Hi there! Thanks for reading this thread, any of your advice or comments about my statement would be greatly appreated!
Instructions
You are required to submit a Personal Statement for our program. The personal statement should describe succinctly your reasons for applying to the proposed program at the Viterbi School of Engineering, your preparation for this field of study, study interests, future career plans, other aspects of your background and interests which may aid the admissions committee in evaluating your aptitude and motivation for graduate study.
There is no required/recommended format or length for the personal statement.
My statement
This summer, after graduation from my undergraduate program, I joined an environmental nonprofit organization dedicated to water source protection as a data analysis intern. During my internship, I performed a time series analysis of water quality data for Lake Taihu, the third-largest freshwater lake in China and, most relevant on a personal level, the lake of my hometown, Suzhou. For generations, my ancestors have lived on the shores of Lake Taihu and regarded it as our primary water source and economic lifeblood. I have always been proud to enjoy such a huge, sparkling gem of nature in my hometown. It never occurred to me that the lake might suffer from the deterioration of the aquatic ecosystem, let alone a shortage of drinking water sources.
However, my analysis of the historical water quality data in Lake Taihu unmasked what had been a blissful illusion and disclosed to me a foul and morbidly verdant lake that has been plagued with severe water eutrophication for decades. The accumulation of excessive nutrients released from non-point pollution sources induced the overgrowth of phytoplankton and frequently triggered large-scale outbreaks of cyanobacteria blooms. My time series forecasting revealed that not only was the lake pollution in the past and present alarming, but its water quality is not promising in the foreseeable future. More significantly, the ecological degradation of Lake Taihu will threaten the drinking water safety in the catchment area, affecting not only Suzhou but also the whole Yangtze River Delta. Unexpectedly, the analysis of water quality data in a lake can disclose such a shocking drinking water crisis in East China, where China's economic and political heavyweights locate.
This experience in data analysis has shown me the enormous potential of data science and how it empowers us to uncover the hidden past and predict unknown futures. I look forward to further studying the application of data science during my master's study. My undergraduate training has provided me with a solid theoretical foundation in probability, statistics, and a broad range of mathematical skills such as linear algebra. I broadened my understanding of computer vision and honed my programming skills in Python and Java by multiple applications of deep learning in medical image processing. Currently, I am working on a research project that uses GAN-based medical image augmentation to boost the performance of CNNs in the classification of interstitial lung disease on HRCT images. The rigorous curriculum of MS in Applied Data Science would transform me into a more professional data scientist, educating me in the holistic pipeline of data analytics, introducing me to the state-of-the-art machine learning techniques, and immersing me in real customer data analytic challenges through the professional practicum. However, more importantly, your flexible elective track would allow me to ponder over the security and privacy of big data applications, where my particular study interest lies. My previous research in the economics of privacy made it clear to me that the subject of private data analysis is my passion, which has driven me to this USC Viterbi's program.
Technology that relies on big data analytics has permeated our daily lives, and personal data is the oil that fuels a multitude of mobile applications. There is growing concern over potential erosions of privacy in that personal data is being recorded, gathered, and tracked beyond individuals' control. Though governments have enacted privacy legislation to regulate the collection and processing of user data by technology companies, such strategies limit the productivity that data-driven technology may boost, and the demand gap for personal datasets remains. Instead of restricting the access to datasets, why not create a fair and unified data marketplace where the demand for data can be satisfied with fair compensations for individuals' information disclosure? This idea struck me when I was addressing Problem F, Cost of Privacy, in the 2018 Interdisciplinary Contest in Modeling during my junior winter vacation. In designing the pricing function of queries, we were inspired to add random noise to the true query results and charge data buyers for the degree of perturbation they can tolerate. It was based on this assumption that we finally established our query-based pricing model of private data in the contest.
Nevertheless, I did not regard this result as the ending of my engagement with data commercialization but decided to make it concrete. I aimed to determine an arbitrage-free pricing model of data commodities with which individuals are able to control their private data through financial means. With this approach, capital can flow from data consumers to data owners freely, similar to what prevails in the derivatives market. I chose this research topic as my final year project. It took me on a journey to realize that the core of quantifying the economic value of data is the trade-off between privacy and utility. In this context, I was naturally drawn to differential privacy, a theory founded on noise generation, which parallels my idea of noise addition in the contest. The more I immersed myself in the discussion of differential privacy, the more I find the elegant balance between the privacy and accuracy of statistical analysis it achieves as well as the field of data privacy fascinating. I look forward to continuing my study of this field in depth under the guidance of Prof. X and Prof. Y in the USC CS Theory Group. I am deeply attracted by Prof. X's innovative work in privacy-preserving algorithms and Prof. Y's contemplation in the interface between algorithmic game theory and mechanism design. The opportunity offered by this master's program would allow me to further my thinking both in the auction design of information markets and in the broader privacy-preserving data analysis. Therefore, I believe that the Viterbi School of Engineering is the ideal place to develop my academic interests.
My internship at the nonprofit organization has shown me the impact of public welfare initiatives on society and the urgency of their decision-making transformation with data-driven analytics in the digital era. For one thing, I hope to become a data science practitioner who brings value to the industries where advanced data analytics capabilities are in demand. For another, I aspire to be a guardian in the data-driven world, safeguarding data privacy but also promoting the circulation of data and the development of data-centric innovation. With the prospect of creating a trustworthy digital society, I am convinced that the best opportunity for me to follow my chosen path is to pursue my graduate education in Applied Data Science at the USC Viterbi School of Engineering.
Viterbi School of Engineering application
Instructions
You are required to submit a Personal Statement for our program. The personal statement should describe succinctly your reasons for applying to the proposed program at the Viterbi School of Engineering, your preparation for this field of study, study interests, future career plans, other aspects of your background and interests which may aid the admissions committee in evaluating your aptitude and motivation for graduate study.
There is no required/recommended format or length for the personal statement.
My statement
This summer, after graduation from my undergraduate program, I joined an environmental nonprofit organization dedicated to water source protection as a data analysis intern. During my internship, I performed a time series analysis of water quality data for Lake Taihu, the third-largest freshwater lake in China and, most relevant on a personal level, the lake of my hometown, Suzhou. For generations, my ancestors have lived on the shores of Lake Taihu and regarded it as our primary water source and economic lifeblood. I have always been proud to enjoy such a huge, sparkling gem of nature in my hometown. It never occurred to me that the lake might suffer from the deterioration of the aquatic ecosystem, let alone a shortage of drinking water sources.
However, my analysis of the historical water quality data in Lake Taihu unmasked what had been a blissful illusion and disclosed to me a foul and morbidly verdant lake that has been plagued with severe water eutrophication for decades. The accumulation of excessive nutrients released from non-point pollution sources induced the overgrowth of phytoplankton and frequently triggered large-scale outbreaks of cyanobacteria blooms. My time series forecasting revealed that not only was the lake pollution in the past and present alarming, but its water quality is not promising in the foreseeable future. More significantly, the ecological degradation of Lake Taihu will threaten the drinking water safety in the catchment area, affecting not only Suzhou but also the whole Yangtze River Delta. Unexpectedly, the analysis of water quality data in a lake can disclose such a shocking drinking water crisis in East China, where China's economic and political heavyweights locate.
This experience in data analysis has shown me the enormous potential of data science and how it empowers us to uncover the hidden past and predict unknown futures. I look forward to further studying the application of data science during my master's study. My undergraduate training has provided me with a solid theoretical foundation in probability, statistics, and a broad range of mathematical skills such as linear algebra. I broadened my understanding of computer vision and honed my programming skills in Python and Java by multiple applications of deep learning in medical image processing. Currently, I am working on a research project that uses GAN-based medical image augmentation to boost the performance of CNNs in the classification of interstitial lung disease on HRCT images. The rigorous curriculum of MS in Applied Data Science would transform me into a more professional data scientist, educating me in the holistic pipeline of data analytics, introducing me to the state-of-the-art machine learning techniques, and immersing me in real customer data analytic challenges through the professional practicum. However, more importantly, your flexible elective track would allow me to ponder over the security and privacy of big data applications, where my particular study interest lies. My previous research in the economics of privacy made it clear to me that the subject of private data analysis is my passion, which has driven me to this USC Viterbi's program.
Technology that relies on big data analytics has permeated our daily lives, and personal data is the oil that fuels a multitude of mobile applications. There is growing concern over potential erosions of privacy in that personal data is being recorded, gathered, and tracked beyond individuals' control. Though governments have enacted privacy legislation to regulate the collection and processing of user data by technology companies, such strategies limit the productivity that data-driven technology may boost, and the demand gap for personal datasets remains. Instead of restricting the access to datasets, why not create a fair and unified data marketplace where the demand for data can be satisfied with fair compensations for individuals' information disclosure? This idea struck me when I was addressing Problem F, Cost of Privacy, in the 2018 Interdisciplinary Contest in Modeling during my junior winter vacation. In designing the pricing function of queries, we were inspired to add random noise to the true query results and charge data buyers for the degree of perturbation they can tolerate. It was based on this assumption that we finally established our query-based pricing model of private data in the contest.
Nevertheless, I did not regard this result as the ending of my engagement with data commercialization but decided to make it concrete. I aimed to determine an arbitrage-free pricing model of data commodities with which individuals are able to control their private data through financial means. With this approach, capital can flow from data consumers to data owners freely, similar to what prevails in the derivatives market. I chose this research topic as my final year project. It took me on a journey to realize that the core of quantifying the economic value of data is the trade-off between privacy and utility. In this context, I was naturally drawn to differential privacy, a theory founded on noise generation, which parallels my idea of noise addition in the contest. The more I immersed myself in the discussion of differential privacy, the more I find the elegant balance between the privacy and accuracy of statistical analysis it achieves as well as the field of data privacy fascinating. I look forward to continuing my study of this field in depth under the guidance of Prof. X and Prof. Y in the USC CS Theory Group. I am deeply attracted by Prof. X's innovative work in privacy-preserving algorithms and Prof. Y's contemplation in the interface between algorithmic game theory and mechanism design. The opportunity offered by this master's program would allow me to further my thinking both in the auction design of information markets and in the broader privacy-preserving data analysis. Therefore, I believe that the Viterbi School of Engineering is the ideal place to develop my academic interests.
My internship at the nonprofit organization has shown me the impact of public welfare initiatives on society and the urgency of their decision-making transformation with data-driven analytics in the digital era. For one thing, I hope to become a data science practitioner who brings value to the industries where advanced data analytics capabilities are in demand. For another, I aspire to be a guardian in the data-driven world, safeguarding data privacy but also promoting the circulation of data and the development of data-centric innovation. With the prospect of creating a trustworthy digital society, I am convinced that the best opportunity for me to follow my chosen path is to pursue my graduate education in Applied Data Science at the USC Viterbi School of Engineering.
