My grandfather-figure in this story is different from Joe Gilroy's, but the instinct is the same: I keep an actual running list, tracked in a notebook, of the next problem I want to solve, and I don't move to the next one until the current one has a working prototype. My goal is to build a career at the intersection of AI and medicine, developing machine learning tools that restore independence to people who've lost it, whether that's sight, mobility, or safety. Getting there requires a plan with real steps, not just a direction. Phase 1 (now through senior year): I'm continuing to build technical depth through coursework already three years ahead of grade level in math, alongside AP Computer Science and ongoing independent research. I'm currently developing BlazeAlert, a neural network trained on satellite data to predict wildfire risk, and maintaining "Algorithms Changing Lives," a published, competition-winning app using ML and AR to help visually impaired users. The plan here is resource-light by design: free/low-cost tools (PyTorch, public datasets like NASA FIRMS), science fair circuits that fund travel through award money rather than out-of-pocket cost, and mentorship through programs like Stanford's AIMI center and Keck Graduate Institute's SURE program, which I've already completed. Budget need here is modest, primarily covering competition fees and travel (roughly $500–$1,500/year), which I've been covering through smaller scholarships and prize money. Phase 2 (college): I'm targeting a Computer Science or Mathematics/CS double major at a research university with strong AI in medicine programs (Stanford, MIT, UC Berkeley, and UCLA are on my dream school list, but I've also had various public CSU schools as safety schools). This is the phase where cost scales up substantially, and it's the phase this scholarship would directly support. My plan is to seek out a lab position within my first year rather than waiting until upper-division coursework, using the research experience I've already built as leverage, and to pursue funded research assistantships and NSF-adjacent undergraduate programs (like REU) to offset costs rather than relying solely on family funding or loans. Phase 3 (graduate work and early career): Depending on how undergraduate research goes, I'm planning toward either a master's in a biomedical AI-adjacent program or moving directly into industry research roles at companies working on clinical ML, while staying involved in policy, since my work on Congressman Ted Lieu's Youth Advisory Council has taught me that good research needs good advocates in the room where decisions get made. I want to keep one foot in the lab and one foot in the policy conversation about how AI gets deployed responsibly in medicine. The biggest risk in this plan isn't ambition, it's resourcing. Every phase so far has required finding ways to keep costs low while still doing real work: free tools, competition funding, mentorship instead of paid programs where possible. This scholarship would go directly toward closing the gap in Phase 2, where costs jump the most and where the quality of the research opportunities I can access starts to depend heavily on not having to choose the cheapest option available. I plan my work because I've learned that ambition without a plan just produces good ideas that never get built. I intend to keep building mine.

College, to me, means the first place I'll get to build without asking permission. Everything I've made so far, from a machine learning app that helps a visually impaired grandfather in my life find his glasses, to a wildfire prediction model trained on satellite data, has been built in the margins: after school, between competitions, squeezed into summers. I've loved every version of that work, but I've also felt its limits. I don't have a lab. I don't have unlimited compute. I don't have researchers down the hall who've spent decades on the exact problem I'm circling. College is where those limits start to lift, where the thing I've been building quietly in a bedroom becomes something I can build in the open, with people who know more than I do and are willing to teach me. What I'm trying to accomplish isn't a single project or a single degree. It's a direction. I want to work at the intersection of AI and medicine, building tools that give people back independence they thought they'd lost, whether that's a visually impaired person navigating a room on their own or a community that gets a wildfire warning early enough to actually act on it. I've spent the last few years teaching myself pieces of that world: the math behind convolutional neural networks, the discipline of tracing a stubborn bug back to the one line of code quietly overriding a model's real output, the patience it takes to earn someone's trust in a tool built to help them. College is where I stop teaching myself in isolation and start learning from people who've already spent their careers on these exact questions. It's also where I want to keep the other half of what I do alive: the advocacy. Sitting across from California state Assemblymembers arguing for eBike safety legislation, or helping draft policy recommendations as Vice President of Communications on my congressional district's Youth Advisory Council, taught me that the best research in the world doesn't matter if nobody in the room with the power to act on it ever hears about it. I want a college experience that lets me keep building both halves of that: the lab work and the advocacy for why it matters, instead of treating them as separate tracks. Mostly, though, college means momentum. Every project I've finished so far has immediately turned into three ideas for what I want to build next, and I've had to let some of them go simply because there weren't enough hours, or resources, or people around me who understood the problem I was trying to solve. I don't want to keep letting good ideas go for lack of room to pursue them. I want to walk onto a campus and find out what I'm capable of when the walls around my curiosity finally come down, and then use whatever I build there to hand that same room to grow to someone else who hasn't found it yet.

The Last Circuit Maya's hands hovered over the competition field, adjusting the arm actuator by half a degree. Six minutes to go. Her VEX robot, Iris, sat motionless, waiting for the signal, while three feet away her teammate ran diagnostics on a sensor that had been glitching all season. "Confidence level?" her teammate asked. "Ask me after it doesn't tip over," Maya said. But she wasn't really worried about tipping. She was thinking about Mira. Iris was for the scoreboard. Mira was for something that mattered more. Mira was the robot Maya had actually built to matter, not for a scoreboard, but for her neighbor, an elderly man named Mr. Alvarez who'd started forgetting his own kitchen since his stroke last year. Maya still remembered the night his daughter had called her mother in a panic because he'd left the stove burning and wandered outside in his socks, looking for a dog that had died eleven years earlier. That was the night Maya decided a science fair ribbon wasn't going to be enough. She wanted to build something that could stand between Mr. Alvarez and the moments his own mind turned against him. Mira wasn't flashy. She didn't compete. She just watched, gently, for the moments he needed a nudge: the stove left on, the door left unlocked, the particular quiet that crept in around 4 p.m. every day when the light started to change and the house emptied out around him. The challenge with robots like Mira was never the robotics. It was trust. The first time Mira spoke up, "You left the stove on, would you like me to turn it off?", Mr. Alvarez had unplugged her and left her facedown in a closet for three days. Maya found out when his daughter called, apologizing, saying he'd yelled that he wasn't a child and he didn't need a machine raising him. Maya sat on her bedroom floor that night with Mira's limp chassis in her lap, feeling like she'd failed the one person she'd actually built this for. It wasn't the sensors. It wasn't the code. It was that she'd built a machine that acted like it knew better than the person it was supposed to serve, and she hadn't stopped to ask what it would feel like to be told, by a machine, that you couldn't trust your own memory. It took months of small, careful redesigns before he let her back into his kitchen. Maya rewrote the interaction loop four times that spring, late enough into the night that her mother started leaving mugs of tea outside her door without knocking. She stripped out every instinct the robot had to solve the problem before the person had finished feeling it, replacing "Problem Resolution" with a quieter directive: presence. Mira learned to notice the tremor in a voice, the length of a pause, the difference between a silence that wanted to be filled and one that just wanted company. The first time she got it right, Mr. Alvarez had been standing at the counter, staring at a photograph of his late wife, his hand pressed flat against the frame like he was trying to hold something in place. Mira didn't ask if he needed anything. She simply lowered her sensors and stayed, two feet away, patient as a held breath. He didn't say anything for a long time. Then, so quietly Maya almost missed it on the audio log the next morning, he said, "Thank you for not asking." Maya cried the first time she heard that recording. Not because the code had worked, but because somewhere in those four rewrites, she'd taught a machine something she was still learning how to do herself: how to stay in a room with someone's pain without trying to fix it. The opportunity in near future robotics isn't more capable machines. It's more humble ones, machines built to share a room with a person's dignity instead of quietly overriding it, machines that know the difference between being helpful and being in control. The hardest part was never the code. It was earning the right to be trusted with someone's independence, and earning it again every single day, because trust like that doesn't stay earned on its own. Maya thought about that every time Mira's sensors picked up the particular hitch in Mr. Alvarez's breathing that meant a bad day was coming, and instead of offering a solution, simply asked, "Do you want to talk about it, or would you rather I just stay?" Sometimes he wanted to talk. Sometimes he just wanted the light on. Mira had learned, slowly and imperfectly, to tell the difference, the same way Maya was learning it, one long night at a time. Back on the competition field, the buzzer sounded. Iris rolled forward, arm extending in a smooth arc Maya had rebuilt from scratch four times that season. The crowd noise blurred into background static as she watched the robot she'd built do exactly what she'd asked it to. It scored. Her team erupted, someone slapping her shoulder, someone else already shouting about the next match. But Maya's mind was somewhere else entirely, sketching the next version of Mira in the margins of her scorecard. She thought about all the rooms a machine like that might someday be trusted to sit in: hospital wards at 3 a.m. when no nurse could spare the hour, classrooms where a kid couldn't find the words for what was wrong, homes where the last living presence some nights was a small green light that knew enough not to fill every silence with noise. Asimov had written his robots bound by three laws, logic stacked on logic, all the way down. But Maya's world didn't run on certainty. It ran on the accumulated weight of a thousand small decisions about when to speak and when to simply stay, decisions no line of code could make without first learning what it felt like to need someone to just be there.

Selflessness, to me, isn't a single grand gesture - it's what you choose to do when no one's asking you to. One of my closest friends has a grandfather who suddenly lost his vision. I watched him change almost overnight, short-tempered, constantly frustrated, robbed of an independence he'd built over a lifetime. Simple things, like finding his glasses or his phone, became really hard. Nobody asked me to fix that. I just couldn't sit with watching someone lose their spark and do nothing, so I taught myself machine learning well enough to build him an app that helped him find lost items on his own again. That project became "Algorithms Changing Lives," which went on to place at the LA County Science & Engineering Fair and get published in the Future Scholars Journal, but none of that mattered as much as the fact that my friend's grandfather actually uses it. That instinct, to notice a gap and do something about it rather than just observe it, has shaped most of what I do outside the classroom. I foster rescue animals through local shelters, taking in cats who need care while they wait for a permanent home, work that isn't glamorous and often means late nights and early mornings for an animal who has no way of thanking you. I also volunteer nearly every weekend with the Palos Verdes Peninsula Land Conservancy, seeding, weeding, and restoring native habitat, work that won't show visible results for years, long after I've graduated and moved on. I keep showing up anyway, because the point was never recognition. That same conviction pushed me into public policy, a space most people don't expect a science student to end up in. As a Capital Convoy delegate, I sat across from California state Assemblymembers and Senators arguing for eBike safety legislation, not because it personally affected me, but because I'd seen how a policy gap have put people in my community at risk. As Vice President of Communications on Congressman Ted Lieu's CA-36 Youth Advisory Council, I've spent nights researching legislation and helping identify weaknesses in bills under consideration, work that isn't visible to anyone outside that room but has led to real bill cosponsorship. Perseverance, for me, has meant staying in that work even when it's slow, unglamorous, and mostly invisible, because the goal was never to be seen doing it. I think that's the thread connecting all of it: an app built for one person who needed it, an animal fostered with no thanks, a habitat restored for a future I won't be around to fully see, a policy argument made in a room where credit was never the point. Selflessness isn't about the moments people notice. It's about choosing to keep showing up for the moments they don't. I want to keep being someone who does that, whether it's in a research lab, a shelter, a conservancy trail, or a state legislator's office, because the people and places that need help rarely announce themselves loudly. You just have to be paying attention.

I love math because there's no room for ambiguity in it. A model either predicts correctly or it doesn't; an algorithm either runs efficiently or it doesn't. There's something grounding about that kind of precision, especially compared to how messy most real world problems are. That precision is part of why I've always pushed to go further with it. As a sophomore in high school, I'm three years ahead of grade level in math, currently taking college level calculus, and every step further has only made me love it more. The deeper you go, the more math describes things that actually matter. Getting this far hasn't been free, and with more advanced coursework, competitions, and eventually college ahead, this scholarship would go directly toward making sure cost isn't what decides how far I get to take this. My favorite part of math isn't the clean textbook version, it's the moment a problem stops being abstract and starts describing something real. When I built my first machine learning model, optimizing an object recognition algorithm to run on a phone for a visually impaired user, I wasn't just solving equations. I was using linear algebra and probability to decide whether a shape in front of a camera was a set of keys or a shadow. That's when math stopped feeling like a subject and started feeling like a tool I could actually use to change something. That feeling only got stronger as the math became more advanced. Training a neural network to predict wildfire risk meant sitting with a stubborn bug for weeks, tracing it back to a piece of bandaid code overriding the model's real output with a placeholder. Fixing it wasn't about more formulas, it was about trusting the math enough to find exactly where the logic broke. When the model finally hit 91.85% accuracy, it wasn't just a number, it was proof that the reasoning underneath it actually held up. I think that's what I love most about math: it rewards patience and precision in a way that feels earned rather than just luck. Every field I care about, AI, medicine, public policy, eventually comes down to a question math can help answer, if you sit with it long enough to find the pattern. Math taught me how to think in a way that's carried into everything else I do, and I don't think I'll stop finding that useful.

I chose STEAM because of a moment I couldn't walk away from. One of my closest friends has a grandfather who suddenly lost his vision, and I watched him change almost overnight - short tempered, constantly frustrated, robbed of an independence he'd built over a lifetime. Before I understood anything about machine learning models or algorithm efficiency, I just wanted to give him back a small piece of what he'd lost. So I built him an app. That project is what pulled me fully into STEAM. What fascinates me most is the space where technology meets real human need - not science as a something abstract, but science built to solve a problem someone actually has. Making something that worked for him meant teaching myself real computer science: the math behind convolutional neural networks, the tradeoffs between accuracy and processing speed, what it takes to build something reliable enough for someone to depend on. "Algorithms Changing Lives" won 1st place at the LA County Science & Engineering Fair and was later published in the Future Scholars Journal - but the version that matters most to me is the one my friend's grandfather actually uses. That belief, that STEAM should be built around real people's problems, is why I kept going. I trained a neural network from raw satellite data to predict wildfire risk with BlazeAlert, working through a stubborn bug that had been silently short circuiting my model's real predictions until I tracked it down. And one year ago, I spent a summer at Stanford's Center for Artificial Intelligence in Medicine and Imaging doing clinical AI/ML research - the experience that convinced me my long term path is building AI systems for medicine. I want to spend my career at that intersection, using AI not as an end in itself but as a tool aimed at people who need it most. I've also learned that a good idea doesn't go anywhere on its own - it needs someone willing to advocate for it. As a Capital Convoy delegate for my school district, I sat across from California state Assemblymembers and Senators arguing for eBike safety legislation, and as Vice President of Communications on Congressman Ted Lieu's CA-36 Youth Advisory Council, I've helped research and draft legislative recommendations that led to real bill cosponsorship. That experience taught me that STEAM and advocacy aren't separate tracks. The best ideas still need someone to fight for them in the room where decisions get made. This scholarship would mean starting to build toward college now, rather than waiting until the cost of that door becomes the thing that decides whether I get to walk through it. I still have two years of high school to go, but I've already seen how quickly research, competitions, and opportunities add up in cost. I'd rather start saving toward that future early than scramble for it later. More than the dollar amount, being recognized through a scholarship carrying Kayla Nicole Monk's name, someone who wanted to build things and help people on her own terms, is exactly the kind of legacy I want my work to be in. College is where I'll get to take everything I've started, from assistive technology for the disabled to wildfire prediction, and turn it into something with real reach.

Change is not something spoken, it's not something written, it's something done. I learned that first not in a classroom, but watching someone I love lose the confidence to move through his own house. One of my closest friends has a grandfather who suddenly lost his vision. I watched him change almost overnight - short tempered, constantly frustrated, robbed of an independence he'd built over a lifetime. Simple things, like finding his glasses or wallet, became extremely hard. He lost his spark along with his sight, and I couldn't just watch that happen. I built him an app before I understood anything about machine learning models or algorithm efficiency - I just wanted to give him back a small piece of what he'd lost. That instinct, that a problem in front of you demands action, not just observation, is the thread running through everything I've done since. Making something that actually worked for him meant learning real computer science: the math behind convolutional neural networks, the tradeoffs between accuracy and speed, what it takes to build something reliable enough for someone to depend on. "Algorithms Changing Lives" won 1st place at the LA County Science & Engineering Fair, was published in the Future Scholars Journal, and I presented it to Members of Congress at the U.S. Capitol - but the version that matters most is the one my friend's grandfather actually uses. That same belief, that science should be built around real people's problems, led me to train a neural network from raw satellite data to predict wildfire risk with BlazeAlert, and to spend a summer at Stanford's Center for Artificial Intelligence in Medicine and Imaging doing clinical AI/ML research that shaped my long term goal of building AI systems for medicine. But science doesn't defend itself, and I learned that firsthand somewhere I didn't expect: the California State Capitol. As a Capital Convoy delegate for my school district, I sat across from state Assemblymembers and Senators arguing for eBike safety legislation, translating a public safety problem into language that could move policy. That's the same instinct I bring to my role as Vice President of Communications on Congressman Ted Lieu's CA-36 Youth Advisory Council, where nights spent researching legislation and finding cracks in the issues we discussed led to real bill cosponsorship, not just conversation. I've carried that conviction into every room I've been part of: as Vice President of the Computer Science National Honor Society, as Treasurer of our TSA chapter helping our team compete nationally in data science, as a member of my school's Principal's Advisory Committee pushing to establish a dedicated mental health room for students, and years earlier as a student representative on PVPUSD's Superintendent's Roundtable. Change is never easy, and it rarely announces itself. It's a grandfather relearning independence. It's a bill cosponsored because a student did the research first. It's a wildfire risk caught before it becomes a headline. What's at stake if we stop championing science is exactly that: the tool that never gets built, the policy made without good data to back it up, the voice that stays quiet because no one thought a student's research mattered. I don't intend to stay quiet. I want to keep showing up to close that gap, because I've seen what happens when someone does.

When we think of our problems, so many come to mind. What if we thought about it the other way around? When we look around, we see the beauty of life - imagine not being able to. There are 49.1 million people in the world that are blind and 295 million people are MSVI - mild to severely visually impaired and when we see their hardships, we view life from a different perspective. My close friend’s grandfather was recently affected with blindness. Every time we visited them, it was heartbreaking to see him struggling. I noticed that he had trouble finding misplaced items. With further research, I realized he was not alone. This was a pressing issue for blind individuals all around the world (Hicks, Strange, Wright 2013). This inspired me to make something to help the blind population find their belongings. Looking for ways to help, I came across a classmate who has hearing difficulties, and because of this, he can't hear the lessons as well. So, he uses a device that the teacher wears to hear the lessons better. I was amazed that products like this exist and I was inspired to make my own. My app, “Lost It? Loc8 It!”, is a lost and found app for the blind. When we lose our belongings, it can sometimes be really hard to find them. But it can be even harder to find lost things when a person is afflicted with visual impairment. “Lost it? Loc8 it!” can be launched using blind user-friendly voice commands . My app uses a phone’s camera in place of a human eye to scan the user’s surroundings to locate their lost items. To input an item in the database, the user or the user’s assistant can take a picture of the item through the app. This gives the app data to train the cameras to find the specific item. A lot of apps that are currently in the market use AI to tell visually impaired individuals of the items in front of them. This makes finding lost items incredibly difficult, because the user is flooded with irrelevant information on objects. Once the item has been found, my app utilizes blind-friendly vibrations and vocal announcements to notify when the object has been detected. This app gave me the drive to accomplish the satisfaction of helping others. Through this project, I grew passionate about AI as well as it's positive impact on society. I hope to one day create more technologies to help my peers and to give back to the community who raised me to be who I am today. As I am a first generation girl, this scholarship will really help me make an even bigger impact on society for the good.

AI has revolutionized the way we see the world today. There is a bad side to this, but when used for the good, AI can help change lives. I have coded an app, “Lost It? Loc8 It!”. It is a lost and found app for the blind. When we lose our belongings, it can sometimes be really hard to find them. But it can be even harder to find lost things when a person is afflicted with visual impairment. Lost it? Loc8 it! can be launched using blind user-friendly voice commands. My app uses a phone’s camera in place of a human eye to scan the user’s surroundings to locate their lost items. To input an item in the database, the user or the user’s assistant can take a picture of the item through the app. This gives the app data to train the cameras to find the specific item. This is what makes my app different and original - it helps the user find their specific items and it is personalized to fit the user’s needs. A lot of apps that are currently in the market use AI to tell visually impaired individuals of the items in front of them. This makes finding lost items incredibly difficult, because the user is flooded with irrelevant information on objects. My app can even identify specific details to the extent of telling two different books apart using an object recognition algorithm. Once the item has been found, my app utilizes blind-friendly vibrations and vocal announcements to notify where the object has been detected. algorithm to assist the visually impaired. When coding the base structure of the app, I found it difficult to find a compatible machine learning model that was both accurate and implementable. I was planning on using the YOLO algorithm, but I decided to switch to a Tensorflow model which could be accommodated to the app easier. I did this without compromising the accuracy of the object detection by using more pictures of the object from all views. This gave more data for the program to train with. AI has allowed me to connect to my community in ways I never thought I would. By giving back to the community and learning the roped of AI, I truly feel like I can put a smile on a persons face and make their life a little easier.

Math is merely not just a subject, its a perspective, a way of looking at the world. A way that allows you to unlock the secrets of the universe. Math is my favorite subject because it helps me understand the world around me. I love math because there's usually one answer, but the path to derive the answer can often be so different. Math requires us to think critically and logically, helping develop problem solving skills in the real world. No matter how complicated our lives may see, math will always be the same, black or white, yes or no. When we don't understand a math concept, we ask why. Why does this work the way it does? It just takes a spark of curiosity to turn into a domino effect that ends with you diving deep into the concept and gaining a whole new understanding in math. But more than what math makes us do, math is all around us. We are being engulfed in numbers, equations and theorems so much, that we often don't think that much about them. The life applications of math are endless. Math is not just a class where everyone receives a lot of homework - its a tool, or in other words, a key. It is the key to understanding ourselves, and the world around us. Math is the fuel for future generations to put minds together and tackle some of the most difficult issues our world faces today. Math encourages unity, inspires the depth of learning, but most importantly, it encourages us to be more curious about the place we call home.