"I don't belong here" and "I'm not good enough" were thoughts that frequented my mind during high school as I became overwhelmed by a severe case of imposter syndrome. I never realized how wrong I was until I watched the movie Into the Spiderverse - not the place I had expected to find relief. I watched the movie during the peak of my imposter syndrome. I wanted to take the highest math course offered at my school: Mathematical Statistics and Data Sciences, a course commonly taken by the smartest students. l fell into a cycle of insecurity about not feeling smart enough to take the course, then embarrassment for feeling inferior. Whenever anyone asked me about my schedule, I avoided mentioning that class, afraid they would think I wasn't smart enough to take the course. Whenever I struggled with my math and science classes, I would beat myself down by thinking "I'll never understand this because I'm not smart enough." Imposter syndrome placed a mental barrier on me. I was continuously scared that not understanding a topic or getting a question wrong, would prove to me and my classmates that I was not smart enough to study STEM. In Into the Spiderverse, Miles' self-doubt about being Spiderman reminded me of my own. Then, Miles' father inspires him to embrace his Spiderman identity by taking a literal leap of faith off of a building. I decided to take my own leap - though it was physically less dramatic. Before I thought the only way I could get rid of my imposter syndrome was to just be better. People who are accomplished and successful don't get imposter syndrome (or so I thought). The movie inspired me to seek out both peers and professional women to confide in, and I was surprised and sad to hear that they had experienced the same self-doubt. But I was also comforted, because I realized that even skilled and accomplished women face the same insecurities, which stem from societal stigma, not actual ability. I took the course. Similar to how Miles found confidence from his dad's words, I found my confidence in a community of women in STEM. Since then, I have earned As in my math class - and am proud to state that I no longer have full-fledged imposter syndrome. Although I will sometimes doubt myself, I now know how to deal with those emotions by connecting with others and thinking back on my accomplishments and hard work. Overcoming Imposter syndrome has also reformed the way I learn. Before I was studying out of fear and to seek validation that I am smart. Now, I study with confidence to know how to solve the problems that come my way. Learning has become much more enjoyable for me. Instead of shying away from subjects because I don't think I'll succeed in them, I have started exploring any subject that I am truly interested in, such as Machine Learning and Artificial Intelligence.

I’ve always embraced the carefully curated rules of mathematics. I love how no matter the topic, the process of doing math problems never seems to change. Compared to the more subjective assumptions about characters and themes that I always seem to make in English class, the absolute rules of math comfort me. That’s why I was so excited to discover data science, the topic I want to study in college. I thought that since numbers were so concrete, combining them with data would create objective, impregnable results. Data science gave me hope for efficiently solving problems in an unjust world. I soon found, however, through reading cases studies, that numbers can be just as sticky as literary analysis. I thought that data science could easily make the world a better place, only to realize that it was infected with all the same biases and inefficiency it was trying to fix. One of the case studies was about a professor, Marvin Wolfgang, whose research counted any contact the police had with Black and Latino youth as crime, including traffic violations and stop-and-frisk. His analysis led policymakers to shift federal investment toward juvenile court systems and detention facilities, causing kids to be unfairly criminalized in the public eye, leading to the perpetuation of racial bias and police mistreatment. It was like reading a novel in English class, where a subtle action foreshadows a domino effect of destruction. I felt completely disillusioned. This wasn’t a short story — it was real life. Data scientists failed black and Latino youth. Initially concrete compared to the novels in English class, data science was seeming closer to fiction: human and imperfect. My mind raced. Does this mean all the policies built upon numbers and data are flawed? After endless reading, I arrived at the conclusion that both numbers and human elements are essential to ethical data science, a practice which requires the ability to apply and frame data without perpetuating harmful stereotypes. Instead of shying away from grey areas of implicit bias and inconclusive results, I will embrace the complexities of human life while doing data analytics. The very things that make numbers fragile and data science imperfect also make the world beautiful, so I aim to embrace them as a part of my work. While mistakes are inevitable, I hope that I can render them more predictable and manageable by being observant, open-minded, and questioning the evidence — qualities that will lead me to become not just a better data scientist, but also a better person. Already, thinking holistically and learning to confront ambiguity has led me to confidently explore new subjects, such as politics. Thinking holistically is an important quality for any career, which is why I believe my STEM studies will prepare me for success. In order to be an ethical data scientist, I need to pull from ideas of literature, history, and philosophy to frame my data in the best way possible. In any career, it is necessary to view your work from different perspectives and not be narrow minded.

When I was younger, I embraced the model minority myth. I took pride in the fact that Asians were viewed as intelligent, hardworking, well-mannered and wanted to uphold this stereotype. As the only Chinese-American in my middle school, however, this pride came with pressure. I felt pressured to act as the perfect Asian representative that my classmates expected or else I would fail the Asian community. It was lonely, sad, and exhausting. As I grew, my belief in the model minority myth started to waver. Going to a diverse high school with a significant Asian community allowed me to connect with my culture. It was exhilarating to connect with people who knew what it was like to be the only person of their race and talk about our culture without fear of judgment or expectation. We would reminisce about our similar upbringings attending Chinese school, learning math from our parents, and having to do extra, non-issued homework from them after the math lessons. We laughed about the overbearing way our immigrant parents piled food onto our friends’ plates and grilled them with questions. We didn’t have to explain the marks we got from cupping therapy, a form of Chinese alternative medicine that leaves temporary circular marks on the skin. I felt more comfortable being myself, and the pressure to uphold the minority model myth lessened. As that pressure and my facade of being a perfect Asian-American disappeared, so did my strong ties to the model minority myth. The coronavirus pandemic was the first time I experienced overt racism. When my figure skating student I was teaching from Essex Special Skaters said “China virus” while staring at me and another Chinese-American girl then asked if we were sisters, I felt embarrassed and angry. I was upset that people viewed me and other Asian-Americans first as a stereotype then as individual human beings. However, I realized that my frustration with how my figure skating student viewed me was how I viewed myself when I was younger. We both viewed ourselves through the lenses as stereotypes: mine through the model minority myth; his through the stereotypes that Asians carried coronavirus and all look the same. This realization was hard for me to process because it made me think about the detrimental effects of the model minority myth, which I held in high accord for so long. The model minority myth was no different than the racist stereotypes my figure skating student said. They both generalized Asian-Americans, a diverse group of people, and didn’t acknowledge their individual feelings and passions. During this time, I also learned about the true intentions and history of the model minority myth. It was created so white America could ignore the effects of racism by comparing minorities to Asian-Americans, who they claimed became successful based on their hardworking and family values. However, knowing this information was not enough to solve the confusion in my heart. I still believed in the model minority myth, which leads people to believe that the problems Asians face are not important because of their supposed success and good stereotypes. When I wondered why no one was discussing the racism and oppression that Asian-Americans endured, I told myself it was because Asians are the highest-earning group in America, so we had no right to complain about our problems. I picked up "Interior Chinatown" by Charles Yu, a satirical screenplay book about the Asian-American experience, to seek answers to my confusion. Yu said that Asian-Americans do not feel justified in claiming solidarity with other historically and culturally oppressed groups because it does not include the original American sin of slavery. Even though Asian-Americans suffered from the Chinese Exclusion Act of 1882, lynchings, discriminatory housing policies, and internment, Asian-Americans still feel like their oppression is second class. I was surprised by how the book clearly articulated how I was feeling and relieved that it validated my feelings that Asians do suffer oppression, and it should be talked about. I now understand that oppression can not be stratified into different levels. One person’s oppression does not make another’s invalid. There is no need to compare people’s oppressions to acknowledge your own. Armed with this information, I have come to see the model minority myth in its true colors. Now, my pride in being an Asian-American does not come from the model minority myth but from the diversity of Asian-Americans and the beauty of my Chinese culture. I understand that the model minority myth harms the Asian-American community by generalizing a huge, diverse group of people and am advocating to dismantle these and BIPOC communities’ stereotypes. I have joined my county’s Dear Asian Youth chapter, where were talk about and advocate for Asian and BIPOC issues. In college, I plan to join the Asian Student Union to bring awareness to the diversity of Asian-Americans and their issues.

The day I realized numbers and data aren’t definite changed my life. I’ve always been drawn to the stability behind numbers and embraced the carefully curated rules of mathematics. I love how no matter the topic, the process of doing math problems never seems to change. Compared to the more subjective assumptions about characters and themes that I always seem to make in English class, the absolute rules of math comfort me. That’s why I was so excited to discover data science. I thought that since numbers were so concrete, combining them with data would create objective, impregnable results. Data science gave me hope for efficiently solving problems in an unjust world. I soon found, however, through reading cases studies, that numbers can be just as sticky as literary analysis. I thought that data science could easily make the world a better place, only to realize that it was infected with all the same biases and inefficiency it was trying to fix. One of the case studies was about a professor, Marvin Wolfgang, whose research counted any contact the police had with Black and Latino youth as crime, including traffic violations and stop-and-frisk. His analysis led policymakers to shift federal investment toward juvenile court systems and detention facilities, causing kids to be unfairly criminalized in the public eye, leading to the perpetuation of racial bias and police mistreatment. It was like reading a novel in English class, where a subtle action foreshadows a domino effect of destruction. I felt completely disillusioned. This wasn’t a short story — it was real life. Data scientists failed black and Latino youth. Initially concrete compared to the novels in English class, data science was seeming closer to fiction: human and imperfect. My mind raced. How do we measure productivity without numbers? Does this mean all the policies built upon numbers and data are flawed? After endless reading, I arrived at the conclusion that both numbers and human elements are essential to ethical data science, a practice which requires not just mathematical savvy but also the ability to apply and frame data without perpetuating harmful stereotypes. I learned to appreciate, rather than resent, the fact that our world is not just made up of numbers, but also of complex histories, beliefs, emotions, music, and philosophies which impact our past, present, and future. While many have good intentions when it comes to interpreting numbers, that isn’t enough. We need historical context, perspective, and vigilance to create a positive impact. Instead of shying away from grey areas of implicit bias and inconclusive results, I will embrace the complexities of human life while doing data analytics. The very things that make numbers fragile and data science imperfect also make the world beautiful, so I aim to embrace them as a part of my work. While mistakes are inevitable, I hope that I can render them more predictable and manageable by being observant, open-minded, curious, and questioning the evidence — qualities that will lead me to become not just a better data scientist, but also a better person. Already, thinking holistically and learning to confront ambiguity has led me to confidently explore new subjects, such as politics. As a high school senior I know I still have a lot to learn, but I am excited to grow up with a generation that holds people accountable for their actions. That’s why I’m so eager to continue learning math at college, where I can gain holistic insights from intellectually curious students and faculty passionate about understanding and impacting the world, each from their own angle.

Bend, grab, lift, and push. Those are my steps as the foot of the vault to push my teammate upside down and over her head. The vault is one of the hardest synchronized skating elements and the scariest to me. Even though it is unlikely, I imagined my teammate's blade cutting my face when I pushed her over. My teammates helped me overcome my fear. We practiced the vault endlessly, and the other footers helped me perfect my technique. Now, when I look at this picture, instead of my fears, I remember the help and satisfaction of learning something challenging.

Although widely inappropriate, the song “WAP” by Megan Thee Stallion helped me find my confidence as a female. My struggle with confidence began when I was the only minority in middle school and continued when I attended a male dominated engineering high school. Feeling lonely and inadequate in my position, I turned to music to empower myself. It’s exactly songs like WAP, which are fearless, unapologetic, independent, that empower me when I’m insecure. My journey with confidence will never end, but now I hope to help others find the confidence I’ve found from music by becoming a mentor to girls.

I’ve always embraced the carefully curated rules of mathematics. I love how no matter the topic, the process of doing math problems never seems to change. Compared to the more subjective assumptions about characters and themes that I always seem to make in English class, the absolute rules of math comfort me. That’s why I was so excited to discover data science, the topic I want to study in college. I thought that since numbers were so concrete, combining them with data would create objective, impregnable results. Data science gave me hope for efficiently solving problems in an unjust world. I soon found, however, through reading cases studies, that numbers can be just as sticky as literary analysis. I thought that data science could easily make the world a better place, only to realize that it was infected with all the same biases and inefficiency it was trying to fix. One of the case studies was about a professor, Marvin Wolfgang, whose research counted any contact the police had with Black and Latino youth as crime, including traffic violations and stop-and-frisk. His analysis led policymakers to shift federal investment toward juvenile court systems and detention facilities, causing kids to be unfairly criminalized in the public eye, leading to the perpetuation of racial bias and police mistreatment. It was like reading a novel in English class, where a subtle action foreshadows a domino effect of destruction. I felt completely disillusioned. This wasn’t a short story — it was real life. Data scientists failed black and Latino youth. Initially concrete compared to the novels in English class, data science was seeming closer to fiction: human and imperfect. My mind raced. Does this mean all the policies built upon numbers and data are flawed? After endless reading, I arrived at the conclusion that both numbers and human elements are essential to ethical data science, a practice which requires the ability to apply and frame data without perpetuating harmful stereotypes. Instead of shying away from grey areas of implicit bias and inconclusive results, I will embrace the complexities of human life while doing data analytics. The very things that make numbers fragile and data science imperfect also make the world beautiful, so I aim to embrace them as a part of my work. While mistakes are inevitable, I hope that I can render them more predictable and manageable by being observant, open-minded, and questioning the evidence — qualities that will lead me to become not just a better data scientist, but also a better person. Already, thinking holistically and learning to confront ambiguity has led me to confidently explore new subjects, such as politics. As a high school senior I know I still have a lot to learn, but I am excited to grow up with a generation that holds people accountable. That’s why I’m eager to join a diverse college community, where I can gain holistic insights from students and faculty passionate about understanding and impacting the world, each from their own angle.