XXXX’s MCIT Program is my first choice for further graduate study for several reasons, especially the accessible and tight-knit alumni network and stellar career services. If I am fortunate to get admitted, I plan to look for a summer internship between my first and second years of study and a unique full-time position with my interdisciplinary background after graduating from the MCIT program. I am encouraged by the achievements of the program alumni, most of whom have become software engineers or developers and made important contributions to the advance of information technology. Therefore, I seek the entire immersion experience possible in XXXX’s MCIT program since I feel that it will enable me to explore my interests in computer science and achieve my career objectives of becoming a software developer and data architect.
When I interned as a summer analyst at CITIC Securities CO., Ltd, China’s largest full-service investment bank, I developed the GARCH model by MATLAB. I applied it to option pricing based on market data. This was the first time that I had the experience of using advanced techniques in computer science to solve real-world problems. This significantly increased my enthusiasm to enhance my programming skills and devote myself to computer science, especially concerning practical applications.
My exploration of CS intensified as a master's student. While majoring in Physics at XXXX University (XXU). I obtained an A in Computational Physics and an A- in Biophysical Modeling. Moreover, I participated in several projects across diverse fields, further enhancing my programming abilities. In my first research project at NYU with Prof. XXXX, I applied image processing techniques to track emulsion droplets' three-dimensional (3D) trajectories.
Soon, I began to appreciate the critical importance of programming by successfully operating a decades-old syringe pump with twenty lines of Python code. Then I developed my 3D particle tracking codes in MATLAB and CUDA to analyze the 120 GB of 3D video data. I next accelerated the analysis by executing the codes on a high-performance computing cluster (HPCC). Through this initial encounter with application programming interfaces, I promoted my programming skills in hardware and software and became most interested in computational efficiency and its enhancement.
During my second research project at XXU, I further explored computational efficiency and polished my programming skills. Working with Prof. XXX, I studied the characteristics of asexual population evolution and independently built up the Wright-Fisher model using Python. To improve the execution efficiency of the program, I optimized the model by efficiently implementing different programming languages, such as Python and C++. Finally, I successfully achieved an execution speed of twenty times faster than the original one to run multiple evolution simulations of an asexual population, each with more generations. The increased sample size allowed me to observe many novel characteristics of the beneficial-mutation selection balance. However, I soon realized this was only the surface of how applications of different programming languages might serve to enhance computational efficiency.
In my final year at XXU, I worked on an astrophysics research project studying Ultra-High Energy Cosmic Rays (UHECRs) for my master’s thesis. Under the direction of Prof. Glennys Farrar, I applied the maximum likelihood estimation (MLE) to determine if certain galaxies were the sources of UHECRs. To analyze the errors of the parameters obtained by MLE, I built a mock data set sampling from all the possible cosmic rays, which have more than one hundred million data points generated by cosmic ray tracking simulation. Then I implemented the distributed computation techniques on an HPCC and significantly increased the computational efficiency. Though my master’s thesis was completed, I continued to refine this project in my spare time after graduating from XXU. To solve the inconsistency between the input parameters and the mean values of the output histograms obtained by my MLE program, I worked independently to investigate the reasons for this anomaly. After carefully examining all the parameters and analyzing the results, I successfully solved the problem by normalizing the index of the likelihood function. Furthermore, I created a more sophisticated model on which I am currently writing a journal paper by considering the attenuation effect. The most valuable treasure from this project is that it set me on the course of mastering the ability to generate data and extract information from existing data sets via computational techniques.
Upon graduating from XXU, I was admitted into the Ph.D. Program at the University of XXXX and began working on fascinating data analysis and theoretical research projects exploring the collective behavior of crowds and animals. In this project, I implemented a multi-agent simulation, conducted an experiment to observe Hex bugs moving in a limited area, and analyzed the data by video recognition. Unfortunately, this project was suspended in early 2017 due to a lack of funding. Then I became involved in the design of multifunctional polymeric rod-like nanocomposites. I applied the coarse-grain method to molecular dynamics simulation in C++ with graphics processing unit (GPU) acceleration. I learned a lot by using HPCC, such as compiling all the packages from the source files rather than easily installing and creating convenient, customized modules of these packages by Lua.
My career goal is to become a software developer/data architect in the technology industry, where I can utilize my background in academic research and implement my new skills in CS. I don’t have vast overall knowledge or systematic skills in CS, though my previous research experiences are related. Therefore, I see the Master of Computer and Information Technology (MCIT) program at the University of Pennsylvania (Penn) as the perfect springboard upon which I can embark on a transition from my Physics background to a successful career in CS. A quick learner who is proud of being self-taught as a coder, I am confident that I will be able to excel in your program.
I appreciate that the elite MCIT program offers entry-level courses for students with limited experience in CS. Within the curriculum, I am especially drawn to CIS 545, “Big Data Analysis,” because it provides practical scaling computation to handle data analytics tasks, which I see as an integral part of the work I hope to undertake in the future. CIS 520 “Machine Learning” is also extremely attractive to me because previously, I took a similar course on Coursera, but I have not had a chance to study it formally. I now look forward to doing so in a world-class institution like XXXX. I am also keenly looking forward to the in-depth research involved in writing a Capstone project addressed to solving a real-world problem.
I thank you for considering my application to the MCIT Program at XXXX.
MCIT Computer Information Technology Personal Statement