So this is my draft 3rd version, polished by GPT4
I feel like if I have to add something else, then I could emphasize on my undergraduate school is not so good, but during the pandemic era, I am opportune to study the same math class taught by CUHK and THU lecturer, and that's how I actually learnt 1/3 of my math. So in some sort of sense, I am actually educated by CUHK.
Plz do not hestitate to give your opinions! I am new to this, so your suggestion will be pivotal in my application journey!
Motivation Letter :
To whom it may concern at the Admission Committee, KU Leuven,
My acquaintance with my research interests in Functional Data Analysis began with the Linear Regression lecture in my sophomore year. It didn't spark my interest in statistics; rather, it make me realize I have learned nothing from Linear Algebra.
Consequently, this realization led me to re-engage with Linear Algebra, seeking insights through two key textbooks: "Linear Algebra" by Stephen Friedberg and "Linear Algebra Done Right" by Sheldon Axler. Although I was enrolled in a Regression course where the instructor mainly covered on the mathematical deduction of OLS properties, my focus shifted to understanding the relationship between range space and null space. My initial encounters with topics like dual spaces were confusing. Yet, delving into Functional Analysis, and drawing from Sheldon Axler's insights in his new series on Real Analysis, uncovered a hidden world in statistics for me. More precisely, it revealed a unified approach to statistical problems via Banach Space and Hilbert Space: the strong geometric underpinnings of Hilbert Spaces allowed me to conceptualize them as n-dimensional Euclidean Space, which enabled me to transform every projection problem I encountered in my Regression course into a simpler, more familiar concept akin to the Pythagorean theorem. This was my Eureka moment; the complex notations that once seemed daunting now became intuitive. This epiphany fostered my strong research interest in Functional Linear Models(FLM), particular in the method of Reproducing Kernel on Hilbert Space, leading me to choose this area for my bachelor's dissertation.
Following this, I engaged deeply with this field, starting from the benchmark methodologies by HG Muller to cutting edge combinations of high-dimensional analysis with Generalized Linear Models in FLM , and I am working with my coworkers to propose a new algorithm for this model. This exploration not only advanced my understanding , but also enhanced my programming skills in R, enabling me to deliver my methods through simulation.
Recently, I attended a lecture by Tony Cai, a leading figure in data science. During his talk, he made a crucial assertion: "The field of statistics changes every ten years. In this decade, statistics equals data science."
Today, statistics is about the trade-off between computational efficiency and statistical precision. The significant improvements in graphics card capabilities over the last decade have transformed RNN from theoretical concepts into a reality. Despite this progress, the quest for algorithms that are both computationally efficient and effective remains our most contingent challenge in statistics, pending further advancements in computing technology. I believe that understanding the essence of high-dimensional data, and even functional data, is the best way to keep pace with the latest developments in statistics for the next decade or beyond. My participation in various Kaggle competitions, ranging from rudimentary challenges like housing price predictions to the latest ones such as sleep states detection, has solidified my programming skills in Python in converting theoretical knowledge into practical models. Engaging within Kaggle community has significantly enhanced my Machine Learning ability, for example, the bi-GRU model I applied in the last competition.
I determine to become a biostatistician in the future, aiming to unravel the mysteries among genetic data in million-dimensional space. I am convinced that the long-standing reputation of the KU Leuven Statistics Department, its emphasis on mathematical foundations in its lectures, along with its researchers and professors who are experts in semiparametric methods, positions Leuven as a pivotal stepping stone in my career path.
I am excited about the opportunities that this program offers, and I am eager to further explore and specialize in my areas of interest including FDA, nonparametric methods, semiparamteric methods and distributed learning in this vibrant academic community there. Having reaffirmed my commitment to pursuing an academic statistical career during the last two years, I would be honored to have the opportunity to continue it at the Department of Statistics at KU Leuven.
Thank you for taking the time to read my motivation letter.
I feel like if I have to add something else, then I could emphasize on my undergraduate school is not so good, but during the pandemic era, I am opportune to study the same math class taught by CUHK and THU lecturer, and that's how I actually learnt 1/3 of my math. So in some sort of sense, I am actually educated by CUHK.
Plz do not hestitate to give your opinions! I am new to this, so your suggestion will be pivotal in my application journey!
Motivation Letter :
To whom it may concern at the Admission Committee, KU Leuven,
My acquaintance with my research interests in Functional Data Analysis began with the Linear Regression lecture in my sophomore year. It didn't spark my interest in statistics; rather, it make me realize I have learned nothing from Linear Algebra.
Consequently, this realization led me to re-engage with Linear Algebra, seeking insights through two key textbooks: "Linear Algebra" by Stephen Friedberg and "Linear Algebra Done Right" by Sheldon Axler. Although I was enrolled in a Regression course where the instructor mainly covered on the mathematical deduction of OLS properties, my focus shifted to understanding the relationship between range space and null space. My initial encounters with topics like dual spaces were confusing. Yet, delving into Functional Analysis, and drawing from Sheldon Axler's insights in his new series on Real Analysis, uncovered a hidden world in statistics for me. More precisely, it revealed a unified approach to statistical problems via Banach Space and Hilbert Space: the strong geometric underpinnings of Hilbert Spaces allowed me to conceptualize them as n-dimensional Euclidean Space, which enabled me to transform every projection problem I encountered in my Regression course into a simpler, more familiar concept akin to the Pythagorean theorem. This was my Eureka moment; the complex notations that once seemed daunting now became intuitive. This epiphany fostered my strong research interest in Functional Linear Models(FLM), particular in the method of Reproducing Kernel on Hilbert Space, leading me to choose this area for my bachelor's dissertation.
Following this, I engaged deeply with this field, starting from the benchmark methodologies by HG Muller to cutting edge combinations of high-dimensional analysis with Generalized Linear Models in FLM , and I am working with my coworkers to propose a new algorithm for this model. This exploration not only advanced my understanding , but also enhanced my programming skills in R, enabling me to deliver my methods through simulation.
Recently, I attended a lecture by Tony Cai, a leading figure in data science. During his talk, he made a crucial assertion: "The field of statistics changes every ten years. In this decade, statistics equals data science."
Today, statistics is about the trade-off between computational efficiency and statistical precision. The significant improvements in graphics card capabilities over the last decade have transformed RNN from theoretical concepts into a reality. Despite this progress, the quest for algorithms that are both computationally efficient and effective remains our most contingent challenge in statistics, pending further advancements in computing technology. I believe that understanding the essence of high-dimensional data, and even functional data, is the best way to keep pace with the latest developments in statistics for the next decade or beyond. My participation in various Kaggle competitions, ranging from rudimentary challenges like housing price predictions to the latest ones such as sleep states detection, has solidified my programming skills in Python in converting theoretical knowledge into practical models. Engaging within Kaggle community has significantly enhanced my Machine Learning ability, for example, the bi-GRU model I applied in the last competition.
I determine to become a biostatistician in the future, aiming to unravel the mysteries among genetic data in million-dimensional space. I am convinced that the long-standing reputation of the KU Leuven Statistics Department, its emphasis on mathematical foundations in its lectures, along with its researchers and professors who are experts in semiparametric methods, positions Leuven as a pivotal stepping stone in my career path.
I am excited about the opportunities that this program offers, and I am eager to further explore and specialize in my areas of interest including FDA, nonparametric methods, semiparamteric methods and distributed learning in this vibrant academic community there. Having reaffirmed my commitment to pursuing an academic statistical career during the last two years, I would be honored to have the opportunity to continue it at the Department of Statistics at KU Leuven.
Thank you for taking the time to read my motivation letter.
