great essayist writes about grief or memory: with curiosity, humor, and an insistence that the subject matters to your actual life. He showed me that a data set is a kind of narrative, that a distribution has a shape much like a story's arc, and that statistics are just a discipline of asking honest questions and being careful about the answers. That reframing gave me a way in. I stopped trying to become a different kind of person to learn math and started applying the reading and analytical habits I already had. That same instinct is what drew me to volunteer as a transcriber for the Library of Congress. Poring over faded letters and handwritten records to turn them into searchable text is, in its own way, a data problem: messy, unstructured information waiting to be cleaned, verified, and made usable. I didn't think of it that way at first. I just loved the history and the puzzle of deciphering someone's handwriting. But looking back, it was proof that I already had an analyst's patience and attention to detail, long before I ever called myself one. I don't want to abandon the writer in me as I become a data analyst. I want to be someone who can pull a signal out of a spreadsheet and then explain, in plain human language, why it matters and what to do about it. That combination is rare, and it's exactly the gap I intend to fill. Too many analyses die in translation between the analyst and the decision-maker. I am training diligently to be a translator. My ambition isn't to master math for its own sake. It's to become fluent enough in it that I can move freely between the quantitative and the narrative, building the bridge that most people are only qualified to stand on one side of. Orlin and Strogatz didn't just teach me formulas. They taught me that I already had the mind for this, and that the real work was learning to trust it. That's the passion I bring to this program: not someone crossing over from English into data, but someone who refuses to choose between the two.

My pursuit of STEM began curiosity which turned into deliberate defiance. A deep wonder about how the world works, how things are built, analyzed, and discovered, led me to books on science and math long before any classroom required it. But academic interest alone wasn't enough to contain me. I self-studied and experimented purely for the joy of it, chasing questions my imagination refused to let go of. When I hit a wall on a complex topic or challenging project, I turned to the internet for answers. What I found there was more than solutions, it was a distortion. Buried beneath forums and comment threads was a pattern of misinformation: the contributions of people of color to the history of computers and coding, quietly erased or credited elsewhere. The pioneers who looked like me had been written out of the story. As a triple minority, I found this deeply discouraging. But the erasure I encountered online did something else too, it held up a mirror. It awakened doubts I had long carried. In my youth, I avoided formal STEM education entirely. I feared that my circumstances; growing up low-income, attending under-resourced schools, lacking the financial access that opens so many doors in this field, had already decided my ceiling for me. Being a woman of color felt less like an identity and more like a set of odds stacked against me. What broke through that doubt was code. Data and statistics followed along with the realization that numbers, properly interrogated, could expose the truth that narratives tried to hide. But loving something and believing you deserve it are two different things. When I sat for standardized placement exams, imposter syndrome arrived in full force. Surrounded by peers who had test prep, tutors, and years of structured instruction, I was working from a deficit I had not chosen. Growing up without reliable internet access or a computer of my own meant that my self-education happened in fragments through borrowed time at libraries, shared screens, and whatever free resources I could find. I had taught myself to code in the gaps between everything else life demanded, and sitting in through assessments, I wondered if gaps were all I was made of. Yet here I stand, choosing STEM anyway. Not despite who I am, but because of it. The data tells a stark story: Black, Latina, and Asian women together represent less than 10% of the STEM workforce, and women of color hold fewer than 1 in 10 computing jobs in the United States. As a Data Analytics student, I am training to be someone who reads stories in numbers and that story demands a response. I pursue this field because the systems we build are only as equitable as the people who build them. Biased data produces biased outcomes, and for too long, the people most impacted by those outcomes have had the least say in shaping them. My goal is to work at the intersection of data and equity, to help design analytics frameworks that center underrepresented communities rather than erase them. That is precisely why I refused to stay quiet. Every dataset I interrogate, every insight I surface, every room I enter as a woman of color in this field is evidence that the numbers can change, because I am already changing them. Stats & Sources: 1. BIPOC women < 10% of STEM workforce National Girls Collaborative Project / NSF 2023 2. Hispanic = 8%, Black = 9% of STEM workers Pew Research Center 2021 3. Women = 34% of S&E workforce NSF Science & Engineering Indicators 2022

My pursuit of STEM began curiosity which turned into deliberate defiance. A deep wonder about how the world works, how things are built, analyzed, and discovered, led me to books on science and math long before any classroom required it. But academic interest alone wasn't enough to contain me. I self-studied and experimented purely for the joy of it, chasing questions my imagination refused to let go of. When I hit a wall on a complex topic or challenging project, I turned to the internet for answers. What I found there was more than solutions, it was a distortion. Buried beneath forums and comment threads was a pattern of misinformation: the contributions of people of color to the history of computers and coding, quietly erased or credited elsewhere. The pioneers who looked like me had been written out of the story. As a triple minority, I found this deeply discouraging. But the erasure I encountered online did something else too, it held up a mirror. It awakened doubts I had long carried. In my youth, I avoided formal STEM education entirely. I feared that my circumstances; growing up low-income, attending under-resourced schools, lacking the financial access that opens so many doors in this field, had already decided my ceiling for me. Being a woman of color felt less like an identity and more like a set of odds stacked against me. What broke through that doubt was code. Data and statistics followed along with the realization that numbers, properly interrogated, could expose the truth that narratives tried to hide. But loving something and believing you deserve it are two different things. When I sat for standardized placement exams, imposter syndrome arrived in full force. Surrounded by peers who had test prep, tutors, and years of structured instruction, I was working from a deficit I had not chosen. Growing up without reliable internet access or a computer of my own meant that my self-education happened in fragments through borrowed time at libraries, shared screens, and whatever free resources I could find. I had taught myself to code in the gaps between everything else life demanded, and sitting in through assessments, I wondered if gaps were all I was made of. Yet here I stand, choosing STEM anyway. Not despite who I am, but because of it. The data tells a stark story: Black, Latina, and Asian women together represent less than 10% of the STEM workforce, and women of color hold fewer than 1 in 10 computing jobs in the United States. As a Data Analytics student, I am training to be someone who reads stories in numbers and that story demands a response. I pursue this field because the systems we build are only as equitable as the people who build them. Biased data produces biased outcomes, and for too long, the people most impacted by those outcomes have had the least say in shaping them. My goal is to work at the intersection of data and equity, to help design analytics frameworks that center underrepresented communities rather than erase them. That is precisely why I refused to stay quiet. Every dataset I interrogate, every insight I surface, every room I enter as a woman of color in this field is evidence that the numbers can change, because I am already changing them. Stats & Sources: 1. BIPOC women < 10% of STEM workforce National Girls Collaborative Project / NSF 2023 2. Hispanic = 8%, Black = 9% of STEM workers Pew Research Center 2021 3. Women = 34% of S&E workforce NSF Science & Engineering Indicators 2022

My name is Jade Chung. I am currently enrolled at Western Governors University and pursuing a Bachelor's in Data Analytics. One of my biggest passions is using data science as an advocacy tool to make the invisible visible. I hope to bring awareness to the life-altering implications of chronic illnesses and autoimmune diseases such as MS, Lupus, etc. As a caretaker for my affected, disabled parent, and a friend to multiple women who live with chronic illness, I'd like to transform lived experiences into quantitative evidence that clinicians, employers, and policymakers take seriously. I will do this not just by analyzing data, but by making invisible experiences visible, credible, and actionable. MS and Lupus patients — especially women and people of color — often face years-long diagnostic delays. Policy change requires not just numbers, not just anecdotes, but both combined to tell the full story in order for it to be impactful. With bold.org's scholarship support, I can afford to further educate myself on how to make real change not only for the women around me but also at the global level. My journey into coding has always been shaped by a single question: how can code serve people who are not programmers? That question led me to enroll in a Python for Biologists course — not because I intend to become a biologist, but because I wished to speak their language well enough to build tools that genuinely help them. Working through DNA sequence analysis and bioinformatics libraries like Biopython gave me more than technical skills; it gave me a translator's perspective, the ability to sit between two disciplines and make them useful to each other. My data analyst goals are rooted in that same instinct. I want to develop software and analytical tools that improve how healthcare systems collect, interpret, and act on patient data. Hospital systems generate enormous volumes of information — vitals, lab results, treatment histories, yet much of it remains siloed or underutilized at the point of care. I am working toward the programming fluency needed to change that through coursework in data structures and algorithm design, self-directed Python projects, and a growing understanding of how clinical workflows actually function. The goal is not just to write working code, but to write code that fits seamlessly into the hands of the people using it. Beyond the screen, I am deeply invested in the human dimension of medicine — the communication, trust, and equity that determine whether a patient receives good care regardless of background. These interests are not separate from my technical pursuits; they sharpen them. Understanding that a poorly designed interface can cause a nurse to miss a critical alert or that a biased training dataset can misdiagnose an entire demographic reminds me that software in healthcare carries real moral weight. As a woman of color in computer science, I am also building for my own community. Minority populations face compounding barriers in healthcare — from systemic bias in diagnostic algorithms to underrepresentation in clinical trials. I want to be at the table where those tools are built, ensuring the software that shapes care outcomes reflects and serves everyone, including us. I plan to make a positive impact on the world by writing software that makes healthcare faster, fairer, and more human. This scholarship would bring me meaningfully closer to that work.

My pursuit of STEM began curiosity which turned into deliberate defiance. A deep wonder about how the world works, how things are built, analyzed, and discovered, led me to books on science and math long before any classroom required it. But academic interest alone wasn't enough to contain me. I self-studied and experimented purely for the joy of it, chasing questions my imagination refused to let go of. When I hit a wall on a complex topic or challenging project, I turned to the internet for answers. What I found there was more than solutions, it was a distortion. Buried beneath forums and comment threads was a pattern of misinformation: the contributions of people of color to the history of computers and coding, quietly erased or credited elsewhere. The pioneers who looked like me had been written out of the story. As a triple minority, I found this deeply discouraging. But the erasure I encountered online did something else too, it held up a mirror. It awakened doubts I had long carried. In my youth, I avoided formal STEM education entirely. I feared that my circumstances; growing up low-income, attending under-resourced schools, lacking the financial access that opens so many doors in this field, had already decided my ceiling for me. Being a woman of color felt less like an identity and more like a set of odds stacked against me. What broke through that doubt was code. Data and statistics followed along with the realization that numbers, properly interrogated, could expose the truth that narratives tried to hide. But loving something and believing you deserve it are two different things. When I sat for standardized placement exams, imposter syndrome arrived in full force. Surrounded by peers who had test prep, tutors, and years of structured instruction, I was working from a deficit I had not chosen. Growing up without reliable internet access or a computer of my own meant that my self-education happened in fragments through borrowed time at libraries, shared screens, and whatever free resources I could find. I had taught myself to code in the gaps between everything else life demanded, and sitting in through assessments, I wondered if gaps were all I was made of. Yet here I stand, choosing STEM anyway. Not despite who I am, but because of it. The data tells a stark story: Black, Latina, and Asian women together represent less than 10% of the STEM workforce, and women of color hold fewer than 1 in 10 computing jobs in the United States. As a Data Analytics student, I am training to be someone who reads stories in numbers and that story demands a response. I pursue this field because the systems we build are only as equitable as the people who build them. Biased data produces biased outcomes, and for too long, the people most impacted by those outcomes have had the least say in shaping them. My goal is to work at the intersection of data and equity, to help design analytics frameworks that center underrepresented communities rather than erase them. That is precisely why I refused to stay quiet. Every dataset I interrogate, every insight I surface, every room I enter as a woman of color in this field is evidence that the numbers can change, because I am already changing them. Stats & Sources: 1. BIPOC women < 10% of STEM workforce National Girls Collaborative Project / NSF 2023 2. Hispanic = 8%, Black = 9% of STEM workers Pew Research Center 2021 3. Women = 34% of S&E workforce NSF Science & Engineering Indicators 2022

My name is Jade Chung. I am currently enrolled at Western Governors University and pursuing a Bachelor's in Data Analytics. One of my biggest passions is using data science as an advocacy tool to make the invisible visible. I hope to bring awareness to the life-altering implications of chronic illnesses and autoimmune diseases such as MS, Lupus, etc. As a caretaker for my affected, disabled parent, and a friend to multiple women who live with chronic illness, I'd like to transform lived experiences into quantitative evidence that clinicians, employers, and policymakers take seriously. I will do this not just by analyzing data, but by making invisible experiences visible, credible, and actionable. MS and Lupus patients — especially women and people of color — often face years-long diagnostic delays. Policy change requires not just numbers, not just anecdotes, but both combined to tell the full story in order for it to be impactful. With bold.org's scholarship support, I can afford to further educate myself on how to make real change not only for the women around me but also at the global level. My journey into coding has always been shaped by a single question: how can code serve people who are not programmers? That question led me to enroll in a Python for Biologists course — not because I intend to become a biologist, but because I wished to speak their language well enough to build tools that genuinely help them. Working through DNA sequence analysis and bioinformatics libraries like Biopython gave me more than technical skills; it gave me a translator's perspective, the ability to sit between two disciplines and make them useful to each other. My data analyst goals are rooted in that same instinct. I want to develop software and analytical tools that improve how healthcare systems collect, interpret, and act on patient data. Hospital systems generate enormous volumes of information — vitals, lab results, treatment histories, yet much of it remains siloed or underutilized at the point of care. I am working toward the programming fluency needed to change that through coursework in data structures and algorithm design, self-directed Python projects, and a growing understanding of how clinical workflows actually function. The goal is not just to write working code, but to write code that fits seamlessly into the hands of the people using it. Beyond the screen, I am deeply invested in the human dimension of medicine — the communication, trust, and equity that determine whether a patient receives good care regardless of background. These interests are not separate from my technical pursuits; they sharpen them. Understanding that a poorly designed interface can cause a nurse to miss a critical alert or that a biased training dataset can misdiagnose an entire demographic reminds me that software in healthcare carries real moral weight. As a woman of color in computer science, I am also building for my own community. Minority populations face compounding barriers in healthcare — from systemic bias in diagnostic algorithms to underrepresentation in clinical trials. I want to be at the table where those tools are built, ensuring the software that shapes care outcomes reflects and serves everyone, including us. I plan to make a positive impact on the world by writing software that makes healthcare faster, fairer, and more human. This scholarship would bring me meaningfully closer to that work.

History is not simply what happened; it is what we choose to preserve. As a triple minority student passionate about technology and the stories that shape our identity, I have found purpose at the intersection of both. Through my volunteer work transcribing historical documents for the Library of Congress, I have dedicated countless hours to preserving and correcting the historical record of Black and Native Americans — a record that has too often been omitted, distorted, or forgotten. This cause matters to me because representation does not stop at the present. It reaches backward into history and forward into the future. When I carefully transcribe a faded letter or correct a mislabeled record, I am doing more than digitizing text — I am restoring someone's ancestor to their rightful place in history. For a minority student in data analytics, that work is deeply personal. It teaches me that data is never neutral. Behind every record is a human story, and how we collect, clean, and categorize information determines whose stories survive. That lesson lives at the heart of my academic pursuit. As a Data Analytics major, I am learning to do with structured information exactly what I do with historical documents. This means to find meaning in raw material, identify what is missing, and build systems that make knowledge more accessible and trustworthy. The skills I develop in the classroom directly mirror the work I do as a volunteer: cleaning inconsistent data, recognizing patterns, and understanding how gaps in a dataset can reflect real-world inequities. Transcribing Black and indigenous history for the Library of Congress did not just give me volunteer hours; it gave me a framework for why data integrity matters and who pays the price when it fails. My hero is someone I have never met: my future self. Not a celebrity, not a historical figure, but the version of me that makes it through. The one who earns her degree, builds something meaningful, and opens doors for students who come after her. On the hardest days, when balancing coursework, financial pressure, and the weight of being a first-generation student in a demanding field, I return to that image. My future self is not waiting to be rescued. She is being built, right now, one difficult decision at a time. That future self is a data analyst who uses her skills purposefully, to close equity gaps, to surface overlooked communities in datasets, and to ensure that the insights driving decisions reflect everyone they affect. That vision demands full focus, and right now, financial pressure threatens it. Currently, working to cover my basic needs pulls time and energy away from coursework, research opportunities, and the hands-on projects that shape a competitive data professional. This scholarship would not just ease a financial burden; it would buy back hours. Hours for deeper study and for building the portfolio that turns ambition into a career. I am not waiting for an opportunity to find me. I am transcribing history, analyzing data, and working every day to close the gap between who I am and who I am meant to become. This scholarship would accelerate that journey.

My journey into computer science has always been shaped by a single question: how can code serve people who are not programmers? That question led me to enroll in a Python for Biologists course — not because I intend to become a biologist, but because I wished to speak their language well enough to build tools that genuinely help them. Working through DNA sequence analysis and bioinformatics libraries like Biopython gave me more than technical skills; it gave me a translator's perspective, the ability to sit between two disciplines and make them useful to each other. My data science goals are rooted in that same instinct. I want to develop software and analytical tools that improve how healthcare systems collect, interpret, and act on patient data. Hospital systems generate enormous volumes of information — vitals, lab results, treatment histories, yet much of it remains siloed or underutilized at the point of care. I am working toward the programming fluency needed to change that, through coursework in data structures and algorithm design, self-directed projects in Python, and a growing understanding of how clinical workflows actually function. The goal is not just to write working code, but to write code that fits seamlessly into the hands of the people using it. Beyond the screen, I am deeply invested in the human dimension of medicine — the communication, trust, and equity that determine whether a patient receives good care regardless of background. These interests are not separate from my technical pursuits; they sharpen them. Understanding that a poorly designed interface can cause a nurse to miss a critical alert, or that a biased training dataset can misdiagnose an entire demographic, reminds me that software in healthcare carries real moral weight. As a woman of color in computer science, I am also building for my own community. Minority populations face compounding barriers in healthcare — from systemic bias in diagnostic algorithms to underrepresentation in clinical trials. I want to be at the table where those tools are built, ensuring the software that shapes care outcomes reflects and serves everyone, including us. The future I am building toward sits at that intersection: engineering tools that are not only technically sound but designed with clinical empathy. Whether that means developing a patient outcome dashboard for a rural hospital, a predictive readmission model, or a data pipeline that flags treatment disparities across populations, my aim is the same — to write software that makes healthcare faster, fairer, and more human. This scholarship would bring me meaningfully closer to that work.

My pursuit of STEM began curiosity which turned into deliberate defiance. A deep wonder about how the world works, how things are built, analyzed, and discovered, led me to books on science and math long before any classroom required it. But academic interest alone wasn't enough to contain me. I self-studied and experimented purely for the joy of it, chasing questions my imagination refused to let go of. When I hit a wall on a complex topic or challenging project, I turned to the internet for answers. What I found there was more than solutions, it was a distortion. Buried beneath forums and comment threads was a pattern of misinformation: the contributions of people of color to the history of computers and coding, quietly erased or credited elsewhere. The pioneers who looked like me had been written out of the story. As a triple minority, I found this deeply discouraging. But the erasure I encountered online did something else too, it held up a mirror. It awakened doubts I had long carried. In my youth, I avoided formal STEM education entirely. I feared that my circumstances; growing up low-income, attending under-resourced schools, lacking the financial access that opens so many doors in this field, had already decided my ceiling for me. Being a woman of color felt less like an identity and more like a set of odds stacked against me. What broke through that doubt was code. Data and statistics followed along with the realization that numbers, properly interrogated, could expose the truth that narratives tried to hide. But loving something and believing you deserve it are two different things. When I sat for standardized placement exams, imposter syndrome arrived in full force. Surrounded by peers who had test prep, tutors, and years of structured instruction, I was working from a deficit I had not chosen. Growing up without reliable internet access or a computer of my own meant that my self-education happened in fragments through borrowed time at libraries, shared screens, and whatever free resources I could find. I had taught myself to code in the gaps between everything else life demanded, and sitting in through assessments, I wondered if gaps were all I was made of. Yet here I stand, choosing STEM anyway. Not despite who I am, but because of it. The data tells a stark story: Black, Latina, and Asian women together represent less than 10% of the STEM workforce, and women of color hold fewer than 1 in 10 computing jobs in the United States. As a Data Analytics student, I am training to be someone who reads stories in numbers and that story demands a response. I pursue this field because the systems we build are only as equitable as the people who build them. Biased data produces biased outcomes, and for too long, the people most impacted by those outcomes have had the least say in shaping them. My goal is to work at the intersection of data and equity, to help design analytics frameworks that center underrepresented communities rather than erase them. That is precisely why I refused to stay quiet. Every dataset I interrogate, every insight I surface, every room I enter as a woman of color in this field is evidence that the numbers can change, because I am already changing them. Stats & Sources: 1. BIPOC women < 10% of STEM workforce National Girls Collaborative Project / NSF 2023 2. Hispanic = 8%, Black = 9% of STEM workers Pew Research Center 2021 3. Women = 34% of S&E workforce NSF Science & Engineering Indicators 2022

“You don't look like you'd study computer science.” “You'll never get a high-paying job dressed like that.” These are only some of the limiting beliefs and common phrases thrown at alternative academics on a constant basis. It was always strange to me that many adults held these views, given that growing up, watching 90s films and 2000s TV shows, the coders, hackers, and scientists were always the punks and the goths. From The Matrix to NCIS to underground hacker tropes in early internet culture, the people who understood systems—the ones who could see through the code of the world—were always the ones dressed in black. They existed on the margins, questioning authority, dismantling structures, and rebuilding them in new ways. To me, that wasn’t just aesthetic; it was philosophical. Goth culture has always been about examining the uncomfortable, interrogating norms, and finding beauty in complexity. Those values align seamlessly with STEM. At its core, STEM is about curiosity and skepticism. Scientists question assumptions. Engineers challenge limitations. Programmers break and rebuild systems. Goths, too, reject surface-level explanations. We are drawn to depth. There is also a misconception that STEM belongs to a narrow personality type: perfunctory, conventionally clean-cut, and aesthetically neutral. But innovation has never thrived in homogeneity. Subcultures exist because people think differently, and different thinkers are exactly what scientific fields need. The alternative community has always embraced individuality, independent research, and self-teaching. Long before online coding bootcamps were mainstream, goth and punk communities were learning HTML to customize blogs, building fan sites, and experimenting with digital art. We were already immersed in technology - not as passive consumers, but as creators. Growing up visibly alternative, it's always assumed that you would only be accepted in the arts. And I was. I love music, poetry, and painting immeasurably. While the arts and sciences are often framed as opposites - emotion versus logic, creativity versus calculation -they actually rely on many of the same cognitive skills. Poetry and music cultivate pattern recognition, structural awareness, disciplined creativity, and comfort with abstraction - all of which are foundational in STEM fields. I found myself drawn to math and statistics, not because they were “safe” or conventional choices, but because they felt like tools for uncovering hidden truths. Choosing a career in data analytics felt less like conforming to expectations and more like embracing who I had always been. Data analytics is about translating complexity into clarity. Goths belong in STEM, not despite our aesthetic, but because of the mindset it represents. We are comfortable with complexity. We are unafraid of difficult questions. We are independent thinkers who see systems from the outside and are therefore uniquely equipped to understand and improve them. Diversity in STEM is not only about race or gender; it is also about perspective. Subcultures cultivate perspective. The world runs on data, algorithms, and scientific advancement. It benefits from people who challenge norms and refuse to fit neatly into boxes. If anything, the goth kid in the back of the classroom - the one quietly observing, questioning, and thinking deeply - may be precisely the kind of mind STEM needs.

My “Pie in the Sky” is to launch a cozy, accessible e-sports league designed specifically for people living with chronic illnesses- a competitive space where high-level play and humane pacing coexist. Following in the footsteps of PARA e-sports and Permistan Gaming, I imagine tournaments with built-in rest breaks, sensory-considerate broadcasts, flexible match scheduling, and community moderators trained in disability awareness. A place where ambition is not diminished by illness, and where competition feels energizing instead of punishing. This dream was sparked the many conversations I have had with members of the gaming community in real life, on Reddit, and various other platforms. I’ve experienced how traditional competitive spaces with their bright lights, long brackets, rigid start times, high-pressure voice comms can unintentionally exclude people whose energy, pain levels, or cognitive stamina fluctuate. I’ve met talented players who disappear from their favorite Discord communities and live events because they couldn’t commit to marathon sessions or because flare-ups made them unreliable in systems that demand consistency above all else. The message, even if unintentional, felt clear: if you can’t keep up, you don’t belong. But what if “keeping up” wasn’t the only measure of worth? Gaming has always been a refuge for many - a place where strategy, creativity, and teamwork matter more than physical limitations. Yet the competitive scene often mirrors the same inaccessibility found offline. That tension sparked this dream: to build something that proves competitive excellence and accessibility are not opposites. They are design choices. The league I envision would focus on cozy or low-stimulation competitive titles that reward skill and strategy without overwhelming sensory input. Matches would be shorter, tournament structures would include buffer days for flare-ups along with assistive tech. There would be transparent communication policies, mental health check-ins, and community guidelines that prioritize kindness without sacrificing competitiveness. I want players to feel the thrill of a finals match without fearing the cost to their health. While I am a big believer that casual gaming is a therapeutic outlet with an array of unexpected health benefits, I will take into account how Esports can lead to common injuries due to eye strain, constant stress on wrist and hands, and prolonged sitting. The key is finding the balance. Getting there will require more than passion. First, I’ll educate myself further deeply on accessible design- learning from disability advocates, occupational therapists, and accessibility consultants to ensure the league is built with the community, not just for it. I’ll need to start small: perhaps a pilot tournament with a handful of players, gathering feedback and iterating thoughtfully. Building partnerships with indie developers or streaming platforms could help amplify the mission while keeping the environment intentionally scaled. Funding will be another step. I’ll need to explore grants focused on disability inclusion, gaming innovation, or community health. I want to show potential supporters that accessibility isn’t charity; it’s untapped potential. There are thousands of chronically ill gamers who crave competition but need flexibility. Launching something so personal means risking skepticism. People may question whether a “cozy” e-sports league can be taken seriously. I’ll need resilience to keep building even if the first tournament is small, even if growth is slow. But I believe slow growth, rooted in care and creativity, is stronger than explosive overnight hype. My dream isn’t just to host tournaments. It’s to redefine what competitive spaces can look like, one where some of the funniest, most entertaining pillars of the gaming community can experience inclusivity and glory despite disability. To create a model that other leagues might adopt. To prove that accessibility is not a compromise- it’s innovation.