How can we provide food to those who need it most in a data-driven way? I’ve grappled with this question for the past 6 years—from handing out burritos in my Oregon hometown to developing statistical models that target Houston’s highest-risk zip codes. In May 2020, I began volunteering with Burrito Brigade, a nonprofit organization in Eugene that distributes 500 burritos every weekend to the unhoused and food insecure. As I delivered burritos to my regulars, I learned about the deep-rooted health and economic challenges that kept them in a cycle of insecurity. Every Sunday at 3pm I delivered 5 burritos to Jane, an elderly woman, who could not chew the federally-provided food because of her dental problems and allergies, and she needed our burritos to survive. These conversations with our clients humanized the debilitating systemic social determinants of health to me; learning about how Jane had to jump through multiple hoops just to eat empowered me to fight for vulnerable individuals. As I continued volunteering, I realized something that was unexpected as I dropped off burritos to the Little Free Pantries around town–some pantries would empty instantly, while others remained untouched for hours. This discrepancy intrigued me—it revealed this concept of efficient resource distribution. To address this, I managed and optimized our 50+ Little Free Pantry network, researching ideal placements based on need and foot traffic. I also created a digital interactive map displaying pantry locations and real-time availability. Yet, I knew it could be better- it still lacked a precise way to rank areas by priority. Motivated by this idea, I pursued a double major in Statistics and Health Sciences at Rice University, a university dedicated to civic empowerment. Now, I use advanced programming and statistical modeling to identify target zip codes for Kid’s Meals, a nonprofit organization in Houston tackling food insecurity for children. My approach incorporates variables like SNAP enrollment, poverty levels, transportation access, overcrowding, and family structure, enabling Kid’s Meals to expand its reach by 30% over the next three years and provide meals to thousands of additional families. Beyond numbers, I integrate qualitative insights from 'Hope Providers'—delivery drivers who build relationships with families. Their stories have expanded my perspective, shaping my consideration of high school education and ESL programs as crucial factors in food insecurity. Each data point represents a real person—someone like Jane, whose struggles underscore systemic failures in food access. By bridging data science with human-centered solutions, I am committed to making sure that food reaches those who need it most. The complexity of food insecurity captivates both my analytical and humanitarian instincts. I believe math holds the power to drive social change, and through data-driven solutions, we can ensure food reaches those who need it the most. Looking ahead, I plan to pursue graduate studies in epidemiology with an emphasis on spatial analysis and the social determinants of health. The lessons I’ve learned from food insecurity work—how to integrate quantitative analysis with qualitative community insights to drive efficient, equity-focused solutions—are lessons I want to apply across other public health and social service challenges. Whether it's optimizing access to reproductive healthcare, addressing rural environmental health burdens, or improving resource allocation in social services, I want to use data-driven strategies to enhance the efficiency and impact of interventions. My experiences in food insecurity have reinforced that solutions must be both analytically rigorous and deeply human-centered—a philosophy I will carry forward in my research and advocacy.

How can we provide food to those who need it most in a data-driven way? I’ve grappled with this question for the past 6 years—from handing out burritos in my Oregon hometown to developing statistical models that target Houston’s highest-risk zip codes. In May 2020, I began volunteering with Burrito Brigade, a nonprofit organization in Eugene that distributes 500 burritos every weekend to the unhoused and food insecure. As I delivered burritos to my regulars, I learned about the deep-rooted health and economic challenges that kept them in a cycle of insecurity. Every Sunday at 3pm I delivered 5 burritos to Jane, an elderly woman, who could not chew the federally-provided food because of her dental problems and allergies, and she needed our burritos to survive. These conversations with our clients humanized the debilitating systemic social determinants of health to me; learning about how Jane had to jump through multiple hoops just to eat empowered me to fight for vulnerable individuals. As I continued volunteering, I realized something that was unexpected as I dropped off burritos to the Little Free Pantries around town–some pantries would empty instantly, while others remained untouched for hours. This discrepancy intrigued me—it revealed this concept of efficient resource distribution. To address this, I managed and optimized our 50+ Little Free Pantry network, researching ideal placements based on need and foot traffic. I also created a digital interactive map displaying pantry locations and real-time availability. Yet, I knew it could be better- it still lacked a precise way to rank areas by priority. Motivated by this idea, I pursued a double major in Statistics and Health Sciences at Rice University, a university dedicated to civic empowerment. Now, I use advanced programming and statistical modeling to identify target zip codes for Kid’s Meals, a nonprofit organization in Houston tackling food insecurity for children. My approach incorporates variables like SNAP enrollment, poverty levels, transportation access, overcrowding, and family structure, enabling Kid’s Meals to expand its reach by 30% over the next three years and provide meals to thousands of additional families. Beyond numbers, I integrate qualitative insights from 'Hope Providers'—delivery drivers who build relationships with families. Their stories have expanded my perspective, shaping my consideration of high school education and ESL programs as crucial factors in food insecurity. Each data point represents a real person—someone like Jane, whose struggles underscore systemic failures in food access. By bridging data science with human-centered solutions, I am committed to making sure that food reaches those who need it most. The complexity of food insecurity captivates both my analytical and humanitarian instincts. I believe math holds the power to drive social change, and through data-driven solutions, we can ensure food reaches those who need it the most. Looking ahead, I plan to pursue graduate studies in epidemiology with an emphasis on spatial analysis and the social determinants of health. The lessons I’ve learned from food insecurity work—how to integrate quantitative analysis with qualitative community insights to drive efficient, equity-focused solutions—are lessons I want to apply across other public health and social service challenges. Whether it's optimizing access to reproductive healthcare, addressing rural environmental health burdens, or improving resource allocation in social services, I want to use data-driven strategies to enhance the efficiency and impact of interventions. My experiences in food insecurity have reinforced that solutions must be both analytically rigorous and deeply human-centered—a philosophy I will carry forward in my research and advocacy.

How can we provide food to those who need it most in a data-driven way? I’ve grappled with this question for the past 6 years—from handing out burritos in my Oregon hometown to developing statistical models that target Houston’s highest-risk zip codes. In May 2020, I began volunteering with Burrito Brigade, a nonprofit organization in Eugene that distributes 500 burritos every weekend to the unhoused and food insecure. As I delivered burritos to my regulars, I learned about the deep-rooted health and economic challenges that kept them in a cycle of insecurity. Every Sunday at 3pm I delivered 5 burritos to Jane, an elderly woman, who could not chew the federally-provided food because of her dental problems and allergies, and she needed our burritos to survive. These conversations with our clients humanized the debilitating systemic social determinants of health to me; learning about how Jane had to jump through multiple hoops just to eat empowered me to fight for vulnerable individuals. As I continued volunteering, I realized something that was unexpected as I dropped off burritos to the Little Free Pantries around town–some pantries would empty instantly, while others remained untouched for hours. This discrepancy intrigued me—it revealed this concept of efficient resource distribution. To address this, I managed and optimized our 50+ Little Free Pantry network, researching ideal placements based on need and foot traffic. I also created a digital interactive map displaying pantry locations and real-time availability. Yet, I knew it could be better- it still lacked a precise way to rank areas by priority. Motivated by this idea, I pursued a double major in Statistics and Health Sciences at Rice University, a university dedicated to civic empowerment. Now, I use advanced programming and statistical modeling to identify target zip codes for Kid’s Meals, a nonprofit organization in Houston tackling food insecurity for children. My approach incorporates variables like SNAP enrollment, poverty levels, transportation access, overcrowding, and family structure, enabling Kid’s Meals to expand its reach by 30% over the next three years and provide meals to thousands of additional families. Beyond numbers, I integrate qualitative insights from 'Hope Providers'—delivery drivers who build relationships with families. Their stories have expanded my perspective, shaping my consideration of high school education and ESL programs as crucial factors in food insecurity. Each data point represents a real person—someone like Jane, whose struggles underscore systemic failures in food access. By bridging data science with human-centered solutions, I am committed to making sure that food reaches those who need it most. The complexity of food insecurity captivates both my analytical and humanitarian instincts. I believe math holds the power to drive social change, and through data-driven solutions, we can ensure food reaches those who need it the most. Looking ahead, I plan to pursue graduate studies in epidemiology with an emphasis on spatial analysis and the social determinants of health. The lessons I’ve learned from food insecurity work—how to integrate quantitative analysis with qualitative community insights to drive efficient, equity-focused solutions—are lessons I want to apply across other public health and social service challenges. Whether it's optimizing access to reproductive healthcare, addressing rural environmental health burdens, or improving resource allocation in social services, I want to use data-driven strategies to enhance the efficiency and impact of interventions. My experiences in food insecurity have reinforced that solutions must be both analytically rigorous and deeply human-centered—a philosophy I will carry forward in my research and advocacy.

One of the most transformative milestones in my journey began in high school, volunteering with the Burrito Brigade, a grassroots organization addressing food insecurity for the unhoused in my hometown. Every weekend, I prepared and distributed fresh burritos, engaging directly with individuals facing homelessness. What started as a simple act of service quickly became a deeply personal learning experience. I listened to their stories about the barriers they encountered—limited access to fresh produce, safe sleeping spaces, and an often inaccessible shelter system. These conversations revealed the deep inequities within systems meant to provide support and inspired me to think critically about systemic solutions. This early experience has profoundly influenced both my academic and leadership roles. At Rice University, I serve on the Executive Board of Rice Mutual Aid, where we have distributed over $35,000 in emergency funds to students facing financial hardships. In this role, I have used statistical analysis to monitor fund allocations, ensuring equity in our distributions and identifying gaps in resource delivery. Additionally, I spearheaded fundraising campaigns in collaboration with over ten student organizations, raising $7,600 for humanitarian causes. These efforts have shown me how data-driven strategies, combined with empathy and collaboration, can create meaningful impact in addressing systemic inequities. In parallel, my academic pursuits have allowed me to deepen this intersection of community engagement and data. As a Research Assistant at Rice, I analyze the health outcomes of agricultural workers, focusing on how work conditions and documentation status shape their well-being. My time with the Burrito Brigade continues to guide my approach, reminding me that each line of data represents an individual navigating systemic challenges. This perspective drives me to ensure my research remains rooted in addressing health disparities. For example, I’ve developed bias-adjusted latent class models to predict undocumented status and its correlation with health inequities, contributing to research expected to be published in 2025. Additionally, my leadership at Biokind Analytics has further cemented my belief in equity-driven solutions. Working with healthcare nonprofits, I’ve used data visualization and statistical modeling to diagnose inefficiencies and propose actionable recommendations. Last semester, I developed cost utilization recommendations for an intellectual disability nonprofit. I fully acknowledged my lack of awareness of intellectual disability hardships, so I actively engaged by asking for their personal stories and being openly curious about the unfamiliar educational limitations. These experiences have reinforced the importance of connecting grassroots insights with data-driven strategies to create systemic change. These milestones—from Burrito Brigade to Rice Mutual Aid, and from academic research to consulting—have solidified my life goal of a career in public service. I aim to use data not only to identify problems but also to advocate for systemic change and create equitable health interventions for vulnerable populations. By blending technical expertise with community-driven insights, I am committed to ensuring that my work is both impactful and ethical. These experiences have taught me that data alone is not enough; it must be paired with compassion, collaboration, and a commitment to equity to drive meaningful and sustainable change.

One of the most transformative milestones in my journey began in high school, volunteering with the Burrito Brigade, a grassroots organization addressing food insecurity for the unhoused in my hometown. Every weekend, I prepared and distributed fresh burritos, engaging directly with individuals facing homelessness. What started as a simple act of service quickly became a deeply personal learning experience. I listened to their stories about the barriers they encountered—limited access to fresh produce, safe sleeping spaces, and an often inaccessible shelter system. These conversations revealed the deep inequities within systems meant to provide support and inspired me to think critically about systemic solutions. This early experience has profoundly influenced both my academic and leadership roles. At Rice University, I serve on the Executive Board of Rice Mutual Aid, where we have distributed over $35,000 in emergency funds to students facing financial hardships. In this role, I have used statistical analysis to monitor fund allocations, ensuring equity in our distributions and identifying gaps in resource delivery. Additionally, I spearheaded fundraising campaigns in collaboration with over ten student organizations, raising $7,600 for humanitarian causes. These efforts have shown me how data-driven strategies, combined with empathy and collaboration, can create meaningful impact in addressing systemic inequities. In parallel, my academic pursuits have allowed me to deepen this intersection of community engagement and data. As a Research Assistant in Rice’s Sociology Department, I analyze the health outcomes of agricultural workers, focusing on how work conditions, sanitation access, and documentation status shape their well-being. My time with the Burrito Brigade continues to guide my approach, reminding me that each line of data represents an individual navigating systemic challenges. This perspective drives me to ensure my research remains actionable and rooted in addressing health disparities. For example, I’ve developed bias-adjusted latent class models to predict undocumented status and its correlation with health inequities, contributing to research expected to be published in 2025. Additionally, my leadership at Biokind Analytics has further cemented my belief in equity-driven solutions. Working with healthcare nonprofits, I’ve used data visualization and statistical modeling to diagnose inefficiencies and propose actionable recommendations. Last semester, I developed cost utilization recommendations for an intellectual disability nonprofit. I fully acknowledged my lack of awareness of intellectual disability hardships, so I actively engaged by asking for their personal stories and being openly curious about the unfamiliar educational limitations. These experiences have reinforced the importance of connecting grassroots insights with data-driven strategies to create systemic change. These milestones—from Burrito Brigade to Rice Mutual Aid, and from academic research to consulting—have solidified my life goal of a career in public service. I aim to use data not only to identify problems but also to advocate for systemic change and create equitable health interventions for vulnerable populations. By blending technical expertise with community-driven insights, I am committed to ensuring that my work is both impactful and ethical. These experiences have taught me that data alone is not enough; it must be paired with compassion, collaboration, and a commitment to equity to drive meaningful and sustainable change.

How can we provide food to those who need it most in a data-driven way? I’ve grappled with this question for the past 6 years—from handing out burritos in my Oregon hometown to developing statistical models that target Houston’s highest-risk zip codes. In May 2020, I began volunteering with Burrito Brigade, a nonprofit organization in Eugene that distributes 500 burritos every weekend to the unhoused and food insecure. As I delivered burritos to my regulars, I learned about the deep-rooted health and economic challenges that kept them in a cycle of insecurity. Every Sunday at 3pm I delivered 5 burritos to Jane, an elderly woman, who could not chew the federally-provided food because of her dental problems and allergies, and she needed our burritos to survive. These conversations with our clients humanized the debilitating systemic social determinants of health to me; learning about how Jane had to jump through multiple hoops just to eat empowered me to fight for vulnerable individuals. As I continued volunteering, I realized something that was unexpected as I dropped off burritos to the Little Free Pantries around town–some pantries would empty instantly, while others remained untouched for hours. This discrepancy intrigued me—it revealed this concept of efficient resource distribution. To address this, I managed and optimized our 50+ Little Free Pantry network, researching ideal placements based on need and foot traffic. I also created a digital interactive map displaying pantry locations and real-time availability. Yet, I knew it could be better- it still lacked a precise way to rank areas by priority. Motivated by this idea, I pursued a double major in Statistics and Health Sciences at Rice University, a university dedicated to civic empowerment. Now, I use advanced programming and statistical modeling to identify target zip codes for Kid’s Meals, a nonprofit organization in Houston tackling food insecurity for children. My approach incorporates variables like SNAP enrollment, poverty levels, transportation access, overcrowding, and family structure, enabling Kid’s Meals to expand its reach by 30% over the next three years and provide meals to thousands of additional families. Beyond numbers, I integrate qualitative insights from 'Hope Providers'—delivery drivers who build relationships with families. Their stories have expanded my perspective, shaping my consideration of high school education and ESL programs as crucial factors in food insecurity. Each data point represents a real person—someone like Jane, whose struggles underscore systemic failures in food access. By bridging data science with human-centered solutions, I am committed to making sure that food reaches those who need it most. The complexity of food insecurity captivates both my analytical and humanitarian instincts. I believe math holds the power to drive social change, and through data-driven solutions, we can ensure food reaches those who need it the most. Looking ahead, I plan to pursue graduate studies in epidemiology with an emphasis on spatial analysis and the social determinants of health. The lessons I’ve learned from food insecurity work—how to integrate quantitative analysis with qualitative community insights to drive efficient, equity-focused solutions—are lessons I want to apply across other public health and social service challenges. Whether it's optimizing access to reproductive healthcare, addressing rural environmental health burdens, or improving resource allocation in social services, I want to use data-driven strategies to enhance the efficiency and impact of interventions. My experiences in food insecurity have reinforced that solutions must be both analytically rigorous and deeply human-centered—a philosophy I will carry forward in my research and advocacy.

How can we provide food to those who need it most in a data-driven way? I’ve grappled with this question for the past 6 years—from handing out burritos in my Oregon hometown to developing statistical models that target Houston’s highest-risk zip codes. In May 2020, I began volunteering with Burrito Brigade, a nonprofit organization in Eugene that distributes 500 burritos every weekend to the unhoused and food insecure. As I delivered burritos to my regulars, I learned about the deep-rooted health and economic challenges that kept them in a cycle of insecurity. Every Sunday at 3pm I delivered 5 burritos to Jane, an elderly woman, who could not chew the federally-provided food because of her dental problems and allergies, and she needed our burritos to survive. These conversations with our clients humanized the debilitating systemic social determinants of health to me; learning about how Jane had to jump through multiple hoops just to eat empowered me to fight for vulnerable individuals. As I continued volunteering, I realized something that was unexpected as I dropped off burritos to the Little Free Pantries around town–some pantries would empty instantly, while others remained untouched for hours. This discrepancy intrigued me—it revealed this concept of efficient resource distribution. To address this, I managed and optimized our 50+ Little Free Pantry network, researching ideal placements based on need and foot traffic. I also created a digital interactive map displaying pantry locations and real-time availability. Yet, I knew it could be better- it still lacked a precise way to rank areas by priority. Motivated by this idea, I pursued a double major in Statistics and Health Sciences at Rice University, a university dedicated to civic empowerment. Now, I use advanced programming and statistical modeling to identify target zip codes for Kid’s Meals, a nonprofit organization in Houston tackling food insecurity for children. My approach incorporates variables like SNAP enrollment, poverty levels, transportation access, overcrowding, and family structure, enabling Kid’s Meals to expand its reach by 30% over the next three years and provide meals to thousands of additional families. Beyond numbers, I integrate qualitative insights from 'Hope Providers'—delivery drivers who build relationships with families. Their stories have expanded my perspective, shaping my consideration of high school education and ESL programs as crucial factors in food insecurity. Each data point represents a real person—someone like Jane, whose struggles underscore systemic failures in food access. By bridging data science with human-centered solutions, I am committed to making sure that food reaches those who need it most. The complexity of food insecurity captivates both my analytical and humanitarian instincts. I believe math holds the power to drive social change, and through data-driven solutions, we can ensure food reaches those who need it the most. Looking ahead, I plan to pursue graduate studies in epidemiology with an emphasis on spatial analysis and the social determinants of health. The lessons I’ve learned from food insecurity work—how to integrate quantitative analysis with qualitative community insights to drive efficient, equity-focused solutions—are lessons I want to apply across other public health and social service challenges. Whether it's optimizing access to reproductive healthcare, addressing rural environmental health burdens, or improving resource allocation in social services, I want to use data-driven strategies to enhance the efficiency and impact of interventions. My experiences in food insecurity have reinforced that solutions must be both analytically rigorous and deeply human-centered—a philosophy I will carry forward in my research and advocacy.

How can we provide food to those who need it most in a data-driven way? I’ve grappled with this question for the past 6 years—from handing out burritos in my Oregon hometown to developing statistical models that target Houston’s highest-risk zip codes. In May 2020, I began volunteering with Burrito Brigade, a nonprofit organization in Eugene that distributes 500 burritos every weekend to the unhoused and food insecure. As I delivered burritos to my regulars, I learned about the deep-rooted health and economic challenges that kept them in a cycle of insecurity. Every Sunday at 3pm I delivered 5 burritos to Jane, an elderly woman, who could not chew the federally-provided food because of her dental problems and allergies, and she needed our burritos to survive. These conversations with our clients humanized the debilitating systemic social determinants of health to me; learning about how Jane had to jump through multiple hoops just to eat empowered me to fight for vulnerable individuals. As I continued volunteering, I realized something that was unexpected as I dropped off burritos to the Little Free Pantries around town–some pantries would empty instantly, while others remained untouched for hours. This discrepancy intrigued me—it revealed this concept of efficient resource distribution. To address this, I managed and optimized our 50+ Little Free Pantry network, researching ideal placements based on need and foot traffic. I also created a digital interactive map displaying pantry locations and real-time availability. Yet, I knew it could be better- it still lacked a precise way to rank areas by priority. Motivated, I pursued a double major in Statistics and Health Sciences at Rice University, a university dedicated to civic empowerment. Now, I use advanced programming and statistical modeling to identify target zip codes for Kid’s Meals, a nonprofit organization in Houston tackling food insecurity for children. My approach incorporates variables like SNAP enrollment, poverty levels, transportation access, overcrowding, and family structure, enabling Kid’s Meals to expand its reach by 30% over the next three years and provide meals to thousands of additional families. Beyond numbers, I integrate qualitative insights from 'Hope Providers'—delivery drivers who build relationships with families. Their stories have expanded my perspective, shaping my consideration of high school education and ESL programs as crucial factors in food insecurity. Each data point represents a real person—someone like Jane, whose struggles underscore systemic failures in food access. By bridging data science with human-centered solutions, I am committed to making sure that food reaches those who need it most. The complexity of food insecurity captivates both my analytical and humanitarian instincts. I believe math holds the power to drive social change, and through data-driven solutions, we can ensure food reaches those who need it the most. Looking ahead, I plan to pursue graduate studies in epidemiology with an emphasis on spatial analysis and the social determinants of health. The lessons I’ve learned from food insecurity work—how to integrate quantitative analysis with qualitative community insights to drive efficient, equity-focused solutions—are lessons I want to apply across other public health and social service challenges. Whether it's optimizing access to reproductive healthcare, addressing rural environmental health burdens, or improving resource allocation in social services, I want to use data-driven strategies to enhance the efficiency and impact of interventions. My experiences in food insecurity have reinforced that solutions must be both analytically rigorous and deeply human-centered—a philosophy I will carry forward in my research and advocacy.

How can we provide food to those who need it most in a data-driven way? I’ve grappled with this question for the past 6 years—from handing out burritos in my Oregon hometown to developing statistical models that target Houston’s highest-risk zip codes. In May 2020, I began volunteering with Burrito Brigade, a nonprofit organization in Eugene that distributes 500 burritos every weekend to the unhoused and food insecure. As I delivered burritos to my regulars, I learned about the deep-rooted health and economic challenges that kept them in a cycle of insecurity. Every Sunday at 3pm I delivered 5 burritos to Jane, an elderly woman, who could not chew the federally-provided food because of her dental problems and allergies, and she needed our burritos to survive. These conversations with our clients humanized the debilitating systemic social determinants of health to me; learning about how Jane had to jump through multiple hoops just to eat empowered me to fight for vulnerable individuals. As I continued volunteering, I realized something that was unexpected as I dropped off burritos to the Little Free Pantries around town–some pantries would empty instantly, while others remained untouched for hours. This discrepancy intrigued me—it revealed this concept of efficient resource distribution. To address this, I managed and optimized our 50+ Little Free Pantry network, researching ideal placements based on need and foot traffic. I also created a digital interactive map displaying pantry locations and real-time availability. Yet, I knew it could be better- it still lacked a precise way to rank areas by priority. Motivated, I pursued a double major in Statistics and Health Sciences at Rice University, a university dedicated to civic empowerment. Now, I use advanced programming and statistical modeling to identify target zip codes for Kid’s Meals, a nonprofit organization in Houston tackling food insecurity for children. My approach incorporates variables like SNAP enrollment, poverty levels, transportation access, overcrowding, and family structure, enabling Kid’s Meals to expand its reach by 30% over the next three years and provide meals to thousands of additional families. Beyond numbers, I integrate qualitative insights from 'Hope Providers'—delivery drivers who build relationships with families. Their stories have expanded my perspective, shaping my consideration of high school education and ESL programs as crucial factors in food insecurity. Each data point represents a real person—someone like Jane, whose struggles underscore systemic failures in food access. By bridging data science with human-centered solutions, I am committed to making sure that food reaches those who need it most. The complexity of food insecurity captivates both my analytical and humanitarian instincts. I believe math holds the power to drive social change, and through data-driven solutions, we can ensure food reaches those who need it the most. Looking ahead, I plan to pursue graduate studies in epidemiology with an emphasis on spatial analysis and the social determinants of health. The lessons I’ve learned from food insecurity work—how to integrate quantitative analysis with qualitative community insights to drive efficient, equity-focused solutions—are lessons I want to apply across other public health and social service challenges. Whether it's optimizing access to reproductive healthcare, addressing rural environmental health burdens, or improving resource allocation in social services, I want to use data-driven strategies to enhance the efficiency and impact of interventions. My experiences in food insecurity have reinforced that solutions must be both analytically rigorous and deeply human-centered—a philosophy I will carry forward in my research and advocacy.

How can we provide food to those who need it most in a data-driven way? I’ve grappled with this question for the past 6 years—from handing out burritos in my Oregon hometown to developing statistical models that target Houston’s highest-risk zip codes. In May 2020, I began volunteering with Burrito Brigade, a nonprofit organization in Eugene, Oregon that distributes 500 burritos every weekend to the unhoused and food insecure. As I delivered burritos to my regulars, I learned about the deep-rooted health and economic challenges that kept them in a cycle of insecurity. Every Sunday at 3pm I delivered 5 burritos to Jane, an elderly woman, who could not chew the federally-provided food because of her dental problems and allergies, and she needed our burritos to survive. These conversations with our clients humanized the debilitating systemic social determinants of health to me; learning about how Jane had to jump through multiple hoops just to eat empowered me to fight for vulnerable individuals. As I continued volunteering, I realized something that was unexpected as I dropped off burritos to the Little Free Pantries around town–some pantries would empty instantly, while others remained untouched for hours. This discrepancy intrigued me—it revealed this concept of efficient resource distribution. To address this, I managed and optimized our 50+ Little Free Pantry network, researching ideal placements based on need and foot traffic. I also created a digital interactive map displaying pantry locations and real-time availability. Yet, I knew it could be better- it still lacked a precise way to rank areas by priority. Motivated, I pursued a double major in Statistics and Health Sciences at Rice University, a university dedicated to civic empowerment. Now, I use advanced programming and statistical modeling to identify target zip codes for Kid’s Meals, a nonprofit organization in Houston tackling childhood food insecurity. My approach incorporates variables like SNAP enrollment, poverty levels, transportation access, overcrowding, and family structure, enabling Kid’s Meals to expand its reach by 30% over the next three years and provide meals to thousands of additional families. Beyond numbers, I integrate qualitative insights from 'Hope Providers'—delivery drivers who build relationships with families. Their stories have expanded my perspective, shaping my consideration of high school education and ESL programs as crucial factors in food insecurity. Each data point represents a real person—someone like Jane, whose struggles underscore systemic failures in food access. By bridging data science with human-centered solutions, I am committed to making sure that food reaches those who need it most. The complexity of food insecurity captivates both my analytical and humanitarian instincts. I believe math holds the power to drive social change, and through data-driven solutions, we can ensure food reaches those who need it the most. Looking ahead, I plan to pursue graduate studies in epidemiology with an emphasis on spatial analysis and the social determinants of health. The lessons I’ve learned from food insecurity work—how to integrate quantitative analysis with qualitative community insights to drive efficient, equity-focused solutions—are lessons I want to apply across other public health and social service challenges. Whether it's optimizing access to reproductive healthcare, addressing rural environmental health burdens, or improving resource allocation in social services- these are some of my recent involvements- I want to use data-driven strategies to enhance the efficiency and impact of interventions. My experiences in food insecurity have reinforced that solutions must be both analytically rigorous and deeply human-centered—a philosophy I will carry forward in my research and advocacy.

How can we provide food to those who need it most in a data-driven way? I’ve grappled with this question for the past 6 years—from handing out burritos in my Oregon hometown to developing statistical models that target Houston’s highest-risk zip codes. In May 2020, I began volunteering with Burrito Brigade, a nonprofit organization in Eugene that distributes 500 burritos every weekend to the unhoused and food insecure. As I delivered burritos to my regulars, I learned about the deep-rooted health and economic challenges that kept them in a cycle of insecurity. Every Sunday at 3pm I delivered 5 burritos to Jane, an elderly woman, who could not chew the federally-provided food because of her dental problems and allergies, and she needed our burritos to survive. These conversations with our clients humanized the debilitating systemic social determinants of health to me; learning about how Jane had to jump through multiple hoops just to eat empowered me to fight for vulnerable individuals. As I continued volunteering, I realized something that was unexpected as I dropped off burritos to the Little Free Pantries around town–some pantries would empty instantly, while others remained untouched for hours. This discrepancy intrigued me—it revealed this concept of efficient resource distribution. To address this, I managed and optimized our 50+ Little Free Pantry network, researching ideal placements based on need and foot traffic. I also created a digital interactive map displaying pantry locations and real-time availability. Yet, I knew it could be better- it still lacked a precise way to rank areas by priority. Motivated by this idea, I pursued a double major in Statistics and Health Sciences at Rice University, a university dedicated to civic empowerment. Now, I use advanced programming and statistical modeling to identify target zip codes for Kid’s Meals, a nonprofit organization in Houston tackling childhood food insecurity. My approach incorporates variables like SNAP enrollment, poverty levels, transportation access, overcrowding, and family structure, enabling Kid’s Meals to expand its reach by 30% over the next three years and provide meals to thousands of additional families. Beyond numbers, I integrate qualitative insights from 'Hope Providers'—delivery drivers who build relationships with families. Their stories have expanded my perspective, shaping my consideration of high school education and ESL programs as crucial factors in food insecurity. Each data point represents a real person—someone like Jane, whose struggles underscore systemic failures in food access. By bridging data science with human-centered solutions, I am committed to making sure that food reaches those who need it most. The complexity of food insecurity captivates both my analytical and humanitarian instincts. I believe math holds the power to drive social change, and through data-driven solutions, we can ensure food reaches those who need it the most. Looking ahead, I plan to pursue graduate studies in epidemiology with an emphasis on spatial analysis and the social determinants of health. The lessons I’ve learned from food insecurity work—how to integrate quantitative analysis with qualitative community insights to drive efficient, equity-focused solutions—are lessons I want to apply across other public health and social service challenges. Whether it's optimizing access to reproductive healthcare, addressing rural environmental health burdens, or improving resource allocation in social services, I want to use data-driven strategies to enhance the efficiency and impact of interventions. My experiences in food insecurity have reinforced that solutions must be both analytically rigorous and deeply human-centered—a philosophy I will carry forward in my research and advocacy.

How can we provide food to those who need it most in a data-driven way? I’ve grappled with this question for the past 6 years—from handing out burritos in my Oregon hometown to developing statistical models that target Houston’s highest-risk zip codes. In May 2020, I began volunteering with Burrito Brigade, a nonprofit organization in Eugene that distributes 500 burritos every weekend to the unhoused and food insecure. As I delivered burritos to my regulars, I learned about the deep-rooted health and economic challenges that kept them in a cycle of insecurity. Every Sunday at 3pm I delivered 5 burritos to Jane, an elderly woman, who could not chew the federally-provided food because of her dental problems and allergies, and she needed our burritos to survive. These conversations with our clients humanized the debilitating systemic social determinants of health to me; learning about how Jane had to jump through multiple hoops just to eat empowered me to fight for vulnerable individuals. As I continued volunteering, I realized something that was unexpected as I dropped off burritos to the Little Free Pantries around town–some pantries would empty instantly, while others remained untouched for hours. This discrepancy intrigued me—it revealed this concept of efficient resource distribution. To address this, I managed and optimized our 50+ Little Free Pantry network, researching ideal placements based on need and foot traffic. I also created a digital interactive map displaying pantry locations and real-time availability. Yet, I knew it could be better- it still lacked a precise way to rank areas by priority. Motivated by this idea, I pursued a double major in Statistics and Health Sciences at Rice University, a university dedicated to civic empowerment. Now, I use advanced programming and statistical modeling to identify target zip codes for Kid’s Meals, a nonprofit organization in Houston tackling childhood food insecurity. My approach incorporates variables like SNAP enrollment, poverty levels, transportation access, overcrowding, and family structure, enabling Kid’s Meals to expand its reach by 30% over the next three years and provide meals to thousands of additional families. Beyond numbers, I integrate qualitative insights from 'Hope Providers'—delivery drivers who build relationships with families. Their stories have expanded my perspective, shaping my consideration of high school education and ESL programs as crucial factors in food insecurity. Each data point represents a real person—someone like Jane, whose struggles underscore systemic failures in food access. By bridging data science with human-centered solutions, I am committed to making sure that food reaches those who need it most. The complexity of food insecurity captivates both my analytical and humanitarian instincts. I believe math holds the power to drive social change, and through data-driven solutions, we can ensure food reaches those who need it the most. Looking ahead, I plan to pursue graduate studies in epidemiology with an emphasis on spatial analysis and the social determinants of health. The lessons I’ve learned from food insecurity work—how to integrate quantitative analysis with qualitative community insights to drive efficient, equity-focused solutions—are lessons I want to apply across other public health and social service challenges. Whether it's optimizing access to reproductive healthcare, addressing rural environmental health burdens, or improving resource allocation in social services, I want to use data-driven strategies to enhance the efficiency and impact of interventions. My experiences in food insecurity have reinforced that solutions must be both analytically rigorous and deeply human-centered—a philosophy I will carry forward in my research and advocacy.

How can we provide food to those who need it most in a data-driven way? I’ve grappled with this question for the past 6 years—from handing out burritos in my Oregon hometown to developing statistical models that target Houston’s highest-risk zip codes. In May 2020, I began volunteering with Burrito Brigade, a nonprofit organization in Eugene that distributes 500 burritos every weekend to the unhoused and food insecure. As I delivered burritos to my regulars, I learned about the deep-rooted health and economic challenges that kept them in a cycle of insecurity. Every Sunday at 3pm I delivered 5 burritos to Jane, an elderly woman, who could not chew the federally-provided food because of her dental problems and allergies, and she needed our burritos to survive. These conversations with our clients humanized the debilitating systemic social determinants of health to me; learning about how Jane had to jump through multiple hoops just to eat empowered me to fight for vulnerable individuals. As I continued volunteering, I realized something that was unexpected as I dropped off burritos to the Little Free Pantries around town–some pantries would empty instantly, while others remained untouched for hours. This discrepancy intrigued me—it revealed this concept of efficient resource distribution. To address this, I managed and optimized our 50+ Little Free Pantry network, researching ideal placements based on need and foot traffic. I also created a digital interactive map displaying pantry locations and real-time availability. Yet, I knew it could be better- it still lacked a precise way to rank areas by priority. Motivated by this idea, I pursued a double major in Statistics and Health Sciences at Rice University, a university dedicated to civic empowerment. Now, I use advanced programming and statistical modeling to identify target zip codes for Kid’s Meals, a nonprofit organization in Houston tackling food insecurity for children. My approach incorporates variables like SNAP enrollment, poverty levels, transportation access, overcrowding, and family structure, enabling Kid’s Meals to expand its reach by 30% over the next three years and provide meals to thousands of additional families. Beyond numbers, I integrate qualitative insights from 'Hope Providers'—delivery drivers who build relationships with families. Their stories have expanded my perspective, shaping my consideration of high school education and ESL programs as crucial factors in food insecurity. Each data point represents a real person—someone like Jane, whose struggles underscore systemic failures in food access. By bridging data science with human-centered solutions, I am committed to making sure that food reaches those who need it most. The complexity of food insecurity captivates both my analytical and humanitarian instincts. I believe math holds the power to drive social change, and through data-driven solutions, we can ensure food reaches those who need it the most. Looking ahead, I plan to pursue graduate studies in epidemiology with an emphasis on spatial analysis and the social determinants of health. The lessons I’ve learned from food insecurity work—how to integrate quantitative analysis with qualitative community insights to drive efficient, equity-focused solutions—are lessons I want to apply across other public health and social service challenges. Whether it's optimizing access to reproductive healthcare, addressing rural environmental health burdens, or improving resource allocation in social services, I want to use data-driven strategies to enhance the efficiency and impact of interventions. My experiences in food insecurity have reinforced that solutions must be both analytically rigorous and deeply human-centered—a philosophy I will carry forward in my research and advocacy.

One of the most transformative milestones in my journey began in high school, volunteering with the Burrito Brigade, a grassroots organization addressing food insecurity for the unhoused in my hometown. Every weekend, I prepared and distributed fresh burritos, engaging directly with individuals facing homelessness. What started as a simple act of service quickly became a deeply personal learning experience. I listened to their stories about the barriers they encountered—limited access to fresh produce, safe sleeping spaces, and an often inaccessible shelter system. These conversations revealed the deep inequities within systems meant to provide support and inspired me to think critically about systemic solutions. This early experience has profoundly influenced both my academic and leadership roles. At Rice University, I serve on the Executive Board of Rice Mutual Aid, where we have distributed over $35,000 in emergency funds to students facing financial hardships. In this role, I have used statistical analysis to monitor fund allocations, ensuring equity in our distributions and identifying gaps in resource delivery. Additionally, I spearheaded fundraising campaigns in collaboration with over ten student organizations, raising $7,600 for humanitarian causes. These efforts have shown me how data-driven strategies, combined with empathy and collaboration, can create meaningful impact in addressing systemic inequities. In parallel, my academic pursuits have allowed me to deepen this intersection of community engagement and data. As a Research Assistant in Rice’s Sociology Department, I analyze the health outcomes of agricultural workers, focusing on how work conditions, sanitation access, and documentation status shape their well-being. My time with the Burrito Brigade continues to guide my approach, reminding me that each line of data represents an individual navigating systemic challenges. This perspective drives me to ensure my research remains actionable and rooted in addressing health disparities. For example, I’ve developed bias-adjusted latent class models to predict undocumented status and its correlation with health inequities, contributing to research expected to be published in 2025. Additionally, my leadership at Biokind Analytics has further cemented my belief in equity-driven solutions. Working with healthcare nonprofits, I’ve used data visualization and statistical modeling to diagnose inefficiencies and propose actionable recommendations. Last semester, I developed cost utilization recommendations for an intellectual disability nonprofit. I fully acknowledged my lack of awareness of intellectual disability hardships, so I actively engaged by asking for their personal stories and being openly curious about the unfamiliar educational limitations. These experiences have reinforced the importance of connecting grassroots insights with data-driven strategies to create systemic change. These milestones—from Burrito Brigade to Rice Mutual Aid, and from academic research to consulting—have solidified my life goal of a career in public service. I aim to use data not only to identify problems but also to advocate for systemic change and create equitable health interventions for vulnerable populations. By blending technical expertise with community-driven insights, I am committed to ensuring that my work is both impactful and ethical. These experiences have taught me that data alone is not enough; it must be paired with compassion, collaboration, and a commitment to equity to drive meaningful and sustainable change.

My life goal is to have a career in statistical analysis researching population health issues specifically for vulnerable communities. This semester, as I parsed through survey data of unhoused veterans at the UT School of Public Health Tsai Lab, I was surprised to realize how I can connect each row of data to my previous experience volunteering with the unhoused in my hometown. I see the nuances in the dataset and the core issues with the survey questions because I remember the people I made burritos for and the conversations I had with them. In addition, my fellow volunteer’s words about their frustrations with the current shelter system in Eugene and the lack of access to fresh produce and safe sleeping sites stay with me when I brainstorm the best phrasing of questions to survey. I believe that my grassroots volunteering experiences allow me to personalize the data to improve accuracy and create more useful conclusions, as well as leading to more compelling analysis. Researching biostatistics and epidemiology with Columbia this summer has only strenghtened my excitement. Furthermore, as a woman in statistics, I’m passionate about including a multitude of perspectives from the initial questions we ask ourselves to the final conclusions of the project. Interpreting data can have many different storylines based on who conducts the research. At Biokind Analytics, a Rice data science consulting club, I’ve suggested that a nonprofit organization should prioritize providing information equally across racial groups in Houston, since many of their clientele is skewed towards certain ethnicities. I learned this equity-focused mindset from volunteering with a latino-focused food insecurity nonprofit. In addition, in group-settings, I’m quick to prioritize equal contribution and collaboration based on my experience as a minority. I’ll make sure to listen closely to all contributed voices, and actively ask for input from group members that are quiet. I’m quick to contribute my own voice in conversations as it brings value in facilitating conversations and providing unique insight. I’m excited to bring my active-listening skills I’ve cultivated through my extensive volunteering to the greater field of biostatistics, which needs community-driven approaches to create sustainable data-driven solutions. Finally, I’m committed to exploring how data has the power to solve health problems in creative methods through researching data science at a high level. Following my initiation into research with the UT Public Health Tsai Lab and researching agricultural workers health with Rice Professor Jing Li, I have solidified my commitment to pursuing biostatistics research in graduate school as a pivotal step in my career trajectory. I can’t wait to be inspired by the high level statistical research around me and learn more about the power of the groundbreaking biostatisticians. At Rice, I hope to learn more about how statistical analysis can be applied to more specific public health crises in Houston. A large diverse city with an amplitude of data sets is exciting because of the potential to uncover solutions to health crises that can eventually help inform public policy nationwide. I’d love to gain an appreciation of the importance of statistical methods in biomedical research that emphasizes equity and inclusion. I eagerly anticipate delving into the study of health disparities through a statistical lens, guided by the expertise of a distinguished faculty at Rice through Community Bridge program where I will partner with non-profit organization in the heart of Houston starting this fall.

My life goal is to have a career in statistical analysis researching population health issues specifically for vulnerable communities. This semester, as I parsed through survey data of unhoused veterans at the UT School of Public Health Tsai Lab, I was surprised to realize how I can connect each row of data to my previous experience volunteering with the unhoused in my hometown. I see the nuances in the dataset and the core issues with the survey questions because I remember the people I made burritos for and the conversations I had with them. In addition, my fellow volunteer’s words about their frustrations with the current shelter system in Eugene and the lack of access to fresh produce and safe sleeping sites stay with me when I brainstorm the best phrasing of questions to survey. I believe that my grassroots volunteering experiences allow me to personalize the data to improve accuracy and create more useful conclusions, as well as leading to more compelling analysis. Furthermore, as a woman in statistics, I’m passionate about including a multitude of perspectives from the initial questions we ask ourselves to the final conclusions of the project. Interpreting data can have many different storylines based on who conducts the research. At Biokind Analytics, a Rice data science consulting club, I’ve suggested that a nonprofit organization should prioritize providing information equally across racial groups in Houston, since many of their clientele is skewed towards certain ethnicities. I learned this equity-focused mindset from volunteering with a latino-focused food insecurity nonprofit. In addition, in group-settings, I’m quick to prioritize equal contribution and collaboration based on my experience as a minority. I’ll make sure to listen closely to all contributed voices, and actively ask for input from group members that are quiet. I’m quick to contribute my own voice in conversations as it brings value in facilitating conversations and providing unique insight. I’m excited to bring my active-listening skills I’ve cultivated through my extensive volunteering to the greater field of biostatistics, which needs community-driven approaches to create sustainable data-driven solutions. This year, I hope to learn more about how statistical analysis can be applied to more specific public health crises in Houston. A large diverse city with an amplitude of data sets is exciting because of the potential to uncover solutions to health crises that can eventually help inform public policy nationwide. I eagerly anticipate delving into the study of health disparities through a statistical lens, guided by the expertise of a distinguished faculty member at Rice.