Hi Team, I am applying for masters program in computer science in the US for fall 2022 semester. This is my first draft.
Having worked for 4 years as a Software Development Engineer after my <Degree> in <Degree Name> , I have developed, deployed and maintained a lot of applications. Spending hours in the trenches to connect the dots between code, the underlying infrastructure and the log files has helped me gain perspective into development and maintenance of micro-services based enterprise applications. Debugging would feel like being a detective in a crime movie where I am the victim. I would spend weeks writing automation scripts to track statuses, send out alerts and summary reports. I wondered how the distributed nature of cloud impact scraping of data and how to distill raw data into intelligent structures. I researched industry standard solutions like ELK or pager duty to streamline the monitoring and reporting component. It was at this process that I realized I needed far more nuanced understanding and a focused education in order to develop scalable applications while increasing automation through programming paradigms.
As a software developer, my first instinct is to automate and reduce human intervention. It is very hard to quantify domain knowledge of a human developer. I want to extract lessons from data produced by applications and redirect human effort towards more abstract challenges faced during the software development lifecycle. What if we could generate a semantic summary of APIs ? Would that make the readability of large code bases easier ? Can I generate test cases to discover dependencies within the program? Can I improve the resiliency and reliability of my application through log analysis ? At graduate school, I intend to work towards applying data mining techniques to extract information from large scale distributed systems and build tools focused on improving developer productivity and operational reliability.
The <University Name> will prepare me to tackle aforementioned engineering and research challenges. The program provides me with the opportunity to work with <Prof A> and <Prof B>. Their combined focus on tackling challenges faced by developers in the real world align closely with my goals. I am looking forward to taking the "Building Scalable Distributed Systems" ,"Information Retrieval" and "Natural Language Processing" courses which will help build upon my understanding of distributed systems and extracting intelligence through text. The fact that these courses are worth 4 credits each emphasizes on how students are allowed to go deep into the subject and explore concepts through application and implementation. I bring on a unique perspective through my professional experience in developing enterprise applications in Java and Python. I hope to contribute to the <LAB Name> through projects leading to publication.
I believe 4 years of professional experience at <Company A> and <Company B> has prepared me for a rigorous masters program at <College Name>. I have collaborated with folks all across the world to achieve a common goal. I have honed my ability to independently identify areas of improvement, translate them into a set of concrete requirements and see it all the way through to its implementation. At <Company A>, I led the design and developed the cleanup workflow on Real Application Clusters. I solved challenges related to multi threaded program execution in distributed environments. I intend to take <course_name> to deepen my knowledge. In 2021, I switched to <Company B> as a Data Engineer where I am responsible for building complete data distribution pipelines for a division with over X billion US dollars worth of assets under management. Here I've firsthand seen the challenges related to managing really large datasets and I am sure "Principles of Scalable Data Management" taught by <Prof C> will enhance my theoretical knowledge and depth.
In addition to my professional experience, an interdisciplinary approach while pursuing a <Qualification> in <DEGREE NAME> has given me an exposure to a wide variety of computer science problems. My mathematical background from my undergraduate courses (Applied Mathematics I - IV) includes advanced courses covering matrix decomposition, multivariate calculus, naive bayes, markov chain and statistics. I've developed a strong understanding in Data Structures, Algorithms, DBMS and Computer Networks which is reflected in the wide array of projects under my repertoire. I developed a career portal which was used by 32 undergraduate and graduate departments to facilitate job opportunities. I wrote the backend architecture and took this opportunity to explore visualization challenges for a variety of key metrics like number of job offers, number of companies, student profile (gender, major) etc.
My undergraduate thesis was in Diabetic Retinopathy Detection under supervision of <THESIS ADv>. There are 77 million diabetics in India alone which motivated me to work towards improving the screening process for early detection, enabling patients to seek effective care in a timely manner. I implemented and compared different neural network architectures using Keras. Lack of medical images across levels of retinopathy lead me to experiment with various pre-processing techniques like advanced histogram equalization, data augmentation and their impact on the final result. I believe courses like "Advanced Machine Learning'' and "Deep Learning." will deepen my academic understanding of learning techniques. I wish to experiment with transfer learning in order to store and apply learning to different areas. This is particularly relevant when the availability of data sets is limited.
In the long-run, I aim to work as an industrial researcher. Focusing on implementing advances in academic research to consumer use cases.<College Name> is my top choice program due to its focus on interdisciplinary collaboration to solve everyday challenges. The campus at Boston is a perfect place to make connections with a thriving academic community and pursue collaborative research. My educational, corporate as well as community engagements have helped me hone some key traits like patience, determination, inquisitiveness, problem-solving and effective communication. I am confident that these traits, backed by my demonstrated technical skills, and industry experience have set me up for success in the Graduate program at <University Name>.
Finally, I would like to thank the admissions committee for taking the time to review my application.
Statement of Purpose
Having worked for 4 years as a Software Development Engineer after my <Degree> in <Degree Name> , I have developed, deployed and maintained a lot of applications. Spending hours in the trenches to connect the dots between code, the underlying infrastructure and the log files has helped me gain perspective into development and maintenance of micro-services based enterprise applications. Debugging would feel like being a detective in a crime movie where I am the victim. I would spend weeks writing automation scripts to track statuses, send out alerts and summary reports. I wondered how the distributed nature of cloud impact scraping of data and how to distill raw data into intelligent structures. I researched industry standard solutions like ELK or pager duty to streamline the monitoring and reporting component. It was at this process that I realized I needed far more nuanced understanding and a focused education in order to develop scalable applications while increasing automation through programming paradigms.
As a software developer, my first instinct is to automate and reduce human intervention. It is very hard to quantify domain knowledge of a human developer. I want to extract lessons from data produced by applications and redirect human effort towards more abstract challenges faced during the software development lifecycle. What if we could generate a semantic summary of APIs ? Would that make the readability of large code bases easier ? Can I generate test cases to discover dependencies within the program? Can I improve the resiliency and reliability of my application through log analysis ? At graduate school, I intend to work towards applying data mining techniques to extract information from large scale distributed systems and build tools focused on improving developer productivity and operational reliability.
The <University Name> will prepare me to tackle aforementioned engineering and research challenges. The program provides me with the opportunity to work with <Prof A> and <Prof B>. Their combined focus on tackling challenges faced by developers in the real world align closely with my goals. I am looking forward to taking the "Building Scalable Distributed Systems" ,"Information Retrieval" and "Natural Language Processing" courses which will help build upon my understanding of distributed systems and extracting intelligence through text. The fact that these courses are worth 4 credits each emphasizes on how students are allowed to go deep into the subject and explore concepts through application and implementation. I bring on a unique perspective through my professional experience in developing enterprise applications in Java and Python. I hope to contribute to the <LAB Name> through projects leading to publication.
I believe 4 years of professional experience at <Company A> and <Company B> has prepared me for a rigorous masters program at <College Name>. I have collaborated with folks all across the world to achieve a common goal. I have honed my ability to independently identify areas of improvement, translate them into a set of concrete requirements and see it all the way through to its implementation. At <Company A>, I led the design and developed the cleanup workflow on Real Application Clusters. I solved challenges related to multi threaded program execution in distributed environments. I intend to take <course_name> to deepen my knowledge. In 2021, I switched to <Company B> as a Data Engineer where I am responsible for building complete data distribution pipelines for a division with over X billion US dollars worth of assets under management. Here I've firsthand seen the challenges related to managing really large datasets and I am sure "Principles of Scalable Data Management" taught by <Prof C> will enhance my theoretical knowledge and depth.
In addition to my professional experience, an interdisciplinary approach while pursuing a <Qualification> in <DEGREE NAME> has given me an exposure to a wide variety of computer science problems. My mathematical background from my undergraduate courses (Applied Mathematics I - IV) includes advanced courses covering matrix decomposition, multivariate calculus, naive bayes, markov chain and statistics. I've developed a strong understanding in Data Structures, Algorithms, DBMS and Computer Networks which is reflected in the wide array of projects under my repertoire. I developed a career portal which was used by 32 undergraduate and graduate departments to facilitate job opportunities. I wrote the backend architecture and took this opportunity to explore visualization challenges for a variety of key metrics like number of job offers, number of companies, student profile (gender, major) etc.
My undergraduate thesis was in Diabetic Retinopathy Detection under supervision of <THESIS ADv>. There are 77 million diabetics in India alone which motivated me to work towards improving the screening process for early detection, enabling patients to seek effective care in a timely manner. I implemented and compared different neural network architectures using Keras. Lack of medical images across levels of retinopathy lead me to experiment with various pre-processing techniques like advanced histogram equalization, data augmentation and their impact on the final result. I believe courses like "Advanced Machine Learning'' and "Deep Learning." will deepen my academic understanding of learning techniques. I wish to experiment with transfer learning in order to store and apply learning to different areas. This is particularly relevant when the availability of data sets is limited.
In the long-run, I aim to work as an industrial researcher. Focusing on implementing advances in academic research to consumer use cases.<College Name> is my top choice program due to its focus on interdisciplinary collaboration to solve everyday challenges. The campus at Boston is a perfect place to make connections with a thriving academic community and pursue collaborative research. My educational, corporate as well as community engagements have helped me hone some key traits like patience, determination, inquisitiveness, problem-solving and effective communication. I am confident that these traits, backed by my demonstrated technical skills, and industry experience have set me up for success in the Graduate program at <University Name>.
Finally, I would like to thank the admissions committee for taking the time to review my application.
