Bridging Worlds: A Vision for Ethical AI in Healthcare A visionary sees possibilities where others see obstacles, and my journey from a struggling first-generation student to a CS graduate student at Northeastern University has taught me to find opportunity in adversity. What makes me a visionary isn't just my technical skills or academic achievements; it's my ability to see connections between seemingly disparate challenges and envision solutions that others might overlook. Growing up in a low-income household, I witnessed firsthand how systemic barriers prevent people from accessing the resources they need. From ages 15 to 27, I struggled with undiagnosed ADHD, depression, and anxiety, working three jobs to put myself through community college while my grades remained strong but my path remained unclear. It wasn't until I was finally diagnosed with ADHD that the pieces began falling into place. This experience gave me a unique lens through which I view problems. I understand what it means to be overlooked by systems, to have potential that goes unrecognized, and to find innovative ways forward when traditional paths are blocked. My vision emerged from this intersection of personal struggle and technological possibility. While studying cognitive science and later discovering computer science, I realized that the same algorithmic thinking that helped me understand my own brain could be applied to solve much larger systemic problems. I began to see how machine learning and AI could bridge the gaps in healthcare that I had experienced personally, like gaps in diagnosis, treatment accessibility, and preventive care that plague our healthcare system. What truly makes me visionary is my ability to see technology not as an end in itself, but as a tool for equity and human flourishing. While many in tech focus on efficiency and profitability, I envision developing machine learning models for early identification of learning differences in students from underrepresented backgrounds, focusing on patterns that traditional screening methods miss. I see creating adaptive public health models that can help institutions and communities respond more quickly to health crises, particularly focusing on vulnerable populations who are always hit hardest and helped last. The most significant boundary I pushed was refusing to accept the limitations others placed on me as a woman in STEM from a disadvantaged background. This wasn't a single moment but a sustained campaign against systemic discouragement that began in high school and continues today. In high school, my math instructors subjected me to microaggressions, making comments about makeup and boys that sent a clear message: serious mathematics wasn't for girls like me. Instead of fighting back then, I internalized these messages and drifted away from STEM, convinced I should pursue something more "realistic." For years, I accepted this limiting narrative. The boundary-pushing began when I took my first computer science course while preparing to transfer to a four-year university. Despite having been steered away from technical fields for years, I dove in completely. I wasn't just naturally gifted; I was hungry to prove that the barriers I'd faced were artificial. Within one semester, I became a TA for Java fundamentals, demonstrating not just my technical ability but also my commitment to helping others navigate the same challenges I had faced. When COVID hit during my second semester at the four-year university, I faced my greatest test. As the world shut down and my own mental health spiraled, my department chair told me I wasn't "dedicated enough" and should pursue another career. She subjected me to microaggressions and racist remarks while I was already struggling with depression and financial instability after being laid off. The easy path would have been to accept her assessment and walk away. Instead, I pushed back against this institutional gatekeeping in a way that was both strategic and principled. I persevered through my degree while documenting the discriminatory treatment I received. I graduated as the first person in my family to earn a bachelor's degree, proving wrong everyone who had told me I didn't belong in STEM. But more importantly, I used this experience to fuel my determination to change the culture of tech from within. The boundary I pushed wasn't just personal; it was cultural. I refused to accept that belonging in STEM required fitting into existing molds or staying quiet about systemic problems. After graduating, instead of simply moving on, I found ways to support other students facing similar challenges through my work with Northeastern's MSCS program. I now help students navigate the same hostile environments I survived, turning my boundary-pushing experience into a pathway for others. Most recently, I've pushed boundaries in how I approach machine learning education and research. Rather than accepting the standard curriculum focused on technical optimization, I consistently ask harder questions: How can these algorithms reduce inequality? What are the ethical implications of our models? How can we ensure AI serves marginalized communities rather than further disadvantaging them? These questions aren't typically central to CS programs, but I've made them central to my academic journey. My vision for the future centers on democratizing healthcare through ethical AI that actively works to eliminate rather than perpetuate existing inequalities. This isn't just about building better algorithms; it's about fundamentally changing how we think about the relationship between technology and social justice. In the next five years, I plan to complete my master's degree at Northeastern while conducting research that bridges machine learning and healthcare equity. My immediate goal is to secure research opportunities that allow me to publish work on using AI for early detection and prevention of mental health crises, particularly in underserved populations. This research will serve as the foundation for my PhD applications, with my ultimate goal being acceptance into a program like MIT and Harvard's HST MEMP PhD program, where I can formally bridge medicine and engineering. But my vision extends far beyond personal academic achievement. I envision a future where AI systems are designed from the ground up with equity as a core principle. Instead of algorithms that reflect and amplify existing biases, I want to build systems that actively identify and correct for the systemic disadvantages that lead to late diagnosis due to socioeconomic barriers, gender bias in ADHD recognition, and racial/ethnic disparities in mental health diagnosis. These models would focus on patterns that traditional screening methods miss: the high-achieving student struggling silently, and the young woman whose symptoms are dismissed, the first-generation college student who doesn't know to seek help. My long-term vision includes establishing research initiatives that focus specifically on health equity applications of AI. I want to lead teams that develop predictive models for disease prevention in underserved communities, create AI-powered tools that make mental healthcare more accessible and affordable, and build systems that can rapidly adapt to global health crises like the COVID-19 pandemic. I also envision myself as a disruptor within the tech industry itself. Too many companies use AI without regard for its social impact, prioritizing shareholder value over human welfare. I want to be a voice for responsible innovation, someone who proves that ethical AI isn't just morally necessary; it's also more effective and sustainable. I plan to work at the intersection of academia and industry, conducting research that demonstrates how centering equity in AI development leads to better outcomes for everyone. Part of my vision involves mentorship and education. Having been the first in my family to pursue higher education, I understand the importance of representation and support. I want to create pathways for other first-generation students, particularly women and people of color, to enter and thrive in AI and machine learning. This includes developing educational programs, mentoring individual students, and advocating for systemic changes in how CS programs recruit and support diverse talent. My ultimate vision is a world where AI amplifies human potential rather than replacing it, where machine learning serves as a tool for liberation rather than oppression, and where the benefits of technological advancement flow to those who need them most. I see myself contributing to this future through research, through the students I mentor, through the companies I influence, and through the models I help build. The obstacles I've overcome, from undiagnosed ADHD to financial struggles to institutional discrimination, haven't just shaped my resilience; they've clarified my purpose. I've experienced what happens when systems fail people, and I've also experienced the transformative power of finally receiving the support you need. My vision is to build AI systems that provide that support at scale, ensuring that fewer people have to struggle in isolation the way I did. This vision drives everything I do now. It's why I pursue research opportunities despite the financial challenges. It's why I persist in spaces where I'm often the only woman or person of color. It's why I ask the difficult questions about ethics and equity that others might prefer to avoid. And it's why I'm committed to pursuing a PhD and dedicating my career to this work, even when easier paths might offer more immediate rewards. My vision isn't just about changing technology; it's about changing who gets to shape technology and who benefits from it. It's about ensuring that the next generation of AI researchers includes voices like mine, people who understand that innovation must serve justice and that true progress means lifting up those who have been left behind. What makes me a visionary is my ability to see beyond current limitations toward a future where technology serves equity. The boundaries I've pushed, from refusing to accept discouragement in STEM to insisting on ethical considerations in AI research, have prepared me to continue challenging systems that don't serve human flourishing. My vision for the future is both ambitious and achievable: AI systems that reduce rather than perpetuate inequality, research that bridges technical innovation with social justice, and a tech industry culture that prioritizes human welfare alongside technological advancement. This vision emerges directly from my lived experience of overcoming systemic barriers, and it will guide my work as I pursue my PhD and beyond. The scholarship support would be transformational in helping me realize this vision. It would allow me to focus on research rather than full-time work, enabling me to conduct the studies and publish the papers that will make me competitive for top PhD programs. More importantly, it would accelerate my timeline for contributing meaningful solutions to the health equity challenges I'm passionate about addressing. I am ready to be a changemaker in tech, someone who disrupts the status quo not through flashy innovations but through sustained commitment to building systems that work for everyone. My journey from a struggling first-generation student to a graduate researcher at Northeastern has prepared me for this work, and my vision for ethical AI in healthcare will guide me through the contributions I hope to make in the years ahead.