Public Health (Biostatistics)公共卫生-生物统计

基本情况:王同学,首都经贸大学+George Washington University,统计学,86.48+3.73,TOEFL 无,GRE 314 


录取学校:University of Georgia Ph.D. in Bioinformatics 


After graduating from the statistics program from the Capital University of Economics and Business in 2012, I was successfully admitted to George Washington University for my master’s degree in statistics. During this period, I was very interested in data analysis. My mother is a doctor. Under her influence, I am keen on knowledge about biological medicine since childhood and my dream of becoming a biostatistician was really lit up by a professor from Harvard University who gave a report at a seminar in the spring of 2013. Afterward, I carefully read materials about biostatistics and became increasingly enthusiastic about the knowledge in this field. 


During my master’s study, I sat on Ph.D. courses and found that Ph.D. courses were more profound and extensive than undergraduate and postgraduate courses. This could provide solid foundations for proving and deducing the various methods used in papers and enable me understand more clearly why these methods were used and how these methods were specifically used in papers. Moreover, I could use the theories learned from Ph.D. courses to further improve those methods. The second year of my master’s degree, and I am already adapt to the American culture. I appreciate American professors for their rigorous academic style and capabilities to make innovations. In addition, the close connection between academic research and business advancements enables the teaching and scientific research of universities to be applied to practice. It’s my great expectation to study for my doctoral degree under such environments. With those favorable objective resources, I know clearly that in order to become a highly qualified biostatistician, a Ph.D. study is essential. I believe that the solid professional theoretical foundations laid during undergraduate and postgraduate studies as well as the current research in biostatistics “trying improving or using the random-effect model to analyze longitudinal data” under the guidance of the supervisor of master professors will contribute to my application for Ph.D. study. 


Career plan 


During my Ph.D. study, I will systematically study the professional theories about research projects, master the methods for analyzing data in research projects and keep track of the current trends in this field. 


After finishing my Ph.D. study, I will first work in American pharmaceutical factories or research institutions for three to five years. Then, I will return home and do work on drug development and focusing on clinical experiments in medical research institutions or pharmaceutical enterprises. For one thing, although foreign countries may have the same drugs, due to different conditions of the nations and the people, it is necessary to develop new drugs for Chinese people and by then, there will be a great number of clinical experiments to be carried out. For another, China’s medical standards are not as high as those of developed countries, but they are progressing towards the world’s advanced level. Therefore, there is a lot of medical problems to solve. In other words, it is necessary to carry out clinical experiments on the drugs developed for treating relevant diseases. In addition, traditional Chinese medicine is the quintessence of China and it has unique effects, which is different from western medicine. However, traditional Chinese medicine has been questioned by foreign people mainly because it is not supported by the results of data analysis based on clinical experiments. Therefore, clinical experiments on traditional Chinese medicine will allow traditional Chinese medicine to become international. Using traditional Chinese medicine to save more patients is also my dream. Therefore, conducting clinical experiments on traditional Chinese medicine is one of my important jobs after returning home. 


Academic preparation& Intern Experience 


Through study of Mathematical Statistics (99%), Time Series Analysis (97%), Society Statistics (96%) and Real Function and Functional Analysis (95%) during undergraduate years, I gained solid basic mathematical and statistical knowledge. Even during undergraduate years, study should not be limited to theoretical knowledge. This idea was well explained by the published papers I wrote (Influential Factors on China’s Life Insurance Premiums on the Basis of Multiple Linear Regression and Comprehensive Evaluation of the Social Development of the National 30 Provincial Administrative Regions on the Basis of Efficacy Coefficient Method). The first paper was written based on the internship in the National Bureau of Statistics of China in January 2011. After exchanging ideas with the director of the Division of the Service Industry of the Department of National Economic Accounting, I wanted to make a more reasonable and comprehensive evaluation of social development. Then, I was busy in preparing the paper and published it in June. Through the internship in the bank, I gained a further understanding of the process and analysis of big data and became more skillful at using SAS. Internships were of great significance to me and it allowed me to achieve mastery through comprehensive study of my theoretical knowledge, ideas and practice together. 


Additionally, I participated in China Undergraduate Mathematical Contest in Modeling in 2011 and obtained the second prize in TeamA of Beijing Division. It was my first time to face big data and I didn’t know how to deal with them at the beginning. As the team leader, I taught myself matlab within the shortest time, arranged the members to consult relevant references and organized them to discuss data analysis methods, inspiring the members tosolve problems together and finally realizing the objective on matlab. Small as it was, the project challenged and exercised the ability to create, coordinate, communicate and execute for me and my team. 


Research Interests 


I have a strong interest in data and biomedicine. With the improvement in the world’s medical level, new drugs or other treatments will be developed. The only method for scientific testing effects is clinical experiments. Therefore, how to obtain true results according to experimental phenomena is very important. In recent years, genome-wide association study has become an important direction for biostatistics. There have been reports on coronary heart disease, obesity, type 2 diabetes, triglyceride, schizophrenia and relevant phenol types, but there are still a lot of problems to be solved. So it is very important to study new and more appropriate methods to analyze genome-wide association. Medicine, medical experiments, the data about medical experiments, the results obtained from analyzing and processing data about medical experiments as well as their organic connection are where my interest lies. 


Research experience 


During postgraduate years, in addition to studying and consolidating courses such as Data Analysis, Data Mining, Urn Models and clinical Trials with quality and quantity assured as well as teaching myself R, SAS and C languages, I spent most of my time on projects. In the first half of the year, I completed three projects on data mining, data analysis and urn models. One was about simulating pill-problem using maple. At the beginning, the initial data were set based on the exercises in books. After modifying the initial values several times, I noticed that the remaining pills were constantly within a range. Then I tried to determine the relationship between the setting of the initial values of two pills and the remaining pills. After several tries, I finally discovered the law. 


Although the title of the project for this semester has not been determined, I have been doing research on Random-effects Model with my supervisor. After carefully reading Random-Effects Models for Longitudinal Data by Nan M. Laird and James H. Ware and making a summary of Random-Effects Models for Longitudinal Data, I have gained a new understanding of the concept of scientific research: scientific research is not reading dull paper, but thinking on the basis of reading papers, discovering problems, looking for solutions and finally solving problems through experiments. 


I am convinced that with my love for scientific research, professional knowledge accumulated through undergraduate and postgraduate study as well as future Ph.D. study in your university, perseverance, rigorous yet vibrant character, I will go further on the career path of a biostatistician. 



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