MCIT Program, Masters Computer Information Technology, Chinese Applicant, Physics Background


XXXX’s MCIT Program is my first choice for further graduate study for a variety of 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 really encouraged by the achievements of the program alumni, most of whom become software engineers or developers, and further contributed to the current vast era of information technology. Therefore, I seek the fullest immersion experience possible in XXXX’s MCIT program since I feel very strongly that it will enable me to explore my individual interests in Computer Science and achieve my career objectives to become a software developer/data architect.


Five years ago, 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 and 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 (CS) to solve real-world problems. This greatly increased my enthusiasm to enhance programming skills and my determination to fully devote myself to the field of computer science, especially with respect to the practical applications.


My exploration in CS was greatly intensified as a Master’s student majoring in Physics at New York University (NYU) where I obtained the grade of A in Computational Physics and A- in BioPhysical Modeling; in addition to a B in Fundamental Algorithms. Moreover, I participated in several projects across diverse fields which served to further enhance my programming abilities. In my first research project at NYU with Prof. XXXX, I applied image processing techniques to track the three-dimensional (3D) trajectories of emulsion droplets. Soon, I began to appreciate the critical importance of programming by successfully operating a decades-old syringe pump with 20 lines Python code. Then I developed my own 3D particle tracking codes in MATLAB and CUDA to analyze the 120 GB 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 both hardware and software and became most interested in computational efficiency and its enhancement.


During my second research project at NYU, I furthered explored in computational efficiency and polished my programming skills. Working with Prof. Edo Kussell, I studied the characteristics of asexual population evolution, and built up the Wright-Fisher model using Python independently. To improve the execution efficiency of the program, I optimized the model by efficiently implementing different programming languages, such as Cython and C++. Finally, I successfully achieved an execution speed of 20 times faster than the original one so that I could run multiple evolution simulations of 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 NYU, 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 100 million data points generated by cosmic ray tracking simulation. Then I implemented the distributed computation techniques on a HPCC and greatly increased the computational efficiency. Though my master’s thesis was completed, I continued to refine this project in my spare time after graduating from NYU. To solve the inconsistency between the input parameters and the mean values of the output histograms obtained by my MLE program, I worked independently investigating possible reasons for this anomaly. After careful examination of all the parameters and analysis of the results, I successfully solved the problem by normalizing the index of the likelihood function. Furthermore, by taking the attenuation effect into consideration, I created a more sophisticated model on which I am currently writing a journal paper. 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 NYU, I was admitted into the Ph.D. Program at the University of North Carolina at Chapel Hill (UNC-CH) and began working on a very interesting data analysis and theoretical research project exploring the collective behavior of crowds and animals. In this project, I implemented multi-agent simulation, conducted an experiment to observe Hexbugs 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, in which I applied the coarse-grain method to molecular dynamics simulation by 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 easy-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. Currently, I don’t have a vast overall knowledge or systematic skills in CS, though to some extent 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 really 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 offers practical scaling computation to handle data analytics tasks, which I see as an integral part of the work which I hope to undertake in the future. CIS 520 “Machine Learning” is also very 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 very much 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 the writing of a Capstone project addressed to solving a real-world problem.


I thank you for considering my application to the MCIT Program at XXXX.


#softewareengineer #computerprogrammer #MCIT #computerscience #information #data

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