When I think about the impact I want to make, I picture my aunt sitting beside the road in Mundamarai, my family’s village in India, selling vegetables. Growing up, I treated scenes like that as ordinary. I’d walk past her stand without thinking about how much uncertainty sits behind a small business whose income depends on the season and whether enough people stop that day. My aunt never called herself an entrepreneur. She opened her roadside stand each day and learned exactly what her regular customers needed. Watching her made small business feel ordinary to me. Only later did I understand how much skill can exist without the paperwork or financial record that institutions know how to measure. Years later, I started researching microlending. I worked with data from more than 2.6 million Kiva loans across 87 countries, trying to predict which borrowers were most likely to receive funding. At first, I approached the project like a student who liked machine learning. I cared about improving my model and fixing the parts that performed poorly. Then I began interviewing people connected to villages like Mundamarai. Those conversations changed what the rows in my dataset meant to me. A loan request for farming equipment could belong to someone whose livelihood depends on one harvest. A few hundred dollars for inventory could mean more stability for a roadside seller like my aunt. I started asking a different question: who gets overlooked when platforms decide where money should go? That question is a large reason I chose to study data science at UC Berkeley. I want to learn how products make decisions about people, especially when those decisions affect access to money or opportunity. My goal is to become a product data scientist and work on systems used by millions of people. I want to study where users drop off and whose needs are missing from the data. Then I want to change products so they work better for people who have less room for error. I also want my education to help communities like the one that raised my family. I’m continuing my microlending work with the hope that the model can eventually help surface borrowers who might otherwise receive less attention. I’m especially interested in designing tools for small business owners in communities where formal credit is hard to access. If a person has years of discipline running a business but little traditional financial history, I don’t think a blank credit file should make that experience invisible. My parents have shaped this goal more than anyone. They brought me to the United States while keeping me connected to Mundamarai, even when I was too young to appreciate why that mattered. I used to complain when they stopped to take pictures during visits. Now those pictures are some of the things I value most because they preserve people and routines I assumed would always be there. They taught me, without giving me a speech about it, that progress means more when you remember who made your opportunities possible. I want to carry that idea through Berkeley and into my career. I don’t expect one model or one company to solve unequal access to opportunity. I do know that data influences more decisions every year, and I want people from places like Mundamarai represented in those decisions. Someday, I want to return to my aunt’s roadside stand with more than memories of it. I want the education I received thousands of miles away to help build systems that recognize the work happening there and give people like her a fairer chance to grow.

The hardest school decision I made happened at a checkout screen. I had prepared for AP Environmental Science and AP Physics C, yet the exam fees sat beside groceries and housing costs my family was already carrying. I closed the page and canceled both exams. No teacher saw the calculation behind that click. At school, it looked like a schedule change. At home, it felt like admitting that effort alone couldn’t make every opportunity affordable. I grew up in a Southeast Asian immigrant family where money is discussed quietly. My parents came to the United States with the pressure of starting over and the fear that one mistake could follow us for years. As their first child to attend a four-year college, I’ve learned that my education belongs partly to them. Every application asks for my name, but I picture my parents whenever I press submit. Their sacrifices shaped my goals, and their worry sits beside my excitement. I became my family’s unofficial college guide before I understood the system myself. I compared aid rules and reread forms late at night because one missed box could cost thousands. Sometimes my parents asked questions I couldn’t answer. I’d say I was figuring it out, then return to my room and stare at the screen until the numbers stopped blurring. Being first-generation has meant learning in public while hiding how uncertain I feel. My immigration status has made that worry harder to ignore. I live in the United States on an H-4 dependent visa. When I turned eighteen, I became old enough to take on more responsibility, yet my status still kept me from accepting paid employment. I watched classmates apply for summer jobs without studying federal rules first. I learned to read opportunity descriptions for hidden barriers, such as citizenship or work authorization requirements. Rejection sometimes arrived before anyone read my résumé. I still searched for ways to contribute. I worked as a soccer referee across more than 400 matches, where I had to make unpopular decisions while parents shouted from a few feet away. That experience taught me to stay steady when approval disappears. I also tutored students and helped lead a research team. Those roles gave me something immigration paperwork couldn’t: proof that I could be useful. My interest in data science grew from wanting to understand why opportunity passes over some people. I studied more than 2.6 million loans from Kiva, combining lending records with economic data to examine how machine-learning systems might evaluate borrowers. Each row represented someone asking to be trusted. Building the model forced me to confront how easily an algorithm can turn poverty into a risk score. My paper was accepted by the Vanderbilt Young Scientists Journal. The borrowers stayed with me. Their futures could be narrowed by a pattern they never chose. This fall, I’ll attend the University of California, Berkeley to study data science. I want to build machine-learning systems that recognize the human cost of their predictions. My family’s immigrant experience has made financial insecurity feel physical: a tightened voice during a bill discussion and an exam canceled after months of preparation. It has made me careful about the power I hope to hold as an engineer. A scholarship would loosen the fear that one expense could undo years of preparation. It would let my parents see college as a place where I’m allowed to grow without adding another risk for them to absorb. I carry their unfinished certainty with me. At Berkeley, I intend to turn it into work that gives families like mine a fairer chance to be seen.

Selected paragraph: Bhagavad Gita, Chapter 2, verses 47 - 48, my working translation "Your claim is to action alone, never to its fruits. Do not let the fruit of action be your motive, and do not become attached to inaction. Fixed in discipline, perform your work, abandoning attachment, and remain even-minded in success and failure. Such evenness is called yoga." The Bhagavad Gita teaches that disciplined work gains moral value when a person gives full attention to the task before them without letting reward become the center of the self. This passage does not praise laziness or emotional distance. It asks the reader to work with care and accept uncertainty without letting outcomes flatter or wound the ego. It makes ambition quieter and more durable over time. That standard is harder than winning because it measures what I choose when no result can protect me from doubt itself. That lesson matters because college will keep testing my patience and intent daily. The first phrase, "Your claim is to action alone," begins with a limit. The word "claim" matters because it sounds legal, almost like a boundary drawn around human power. A person can claim effort. A person can claim preparation. A person can claim the choice to show up again after disappointment. The passage does not say that outcomes are meaningless. It says that outcomes are beyond ownership in the same way the weather is beyond ownership. We live with them and learn from them, yet we never fully possess them. That idea became real to me through machine learning before I had language for it. When I trained my first serious model on Kiva microfinance data, I wanted the numbers to prove that my work mattered. I cleaned loan records and watched the metrics change with each experiment. Some days, precision rose after one small fix. Other days, a model looked strong until I checked where it failed. The data kept reminding me that effort and result have a tense relationship. I could control how carefully I built the pipeline. I could not force the model to become useful or trusted simply because I wanted it to. That slow lesson mattered more than a clean leaderboard score because it taught me to respect the gap between intention and effect. The phrase "action alone" also changes the meaning of ambition. Ambition usually points forward, toward a future prize: admission or scholarships. I have chased those goals. I have stayed up with code and scholarship essays because I wanted doors to open. Yet the Gita suggests that ambition becomes dangerous when it makes the present task serve only as a ticket to something else. If I treat research as a way to decorate a resume, I stop seeing the borrowers behind the spreadsheet. If I treat tutoring as a leadership bullet, I stop hearing the student stuck on the error message. The action loses its honesty. The second sentence says, "Do not let the fruit of action be your motive." The word "fruit" is a simple metaphor, but it carries weight. Fruit grows after labor and waiting, under conditions no worker can fully command. A farmer can tend soil and water plants, yet weather and season still matter. By using fruit as the image for reward, the passage shows why outcomes can tempt us. Fruit is sweet. It is visible. It can be counted and displayed. Effort is quieter. Most of the work that changes a person happens before anyone sees it. This line also warns against making reward the inner reason for work. A motive sits deeper than a goal. Goals can guide. Motives shape character. When the fruit becomes the motive, the self starts leaning toward applause and comparison. I have felt that pressure during college applications and scholarship season. Each form asks for a clean version of life: awards and proof. There is value in documenting work, yet the process can make every activity feel like evidence for a judge. The Gita asks a harder question: would I still do the work if no one added it to a profile? My answer has not always been perfect. I remember helping as a teaching assistant for Folsom Lake College's machine learning bootcamp. A student had a Jupyter notebook error: "Found input variables with inconsistent numbers of samples." He looked embarrassed and insisted he had not changed anything. The quick route would have been to fix it for him and move on. Instead, we printed the lengths of X and y, then traced the filter and rebuilt the split. A few minutes later, another student asked what had happened. He turned his laptop and explained it himself. No metric could capture that shift. The fruit was his confidence, and I did not own it. Then the passage adds a second warning: "and do not become attached to inaction." This clause prevents the first half from being misread. If results are uncertain, a person may choose safety. They may say effort is pointless because outcomes cannot be controlled. The Gita rejects that escape. Detachment is not withdrawal. It is the condition that makes brave work possible. When a person no longer needs a guaranteed reward, they can act before certainty arrives. This part speaks to my family's immigration situation. As an H-4 visa holder, I have learned that some doors close for reasons unrelated to merit. Paid work and many scholarships can become complicated before anyone reads my qualifications. It would be easy to let that uncertainty become an excuse for inaction. Instead, I have built projects and searched for scholarships that fit my status. I do not control the policy landscape. I do control whether I keep working with the options available today. The next sentence begins, "Fixed in discipline, perform your work." The word "fixed" suggests steadiness, yet it does not mean being rigid. A disciplined person can change strategy without changing purpose. In machine learning, this distinction matters. When a model fails, discipline is not repeating the same training run out of pride. Discipline is checking the confusion matrix and accepting that the first design may be flawed. The work continues because the purpose remains. Discipline also connects the passage to close reading itself. A surface reading of the Gita might turn it into a slogan about ignoring results. A closer reading notices how many commands refer to action. The passage keeps moving through direct verbs. It gives no shelter to passivity. Its calm tone can hide how demanding it is. To work without clinging to reward means to keep showing up after rejection. It means to revise without self-pity. It means to care about quality even when recognition is delayed. The phrase "abandoning attachment" is the emotional center of the paragraph. Attachment is different from care. Care makes the work better. Attachment makes the ego fragile. When I worked on my microfinance model, care meant asking whether the features were responsible and whether borrower selection could become less biased. Attachment meant wanting the project to confirm that I was impressive. Care pushed me toward better questions. Attachment made every flaw feel personal. The Gita asks the worker to keep care and release possession. This distinction matters for technology because technical work can hide ego behind numbers. A model can have high recall and still harm people if the problem is framed poorly. A dashboard can look clean and still lead users toward bad decisions. If I am attached to the fruit, I may defend the output because it carries my name. If I am attached to the action, I can admit when the work needs repair. That humility is part of the discipline the passage demands. The final command is to "remain even-minded in success and failure." Even-mindedness is easy to admire and hard to practice. Success can distort judgment as much as failure. A publication or acceptance letter can make a person believe the latest outcome reveals their worth. A rejection can make the same person believe the opposite. The Gita places success and failure side by side because both can pull the mind away from honest work. Both create noise. I felt that noise when my research was accepted by Vanderbilt's Young Scientists Journal. I was proud because the project had taken real effort, and the recognition helped me believe I could contribute to applied machine learning. Yet after the first excitement, the same question returned: what does the work do for people? That question led me back to Kiva and to the goal of seeing whether my credit-scoring model could support borrower selection. Publication was fruit. The next action was improving the work so it could be used with care. Failure has taught the same lesson with a harsher voice. Rejected applications and visa limits can make effort feel invisible. During those moments, this passage does not tell me to pretend disappointment feels good. It tells me to keep disappointment from becoming the author of my next decision. Even-mindedness is not the absence of feeling. It is the refusal to let feeling take over the steering wheel. The last line, "Such evenness is called yoga," gives the passage its deeper meaning. In common use, yoga often refers to posture or exercise. Here, yoga means discipline joined with a trained steadiness of the self. The passage defines spiritual maturity through the way a person works. It does not separate thought from action. It does not let the reader hide noble values in private belief. Evenness must appear in the lab, the inbox, and the small choices no one praises. For me, this reading has changed how I think about impact. I want to become a machine learning engineer because I care about systems that help people at scale. That goal can sound large, yet the Gita pulls it back to daily practice. Clean the data carefully. Ask who may be harmed. Teach the student to debug instead of taking the keyboard. Write the follow-up email even when the answer may be no. Keep doing the next right task without turning every outcome into a verdict. That is why the line feels useful for artificial intelligence. Many people judge technology by the shine of the finished tool. The Gita asks me to look earlier, at the worker's state of mind during design. Was I patient enough to test edge cases? Was I honest enough to question a feature that helped accuracy while risking unfairness? Was I willing to slow down when a rushed answer would look impressive? These questions turn ethics from a final checklist into a habit inside the work itself. They make close reading practical because the same attention used on a sentence can be used on a system. The passage's underlying meaning is that work becomes cleanest when the worker stops trying to own the future. This is a demanding idea for a student standing at the edge of college, especially one trying to pay for Berkeley and build a career in artificial intelligence. So much feels tied to results. Scholarships matter. Grades matter. Opportunities matter. The Gita does not erase that reality. It asks me to live inside it without letting it consume my character. Close reading reveals the passage's balance. It limits human control, then commands action. It warns against reward, then warns against escape. It asks for discipline, then defines that discipline as evenness. The movement of the paragraph mirrors the life it recommends: step forward, release the claim to what follows, then steady the mind again. I do not read this as comfort. I read it as responsibility. If my claim is to action alone, then I cannot wait for perfect certainty or guaranteed recognition before I begin. I have to work where I stand for the people my work may serve. That is the kind of close reading I want to carry into college: reading texts closely enough that they change how I build, and building carefully enough that my actions become worthy of what I have read.

For a long time, I treated mental health like a problem I could outwork. If I felt worried, I added another assignment to my planner. If I felt scared about money, I searched for another scholarship. I opened my laptop late at night and coded until the screen became easier to face than my own thoughts. That habit helped me achieve a lot, yet it also taught me how easy it is for a student to look fine from the outside while carrying fear in private. My experience with mental health began at home, during a period when my family was dealing with illness and financial uncertainty. My mother’s health challenges changed the rhythm of our house. Some days were quiet in a way that made every small sound feel larger, especially the door closing softly while my father took a call in another room. I wanted to help, so I tried to become the person who needed less. I kept my grades high while taking community college classes, and I applied for scholarships without adding stress to my parents. At the same time, my immigration status added another layer of pressure. I am in the United States on an H-4 visa, and my family has been waiting in the employment-based green card backlog for more than a decade. Because of that status, I am ineligible for FAFSA and the California Dream Act Application, even though California has been my home for most of my life. When classmates talked about financial aid as a normal step before college, I felt trapped in a category that few people understood. I had worked hard enough to earn admission to UC Berkeley as a Data Science major, yet I still had to wonder whether the cost could make that opportunity unstable. That stress shaped my mental health in a quiet way. I did not have one dramatic moment where everything broke. It was more like carrying a backpack that became heavier each month. After finishing AP homework and community college labs, I checked scholarship portals late at night. I told myself that every hour of effort had to move my family closer to relief. When I became tired, I felt guilty. When I received good news, I felt pressure to turn it into the next application. Over time, I learned that achievement can hide anxiety. A transcript can show a 4.0 GPA and more than 70 college units. It cannot show the fear behind a FAFSA form I could not submit. It cannot show the nights when I wondered if my parents’ sacrifices would be enough while trying to seem grateful and prepared, and still needing reassurance. That gap changed how I saw real success too. This experience changed my goals. Before, I saw machine learning mainly as a field where I could solve hard problems with data. I still love that work. I enjoy cleaning messy datasets and testing models that find patterns a person might miss. My research on microfinance helped me see a deeper purpose for those skills. I studied Kiva loans and built a model to identify reliable borrowers who might lack formal credit histories. That project mattered to me because my own life has taught me how systems can overlook people when they do not fit neat categories. Seeing data connect to real lives made my career feel tied to fairness, with model accuracy becoming one measure of useful public service. A person can be responsible and still have no access. A student can be qualified and still face barriers that a form does not capture. At Berkeley, I want to study Data Science with that lesson in mind. I hope to become a machine learning engineer who builds tools that help people receive support earlier. Technology should support human care by making hidden needs easier to notice. A model can never understand a family’s full story, but it can help a counselor or school see who may be struggling before silence becomes the default. My experience with mental health also changed my relationships. I used to think helping meant being useful. If someone had a problem, I wanted to fix it. That mindset made sense to me because I was used to solving problems through work. As a soccer referee, I learned to stay calm when parents shouted from the sideline. As a teaching assistant for a machine learning bootcamp, I helped students debug code when they felt stuck. Those roles taught me that support often begins with patience before advice. Now I try to listen for what people are leaving unsaid. If a friend says they are tired, I do not assume they need a productivity tip. If a student asks the same question twice, I do not assume they were careless. When I created a Discord space for students on H-4 visas, I wanted it to be more than a list of opportunities. I wanted it to be a place where students could say, “This is confusing,” without feeling embarrassed. That kind of honesty can lower the shame that keeps people isolated. My understanding of the world is less judgmental now. I used to separate people into those who were prepared and those who were behind. I now know that preparation depends on what someone has been carrying. Some students study after caring for family members. Some fill out applications while translating bills for their parents. Their effort may be invisible, even when their need is real. Mental health has taught me to value quiet forms of strength. It has made me more careful with other people’s stories and more honest about my own limits. I still believe in hard work. I also know that people need support and language for what hurts. My goal is to build a life where my technical skills serve that belief. If I can use data science to make overlooked struggles more visible, then my own experience with pressure will become part of how I help others feel seen.

Jack Terry's story inspires me because he proves that adversity doesn't have to end a life's purpose. He survived unimaginable loss, came to a new country alone, and rebuilt his life through education and service. He turned suffering into strength and strength into service. That example has shaped how I think about my own obstacles and the kind of life I want to build. My background echoes Jack Terry’s in a different way. Although I’ve lived in the United States for more than fifteen years, on paper, I’m still treated as an outsider. In ninth grade, I was driving home from Disneyland, replaying the best parts of the day, when I opened the eligibility page for MITES, MIT’s summer STEM program. One line changed everything: because I’m on an H4 visa, I was considered an international student and couldn’t apply. Once I noticed it, I couldn’t unsee it. CMU AI Scholars, MIT PRIMES, AI4ALL, and others I’d worked toward all followed the same pattern. It wasn’t my effort that failed. It was the fine print. The barriers extended far beyond summer programs. My visa status means no FAFSA, no work authorization, and tuition costs approaching $100,000 annually at many schools. Most internships, scholarships, and research programs begin with the same quiet requirement: U.S. citizen or permanent resident only. One night, frustrated, I asked my parents why we'd come here at all. My dad answered: to give me the best education and opportunities possible. The irony stung, but I understood. Both my parents had grown up in small villages in India and taken a leap into the unknown for my future. I realized I could either let these limits define me—or respond with the same courage they had shown. What I've learned from adversity is that resilience is about building community where none seems to exist. I started a Discord server with two other H4-visa students. What began as a group chat evolved into a larger support network of thirty-two students sharing deadlines, resources, and opportunities we were actually eligible for. We set one firm norm: share opportunities early, even when uncertain, because scarcity is what keeps barriers in place. Then the community became something deeper than a spreadsheet of programs. When my mom became bedridden, my schedule doubled. I managed meals, medication, chores, and care for my younger sister while keeping up with schoolwork. One night at 9:45 p.m., a friend from that server knocked on my door with homemade Roti and Sabji. He apologized for the late hour, but all I could think was how grateful I was that someone I'd met through a screen cared enough to show up in person. Struggle can isolate people, but it can also bring them together with empathy and purpose. That lesson now shapes how I want to use my studies to give back. I plan to study data science to solve problems that affect people overlooked by existing systems. I've already started my microfinance research. Inspired by my family's village of Mundamarai, India, I built a machine learning model analyzing 2.6 million microloans to identify reliable borrowers without traditional credit scores. In places like Mundamarai, hardworking families are excluded from banks simply because they lack formal credit histories. I watched neighbors turn to loan sharks because trust and character didn't count. I want to build tools that expand fair access to credit for communities like Mundamarai around the world. Jack Terry's life reminds me that, although hardship can wound a person, it doesn't have to limit what they become. Inspired by him, I want my response to adversity to be service.