Please note that I use "UXXX" in replacement of the university's name, and welcome to include hints tailor to UK universities if there's any. Thank you!
My intention to take a graduate level computer science programme mainly comes from my shift of interest from laboratory research to an application-oriented industrial career path, and so is the reason why I'm leaving my previous PhD programme in biology with a master's degree.
My attention to computer science was unsurprisingly brought up by the thrilling achievements artificial intelligence made within the past few years. Originated from the eager for understanding what's the difference between artificial intelligence and biological intelligence, I gradually discovered that my interest in their common ground at the 'algorithm level' overweighed their difference at the 'implementation level'. More importantly, modern AI technology introduces me to a wide range of real-world problems this powerful tool can solve, and raises my desire in working from an engineering perspective. In the meantime, I found myself not that comfortable working with wet-lab experiments all day long, so I switched to a project focusing on neuroanatomy, practically dealing with more dry-lab image-processing and analysis, and tried to utilize some CNN networks in my own project. Fortunately, I got my supervisor's kind support on this change of path, and he aided me in building up the image processing and neuron morphology reconstruction pipeline for our customized imaging system. Finding my passion lies more in application-oriented questions, I started to think about pursuing my future career in IT-related industries instead of academia in Biology.
UXXX impressed me with its {a particular institution's name} when I first got into the field of Neuroscience, and it was {a leading researcher's name}'s review on {topic name} that opened the door for me to this interdisciplinary field. Although I'm presently not proficient enough on both sides to combine the two, it is naturally the first place that comes across my mind to dig my potentials. In a more practical sense, I'm really excited about the {a specific section} UXXX's Computer Science Department offers because it's exactly what I need to get firsthand experience in the industry. Above all, I really respect UXXX's global vision and its emphasis on innovation, which makes it my dreamland of study.
Getting into details about this programme, I find it suitable for me mainly because it covers the whole picture in Computer Science for non-CS background students. Apart from what I learnt through my lab work on image processing, I also got to know the importance of the parts I don't really understand. For example, I knew very little about how software is built and executed at an architectural level, which I found vital when I tried to incorporate existing pieces of software from different sources. And database knowledge was also needed to manage the large amount of data we generated in our lab. I'm glad these content are all included in the compulsory modules. As for the optional and elective modules, I'm particularly interested in the AI-related and management-related ones, which just fit my intended career path.
I'm also confident in handling this course with my previous experience. I learnt C++ programming as part of my undergraduate curriculum, and completed online courses in Deep Learning on Coursera in addition to my lab research. As part of my master's research project, I worked on developing the post-acquisition processing pipeline that synthesizes 3D image blocks back to a big volume of a whole mouse brain, and reconstructs single neuron morphology. One of my major contributions is correcting distortion caused by mechanical sectioning of the brain sample during imaging, using the Elastix toolbox. I learnt about types of image transformation and how intensity-based registration algorithms work and successfully tuned the parameter sets to fit our extremely 'sparse' signal distribution, which is different from typical biomedical images that Elastix was designed to work with. At the same time, I got experienced with writing Matlab scripts to implement functions like integrating different command-line software tools and detecting signal missing cases for optimizing the image-acquisition procedure. I also collaborated with a graduate student working on CNN network design to train a segmentation using our manually labeled images, based on her network developed using public neuron reconstruction dataset. Although I didn't craft the network myself, I reviewed the python implementation I practiced in the Coursera Course, and learnt about the basic elements of a typical U-net and its advantage in pixel-wise semantic segmentation problems. Related work was summarized into a modest paper and is currently under review.
For mathematical skills, I checked A-level syllabus and find it's equivalent to the level required for the Chinese College Entrance Exam, which I performed well. During college, I had Advanced Mathematics courses that covered advanced differential and integral calculus, linear algebra, and probability theory, and Biostatistics course teaching statistical analysis in biological context. Moving into the field of Neuroscience and image processing during my master's, I picked up key concepts in information theory, decision models, Fourier transformation, and mathematical basis for deep learning through classroom courses and online self-learning. So I'll be quite comfortable with self-educating if I come across challenging parts in theoretical aspects of the course.
Should I complete this MSc degree, my first consideration is to become a software engineer in machine learning applications relating to biomedical context, as it could make the most of my background. A second choice is going into consultancies since I learnt from my collaboration experience that sometimes algorithm engineers don't really get the point of what kind of output best represents biological issues. I feel it's important to bridge the gap between different stakeholders and I really enjoy promoting communications that bring out reasonable solutions. I think both of my intentions will benefit a lot from this computer science master's programme, and I'd be happy to share my biological background to the community that welcomes interdisciplinary innovations.
I sincerely wish that my application will be considered with approval.
Personal Statement for MSc Computer Science
My intention to take a graduate level computer science programme mainly comes from my shift of interest from laboratory research to an application-oriented industrial career path, and so is the reason why I'm leaving my previous PhD programme in biology with a master's degree.
My attention to computer science was unsurprisingly brought up by the thrilling achievements artificial intelligence made within the past few years. Originated from the eager for understanding what's the difference between artificial intelligence and biological intelligence, I gradually discovered that my interest in their common ground at the 'algorithm level' overweighed their difference at the 'implementation level'. More importantly, modern AI technology introduces me to a wide range of real-world problems this powerful tool can solve, and raises my desire in working from an engineering perspective. In the meantime, I found myself not that comfortable working with wet-lab experiments all day long, so I switched to a project focusing on neuroanatomy, practically dealing with more dry-lab image-processing and analysis, and tried to utilize some CNN networks in my own project. Fortunately, I got my supervisor's kind support on this change of path, and he aided me in building up the image processing and neuron morphology reconstruction pipeline for our customized imaging system. Finding my passion lies more in application-oriented questions, I started to think about pursuing my future career in IT-related industries instead of academia in Biology.
UXXX impressed me with its {a particular institution's name} when I first got into the field of Neuroscience, and it was {a leading researcher's name}'s review on {topic name} that opened the door for me to this interdisciplinary field. Although I'm presently not proficient enough on both sides to combine the two, it is naturally the first place that comes across my mind to dig my potentials. In a more practical sense, I'm really excited about the {a specific section} UXXX's Computer Science Department offers because it's exactly what I need to get firsthand experience in the industry. Above all, I really respect UXXX's global vision and its emphasis on innovation, which makes it my dreamland of study.
Getting into details about this programme, I find it suitable for me mainly because it covers the whole picture in Computer Science for non-CS background students. Apart from what I learnt through my lab work on image processing, I also got to know the importance of the parts I don't really understand. For example, I knew very little about how software is built and executed at an architectural level, which I found vital when I tried to incorporate existing pieces of software from different sources. And database knowledge was also needed to manage the large amount of data we generated in our lab. I'm glad these content are all included in the compulsory modules. As for the optional and elective modules, I'm particularly interested in the AI-related and management-related ones, which just fit my intended career path.
I'm also confident in handling this course with my previous experience. I learnt C++ programming as part of my undergraduate curriculum, and completed online courses in Deep Learning on Coursera in addition to my lab research. As part of my master's research project, I worked on developing the post-acquisition processing pipeline that synthesizes 3D image blocks back to a big volume of a whole mouse brain, and reconstructs single neuron morphology. One of my major contributions is correcting distortion caused by mechanical sectioning of the brain sample during imaging, using the Elastix toolbox. I learnt about types of image transformation and how intensity-based registration algorithms work and successfully tuned the parameter sets to fit our extremely 'sparse' signal distribution, which is different from typical biomedical images that Elastix was designed to work with. At the same time, I got experienced with writing Matlab scripts to implement functions like integrating different command-line software tools and detecting signal missing cases for optimizing the image-acquisition procedure. I also collaborated with a graduate student working on CNN network design to train a segmentation using our manually labeled images, based on her network developed using public neuron reconstruction dataset. Although I didn't craft the network myself, I reviewed the python implementation I practiced in the Coursera Course, and learnt about the basic elements of a typical U-net and its advantage in pixel-wise semantic segmentation problems. Related work was summarized into a modest paper and is currently under review.
For mathematical skills, I checked A-level syllabus and find it's equivalent to the level required for the Chinese College Entrance Exam, which I performed well. During college, I had Advanced Mathematics courses that covered advanced differential and integral calculus, linear algebra, and probability theory, and Biostatistics course teaching statistical analysis in biological context. Moving into the field of Neuroscience and image processing during my master's, I picked up key concepts in information theory, decision models, Fourier transformation, and mathematical basis for deep learning through classroom courses and online self-learning. So I'll be quite comfortable with self-educating if I come across challenging parts in theoretical aspects of the course.
Should I complete this MSc degree, my first consideration is to become a software engineer in machine learning applications relating to biomedical context, as it could make the most of my background. A second choice is going into consultancies since I learnt from my collaboration experience that sometimes algorithm engineers don't really get the point of what kind of output best represents biological issues. I feel it's important to bridge the gap between different stakeholders and I really enjoy promoting communications that bring out reasonable solutions. I think both of my intentions will benefit a lot from this computer science master's programme, and I'd be happy to share my biological background to the community that welcomes interdisciplinary innovations.
I sincerely wish that my application will be considered with approval.
