Opening Scene: INT. BEDROOM — NIGHT A soft hum. The kind of silence where the only sound is the low whirr of a laptop and the occasional sigh of frustration. The walls are cluttered — not with posters or photos, but with sticky notes and timelines. Speech outlines, Python snippets, quotes from Ambedkar and Ruth Bader Ginsburg. A mini mandir glows gently in the corner, next to a stack of old Diwali diyas and a dog-eared copy of the Constitution. 3:47 a.m. Cut to: Me — hunched over my laptop in oversized pajamas, one leg tucked under me, dark circles under my eyes, but a spark in them. I'm debugging a stubborn piece of code in my hate speech legislation project. The model keeps crashing. I hit Enter one more time. The terminal blinks: "Run Successful." I exhale. Not just relief, but something deeper — the sense that I’m onto something bigger. MONTAGE: — A roomful of middle schoolers at Pathways, laughing nervously as I ask who’s ready to debate. I kneel next to a student struggling with public speaking and whisper, “Just tell your story. That’s your power.” — My Programming Club giving a live demo of a student-built AI chatbot. The hackathon trophies lining the edge of the whiteboard. — Me chairing a Model Congress session, gavel in hand, diffusing a heated argument on surveillance laws. “Order. Let’s hear all sides. That’s what democracy requires.” — A prayer room at home. I touch my forehead to the floor before my Model Congress overnight trip. My mom hands me prasad. My dad wishes me luck in Hindi. — At Columbia’s SHP program, scribbling Dijkstra’s algorithm in the margins of my notes while Andrew Yang explains how to optimize batch processing. — At Harvard’s Spatial Data Lab, sitting in awe as a mentor helps me fine-tune a BERT model. I realize I’m not just doing research — I’m creating policy tools. Voiceover: “They say to make it, you have to specialize. Pick one thing. Become the best at it. But I was never just one thing. I was code and culture. Algorithms and advocacy. A girl who prayed before tests and debated after school. A keyboard in one hand and a mic in the other.” SCENE CHANGE: Washington, D.C. — near-future. A young woman — future me — strides into a Capitol Hill office. She’s holding a printed proposal: “AI for Equitable Policy Modeling.” The same girl who used to whisper arguments to scared kids at debate practice is now briefing a senator on how to identify legislative gaps in hate crime enforcement. Outside the window, a peaceful protest marches down the street. I pause to look. Their signs say: “Justice is not an algorithm.” “Data is not neutral.” I smile — not because they’re wrong, but because they’re right. That’s why I’m here. I turn back, step into the room, and say: “We ran the simulations. But we also listened to the people. Here’s how we fix it.” FADE TO BLACK. TITLE APPEARS: Ctrl+Alt+Create Tagline: “She didn’t just rewrite code — she rewrote the system.” This is a story about a girl who didn’t fit into one box — STEM or humanities, tradition or modernity, debate or data science — so she built her own framework. It’s about seeing Hindu dharma not just as rituals, but as a philosophy of balance and responsibility. It’s about realizing that being a good citizen means participating fully — through discourse, service, prayer, and persistence. And it’s about how every error in code, every debate lost, every night spent rerunning models — brought her one step closer to creating a future worth living in.

To me, being a good citizen means more than just following laws or showing up to vote—it’s about being informed, open-minded, and willing to engage in the difficult but necessary conversations that push our society forward. I’ve come to understand this through my deep involvement in Model Congress. For the past two years, I’ve served as a Senate Chair, leading committee sessions, facilitating debates, and ensuring that all students—regardless of their political leanings—felt heard and respected. In a time where schools often shy away from political discussion out of fear of controversy, Model Congress has given me space to explore discourse in its most productive form. I’ve seen students debate the ethical implications of AI, immigration reform, and climate policy, and walk away with a better understanding of opposing views. As Chair, I had to remain neutral, guiding the discussion and maintaining order—not always easy when emotions ran high. But that experience taught me how to listen actively, ask meaningful questions, and value the art of respectful disagreement. This year, I was also selected as a party leader, where I worked closely with delegates to strategize, write bills, and collaborate across committees. I had to learn how Congress really works—filibusters, floor procedures, lobbying, party-line votes—and in doing so, I realized how complex yet essential our democratic system is. That experience made me not only more knowledgeable but also more invested in the legislative process. I now understand how change is made, where it gets stuck, and why it's so important that young people take part. Voting, in this context, becomes more than just a right—it becomes a civic duty. It’s how we hold power accountable and how we make our voices heard. Through Model Congress, I’ve seen how a single idea, when backed by evidence and presented with passion, can shift a room. That’s what voting does on a national scale. It gives people the opportunity to be part of the conversation, to influence laws that impact their lives, and to participate in the collective shaping of our future. The Constitution, to me, is the foundation that makes all of this possible. It doesn’t just grant us rights; it empowers us to use them. It protects our freedom to speak, to assemble, and to vote. And as someone who has spent years simulating its processes, I’ve gained not just an academic understanding of it—but a personal appreciation for what it enables us to do when we engage with it fully.

My innate drive to analyze attracted me to my first Data Science course at Stanford, where I learned how algorithms can transform massive datasets into actionable insights. By building my first model to predict national recessions using global financial indicators, I realized data science wasn’t merely lines of code, but rather a powerful tool to drive policy action. Through independent study under the Gifted and Talented program at school, I investigated statistical modeling, AI, and machine learning in Python. My sophomore-year exploration of classification algorithms, from logistic regression to random forests, took on a personal meaning when a close friend’s struggle with effective antidepressant treatment inspired me to develop a model for optimizing medication dosages. During my junior year, I studied geospatial information systems in Google Earth Engine, learning to process satellite imagery and topographical data to develop a land-degradation assessment model. I was then selected for two research internships. At Yale University, under Dr. Pandey, I’m developing a regression classifier using machine learning to identify vulnerabilities in African public health infrastructure with CDC/WHO reports. At Harvard’s Spatial Data Lab, I’m analyzing the correlation between hate speech and local hate-crime legislation severity with Geospatial Information Systems and trained BERT models, increasing policymakers' understanding of hate-speech dynamics. But numbers only matter if they can be heard. That’s where debate comes in. As a nationally recognized debater and coach, I’ve learned to take complex issues—like the ethics of facial recognition or the effect of sanctions on global supply chains—and make them understandable, persuasive, and human. Debate taught me how rhetoric shapes policy as much as evidence does. It’s also why I’ve spent over 170 hours building a nonprofit debate program for students with disabilities: access to information and advocacy should never be exclusive. Through various STEM programs, I have been able to form a technical community with like-minded peers and explore different aspects of data science I wouldn’t have discovered on my own. At Governor's School in the Sciences, I learned how to model epidemics with cellular automata, and as a NJ Governor's STEM Scholar am developing a water-treatment application using Python and Kivy Visual Studios. Along with that, I am also learning the math behind major programming algorithms in “Introduction to Algorithms” at the Columbia Science Honors Program. These experiences have shown me data science’s range to drive meaningful societal change across multiple fields, a journey of analysis and innovation I’m eager to continue at college. I will combine Artificial Intelligence with Social Policy at the University of Pennsylvania to influence/evaluate government legislation by accounting for structural injustices within current analytical models. Under Dr. Pandey at Yale, I’m currently developing a regression classifier using machine learning to identify vulnerabilities in African public health infrastructure during conflicts. I hope to turn this research into legislation that will develop resilient infrastructure in these regions. Additionally, at Harvard’s Spatial Data Lab, I’m analyzing the correlation between hate speech and local hate crime legislation severity, which can help foster more inclusive digital communities. I hope to work as a political consultant in the future, developing quantitative machine-learning models to evaluate current policies and propose new legislation. My goal is to use artificial intelligence(with machine-learning and simulations) to optimize government strategies and help millions.

As a mentor at Pathways, a local nonprofit that provides educational programs for elementary schoolers with special needs, I noticed a concerning pattern while teaching Scratch. Though the students could create impressive games, they lacked the confidence to communicate their programming process. Wanting to investigate the scope of this issue, I asked other elementary schoolers, including my brother if they felt comfortable participating in class discussions. Nearly every single child said no, admitting to avoiding participating due to fear of being wrong, a problem intensified by the lack of active engagement during virtual learning during the COVID pandemic. Having seen how debate transformed me into a confident speaker, I recognized that teaching these same skills could help these students find their voice. Thus, I founded the debate program at Pathways– the only one in our community specifically designed for students with special needs in my community. Whereas other Pathways programs solely focus on individual instruction which inadvertently minimizes peer interaction, I developed a curriculum that provides individualized support while maintaining group interaction. Each child receives personalized mentorship to fill in outlines for each speech, after which they reenact a debate with another student to encourage interaction and confidence-building. But this process didn't come immediately. In fact, 20 minutes after my first class I was in tatters. My conventional teaching methods–a short lecture followed by an assignment– failed as I struggled to manage eight kids with varying needs: Kyle struggled with writing and Julia barely spoke. Adapting quickly, I simplified my instructions for next week’s session, breaking down complex ideas into bite-sized pieces. Instead of lecturing to the whole group, I spent time working one-on-one with students, modifying the assignment to fit their needs. Slowly, I saw progress. Julia used visual aids to make her first argument. Kyle could use the text-to-speech function on his iPad to record his arguments. This experience taught me the important lesson of flexibility and perseverance, specifically how to systemically break down obstacles and adapt to each student’s unique needs and abilities. Through the program, I witnessed students transform as they began to articulate complex ideas, believe in their own voices, and most importantly have fun as they debate the need for Stanleys, the best superheroes, and why homework should be banned. The debate program became a pathway to personal empowerment and we have so far helped over 60 students, raised funds for Pathways, and mentored underclassmen to take the program further after I graduate. Wanting to extend similar opportunities to older students, I founded Key2 Debate, an affordable summer camp that reached 58 students across five states. By providing these children with the tools to express themselves confidently, I wasn't just helping them communicate and reclaim their potential, one conversation at a time That’s why I’d be honored to join Inspire11’s mentorship program. I believe mentorship multiplies impact. With guidance from professionals who have “turned it up to 11” in their own fields, I’d refine my leadership skills, learn how to scale education initiatives more sustainably, and gain insight into how to build systems—not just programs—that empower marginalized voices. Inspire11 could help me amplify the voice I’ve worked hard to find—and help others find theirs too.

As a first-generation immigrant, I grew up with a deep awareness of how narrow the definition of “success” can be. For many people around me, especially in immigrant communities, success looked like neat resumes, predictable achievements, and choosing the safest, most linear path. Specifically, in high school, the unspoken norm was to focus on one research project for years, polish it, and present it for awards. That’s what college counselors, mentors, and even science fair judges expected. But I couldn’t do it. I didn’t want to limit my curiosity to a single lab or topic. Instead, I explored everything from machine learning applications in mental health to using satellite data to track soil erosion, from the effects of political coups on public health in Africa to the behavior of amphibian diseases in closed ecosystems. It wasn’t the most “efficient” path, and many people told me I was making a mistake. But I kept going because my immigrant experience taught me to be adaptive, curious, and bold. My parents didn't come to this country to raise someone who played it safe. They came here so I could ask my own questions. As I dove deeper into these varied projects, I began sharing what I learned with younger students at my school who also didn’t see themselves in the rigid paths they were told to follow. I mentored underclassmen, gave workshops on using AI tools, and helped others design projects that reflected their passions, not just what looked good on paper. Slowly, I watched the culture shift. Students stopped asking, “What topic should I do to win?” and started asking, “What do I want to explore?” Broadening my horizons in research had unexpected benefits as well. Pursuing diverse research projects broadened my technical skill set and exposed me to different approaches across disciplines—from natural language processing to public health data modeling. For example, my earlier work using machine learning to predict antidepressant dosages gave me a strong foundation in regression modeling, which I later applied to evaluate the resilience of public health systems in African nations during political instability Choosing the unconventional path wasn’t easy, but it gave me a stronger voice and a clearer mission. I want to carry that spirit into the future as a data scientist and policymaker to be someone who doesn’t just use code to find trends but to challenge broken systems and build better ones for people like my parents, and the communities we come from. Now, I aspire to work and research on projects that truly interest me at the moment, and allow myself to understand that my interests may change in the future.

For me, success means leveraging artificial intelligence to transform how governments understand and address structural injustices. This scholarship will help me afford my education at my dream school, the University of Pennsylvania, which is a critical step in my goal to become a political consultant who uses quantitative machine-learning models to evaluate existing policies and develop more effective legislation. My current work provides a foundation for this vision. As an intern under Dr. Abhishek Pandey at Yale's School of Public Health, I am developing a regression classifier to identify the impact of 5 African coups on the continent's public health infrastructure. This information is crucial for developing targeted and effective aid strategies in volatile regions. Success for me would be if my research can influence policymakers who are looking to sustainably rebuild countries, potentially helping millions of African civilians. Similarly, my analysis as an intern at Harvard's Spatial Data Lab examining the correlations between the spatial distributions of hate speech on Twitter and the severity of local hate crime legislation has the potential to contribute to policymakers' understanding of hate speech dynamics and can help foster more inclusive online communities. Through this opportunity, I would be able to combine analytics with a philosophy to become a well-rounded decision-maker, using frameworks to critically examine the logic of data-driven conclusions and identify/account for structural injustices within AI. I outlined some of the success of my regression model with Yale's School of Public Health above, but truth to be told, I made some critical mistakes early in the research process. My solutions initially overlooked the structural capabilities of many African nations and implicitly used Western standards to determine the most effective infrastructure. While the model had a high accuracy rate, after many discussions with other researchers, I discovered that many of the proposed solutions would be unfeasible. These challenges revealed to me the importance of interdisciplinary learning and diverse perspectives in AI development. My technical skills alone weren't sufficient, I needed a deeper cultural understanding and policy context to create truly viable proposals. This experience fundamentally shaped my view of success in this field: it's not just about building models with high statistical accuracy, but about developing solutions that respond to real-world constraints and respect cultural contexts. I hope to use this scholarship to be able to afford this kind of holistic education, where I would be able to learn from experts across disciplines and explore diverse regional perspectives. This scholarship will enable me to deepen my expertise in both AI methodologies and social policy frameworks without the financial burden that might otherwise limit my academic pursuits. By combining technical skills with policy insights, I'll be positioned to develop AI solutions that optimize government strategies while prioritizing equity and inclusion. Ultimately, my success will be measured by my ability to influence policy decisions that improve millions of lives, particularly those from marginalized communities. This scholarship provides the financial support, knowledge, resources, and network needed to pursue this vision of using artificial intelligence as a force for creating more just and effective governance systems.

My debate round was slipping away. My opponent had just presented a flurry of statistics on economic downturns, and I needed a way to dismantle their argument, and find a loophole in the data. Then it hit me: the correlation they cited didn’t imply causation. I quickly reconstructed the numbers, reframing the evidence in my favor. The judge nodded. That was the moment I realized that raw data, without careful analysis, could be misleading. It wasn’t enough to have numbers—I had to understand what they truly meant. That instinct—to break down complexity, find patterns, and uncover the deeper truth—led me to my first Data Science course at Stanford, where I learned how algorithms can transform massive datasets into actionable insights and made my first model to predict national recessions using global financial indicators. Through this course, I realized data science wasn’t merely lines of code, but rather a powerful tool to drive policy action. Curious to learn more, I independently studied statistical modeling, AI, and machine learning through independent study under the Gifted and Talented program at school. My sophomore-year exploration of classification algorithms, from logistic regression to random forests, took on a personal meaning when a close friend’s struggle with effective antidepressant treatment inspired me to develop a model for optimizing medication dosages. In junior year, I studied geospatial information systems in Google Earth Engine to develop an erosion prediction model, learning to process satellite imagery and topographical data. My quest for doing scientific research continued and I approached professors for internship opportunities and was selected for two research internships. Under Dr. Pandey at Yale, I’m developing a regression classifier using machine learning to identify vulnerabilities in African public health infrastructure during conflicts. I hope to turn this research into legislation that will develop resilient infrastructure in these regions. At Harvard’s Spatial Data Lab, I’m analyzing the correlation between hate speech and local hate-crime legislation severity, hoping to increase policymakers' understanding of hate-speech dynamics and foster more inclusive communities. Through various STEM programs, I have been able to form a meaningful community with like-minded peers and explore different aspects of data science I wouldn’t have discovered on my own. These experiences have shown me how data science helps everything from restaurant choices to complex systems, a journey of analysis and innovation I’m eager to continue in college.