For me, the American dream is the belief that where you start does not have to determine where you end up. It is the idea that through education, hard work, and perseverance, people can create opportunities for themselves and build a better future than the one they were born into. My understanding of the American dream is shaped by my personal experiences growing up between two cultures and working toward my education in the United States. Being born in Mexico and later building my life in Texas showed me how powerful opportunity can be when people are given access to education and the freedom to pursue their goals. At the same time, I have learned that the American dream is not something that simply happens. It requires determination, sacrifice, and the willingness to keep moving forward even when circumstances are difficult. For many students like me, the American dream is closely tied to education. Education opens doors that might otherwise remain closed. As a computer science student, every class I take and every skill I learn represents another step toward building a stable and meaningful future. Balancing work, school, and personal challenges has not always been easy, but those experiences have strengthened my belief that persistence and dedication can transform obstacles into opportunities. Living with Type 1 diabetes has also shaped my perspective on the American dream. Managing a chronic condition while pursuing my goals has required resilience and discipline. It has taught me that success is not just about achievement, but about learning to adapt and continue moving forward despite challenges. Rather than discouraging me, these experiences have motivated me to pursue a career in technology where I can contribute to innovations that improve people’s lives. To me, the American dream is not simply about financial success. It is about having the freedom to pursue knowledge, develop your talents, and use them to make a positive impact on others. My dream is to use my education in computer science to contribute to the development of technology that improves healthcare, particularly for people living with chronic illnesses like diabetes. Ultimately, the American dream means possibility. It means believing that through effort, education, and resilience, individuals can create a future that is better not only for themselves but also for their communities. My goal is to take the opportunities I have been given, continue growing as a student and innovator, and one day contribute to a world where technology helps people live healthier and more empowered lives.

I grew up between different worlds. I was born in northern Mexico and later built my life in Texas, which meant learning to adapt early. Financial stability was never something I could take for granted. I have always had to think carefully about school, work, and how to support myself while continuing my education. I currently pay for school out of pocket while balancing work and classes, and the cost of tuition, transportation, and living expenses has made every step of my academic journey something I’ve had to fight for. One of the biggest personal challenges I’ve faced is living with Type 1 diabetes. Managing a chronic condition while being a full-time student requires constant discipline. Every day involves monitoring my blood sugar, planning meals, and making adjustments that most people never have to think about. There have been moments when balancing my health, work, and school felt overwhelming, but it also shaped the way I approach problems. It taught me resilience and forced me to become extremely organized and determined. My experience with diabetes is actually what sparked my interest in technology and STEM. I’ve seen firsthand how important medical technology is for people living with chronic illnesses. Devices like continuous glucose monitors and insulin pumps have improved lives, but I also see how much room there still is for innovation. As a computer science student, I’m especially interested in artificial intelligence and how it can be applied to healthcare. In the future, I want to help build smarter systems that can predict blood sugar patterns and make diabetes management easier and safer. Imagine technology that could analyze real-time data and warn someone before a dangerous blood sugar drop happens, or systems that help doctors personalize treatment using AI. I want to work on technology that doesn’t just exist in theory but directly improves people’s daily lives. My journey into STEM has not been easy financially or personally, but those challenges are exactly what motivate me. I know what it feels like to rely on technology to stay healthy, and that perspective drives me to keep learning and building. My goal is to contribute to the future of healthcare technology, particularly tools that support people with chronic illnesses like diabetes. If I can create even one piece of technology that makes someone’s life safer or easier, then every challenge I’ve faced along the way will have been worth it. Pursuing a degree in computer science has required persistence and a lot of self-belief. STEM can feel intimidating, especially as a woman and as someone who did not grow up with unlimited resources or connections in the field. There have been moments when classes felt overwhelming or when I questioned whether I truly belonged in such a technical space. Learning programming languages, working through complex math, and balancing work with demanding coursework has pushed me in ways I never expected. However, every challenge I’ve faced has strengthened my confidence and reminded me why I chose this path in the first place. As I progressed through my studies, I began to realize that computer science is not just about writing code. It is about solving real-world problems through creativity, logic, and innovation. I have especially grown interested in the intersection between artificial intelligence and healthcare. Technology already plays a huge role in managing chronic illnesses, but I believe the next generation of tools will be even more intelligent and personalized. AI has the potential to analyze massive amounts of health data and turn it into actionable insights that can improve patient outcomes. For people with Type 1 diabetes, this kind of innovation could be life-changing. Managing the condition requires constant monitoring, decision-making, and adjustments throughout the day. Even with current devices, people still face unpredictable blood sugar fluctuations and the risk of dangerous highs or lows. I want to contribute to a future where technology can anticipate these changes before they happen. By combining machine learning with medical data, systems could potentially detect patterns in glucose levels, activity, stress, and nutrition to provide more accurate predictions and alerts. Beyond the technical side, I also care deeply about accessibility. Many advanced medical technologies are expensive and not equally available to everyone who needs them. Growing up with financial limitations has made me aware of how difficult it can be to access certain healthcare tools or treatments. Because of this, I hope to work on solutions that are not only innovative but also scalable and accessible to diverse communities. My long-term goal is to work in a field where technology directly improves human health and quality of life. Whether through AI-driven health monitoring systems, smarter medical devices, or new predictive algorithms, I want my work to contribute to safer and more effective healthcare systems. The challenges I have faced financially and personally have shaped my determination to succeed. They have also given me a clear sense of purpose. I am not simply studying computer science to build software. I am studying it so that I can help build a future where technology empowers people to live healthier and more secure lives.

My passion for cybersecurity grew from two things: curiosity and responsibility. I have always been drawn to technology, especially artificial intelligence and how systems make decisions. As I learned more about AI, I started wondering who protects these systems and who makes sure they are not manipulated, biased, or weaponized. Who defends the people most vulnerable to digital harm? That is where cybersecurity stopped being just a technical field to me and became a mission. As a Hispanic woman entering computer science, I am deeply aware that technology does not impact everyone equally. Communities like mine are often underrepresented in tech, yet we are frequently the most affected by data misuse, identity theft, misinformation, and algorithmic bias. Artificial intelligence is growing at an unprecedented pace. It powers healthcare systems, financial approvals, surveillance tools, and social media algorithms. Without strong cybersecurity foundations, AI can become dangerous. Models can be poisoned. Systems can be breached. Personal data can be exploited. The more I studied AI, the more I realized that cybersecurity is its backbone. If AI is the brain of modern technology, cybersecurity is the immune system. My initial interest was sparked when I began learning programming and understanding how vulnerable systems can be if not properly designed. Writing my first C++ programs made me realize how small errors can lead to major failures. From there, I started exploring topics like data protection, adversarial attacks on AI models, and the risks of automated decision-making systems. I became especially interested in how malicious actors can manipulate AI through data poisoning or prompt injection attacks. One of the biggest challenges I have faced is navigating a field where women, especially Hispanic women, are still underrepresented. There have been moments where I questioned whether I belonged in highly technical spaces. Cybersecurity can also feel overwhelming because the threats constantly evolve. The landscape never stands still. It requires continuous learning, resilience, and confidence. I have worked to overcome these challenges by strengthening my technical foundation. I focus on mastering programming, mathematics, and systems thinking so that I am not just using tools but understanding how they work at a deeper level. I actively seek out opportunities to build projects, ask questions, and engage with difficult material instead of avoiding it. Each obstacle has made me more persistent and more intentional about my growth. These experiences have shaped my approach to cybersecurity in a significant way. I do not see it only as protecting servers or preventing breaches. I see it as protecting people. Especially as AI becomes more embedded in healthcare, finance, and national infrastructure, cybersecurity must evolve alongside it. We need professionals who understand both artificial intelligence and security principles. I want to be someone who builds secure AI systems from the start, not someone who patches vulnerabilities after damage is done. In the future, I hope to specialize in AI security and adversarial machine learning. My goal is to help design systems that are resilient, ethically aligned, and resistant to manipulation. As technology becomes more powerful, the responsibility to secure it becomes greater. Being a Hispanic woman in cybersecurity is not just part of my identity. It is part of my motivation. Representation matters, especially in fields that shape the digital future. I want to contribute to building safer systems while also showing other young women from communities like mine that they belong in this space.

The healthcare problem I want to solve is dangerous hypoglycemia in people with Type 1 Diabetes before it happens.Current models trigger alerts when someone is already low. But lows rarely come from one single event. They usually build up from patterns such as insulin stacking, late-night corrections, missed meals, exercise without adjustment, stress, poor sleep, or gaps in CGM data. As a Type 1 diabetic, I can relate to this issue. Type 1 Diabetes is not just a glucose prediction problem. It is a systems problem. Risk builds through interconnected behaviors and physiological feedback. Because of that, I want to model T1D as a network and use iterative risk propagation to detect when someone is drifting toward instability. Instead of assigning each patient a static risk score, I model patients and risk factors as a graph. Let B be a patient–factor matrix: -B_ij = strength of risk factor j for patient i Risk factors could include: -frequency of overnight corrections -recent time below range -insulin variability -missed boluses -CGM data gaps -recent exercise without basal adjustment From this matrix, I construct a patient similarity matrix: A = B B^T This connects patients who share reinforcing patterns of risk. To stabilize the system, I normalize A row-wise: P_ij = A_ij / sum_k A_ik Then I introduce damping to ensure convergence: M = alpha P + (1 - alpha) 1 v^T where alpha is typically around 0.85 and v is a personalization vector (for fairness control or prioritization). Then I iteratively propagate risk: r^(t+1) = r^(t) M At convergence: r* = r* M The final vector r* is the dominant left eigenvector of M. This represents the Healthcare Risk Equilibrium. It captures not just individual risk, but how risk reinforces across patterns in the system. Why This Matters for T1D? In T1D, small behaviors amplify each other. For example: -Repeated late corrections increase insulin stacking. -Insulin stacking increases overnight lows. -Overnight lows increase variability the next day. -Variability increases stress and correction frequency. A simple predictive model might catch one step of that chain. But iterative risk propagation captures the reinforcing loop. The eigenvector score identifies patients whose patterns are structurally unstable, even if their most recent glucose reading looks fine. On top of the graph model, I would use a predictive layer such as gradient boosting or a lightweight sequence model to estimate: -probability of hypoglycemia in the next 6–12 hours -expected time below range -likelihood of insulin stacking event These predicted probabilities would update the weights in matrix B in real time. The graph then propagates those risks until equilibrium is reached. This creates a dynamic, system-aware risk score rather than a one-step prediction. Because risk is derived from matrix structure, we can compute contribution scores: influence_j ≈ sum_i r_i * B_ij This allows the system to explain: “Your current risk is primarily driven by: -frequent late-night corrections -insulin variability -recent CGM signal gaps” This is critical in diabetes care. Patients need actionable explanations, not black-box numbers. Standard models: -treat patients independently -output a probability -do not model reinforcement loops This equilibrium approach: -captures interconnected behavior patterns -stabilizes noisy signals -identifies structural instability -supports explainable risk decomposition -allows fairness adjustments via personalization vector My goal is to build a proactive T1D risk engine that identifies instability before hypoglycemia occurs. By combining AI prediction with iterative risk propagation and eigenvector-based equilibrium modeling, we move from reactive alerts to systems-level stabilization. This approach aligns with value-based care goals, reduces emergency visits, and addresses the real-world complexity of living with Type 1 Diabetes. It transforms risk prediction into risk management.