Hi, I am applying to the EPFL's master program in data science, can you provide a feedback about my statement of purpose? Thanks in advance.
The choice of Data Science as my academic and professional path is rooted in the passion developed, during my bachelor in Economics and Computer Science at Bocconi University, for quantitative classes like machine learning, statistics, and computer programming. Specifically, in the machine learning course I have learned the theoretical foundations of machine learning and their practical applications, being able to build end-to-end machine learning pipelines using Python. To this regard, I applied what I studied developing a predictive model able to predict the price of residential buildings in three major Italian cities based on the features of such houses. I obtained a satisfying result, placing in the top 5% of the leaderboard in such project. In addition, computer programming has given me the knowledge of the fundamental optimization techniques, both theoretical, and practical when it came to coding. Multivariate distributions and Bayesian and frequentist approaches to statistical inferences are the topics I explored deeper in the statistics course: in one project, I employed Bayesian methods to incorporate prior information in predicting financial market trends, highlighting the applicability of Bayesian inference in dynamic and uncertain environments.
Research-wise, the study of image processing and pattern recognition during my exchange semester spent at Sydney University of Technology, deeply attracted me towards the world of computer vision. During my stay in Sydney, I engaged in research focused on face recognition and biometric authentication using machine learning techniques, and EPFL caught my attention thanks to the groundbreaking work of Professor Andrea Cavallaro in the field of person identification: I am truly eager to collaborate with him to delve deeper into biometric authentication using computer vision techniques, after having read his groundbreaking research on the use of Omni-Scale Feature Learning for Person Re-Identification. To this regard, I am currently focusing on my thesis, which consists of the development of a sophisticated age estimation system using Convolutional Neural Networks to predict the age range of users based on facial features. With this project, I aim at developing a system that protects minors by restricting access to websites considered inappropriate for their age group.
he passion for data science led me to work as a data analyst intern at Cushman and Wakefield (a global leader in commercial real estate services), where I am responsible for collecting and analyzing data with the goal of identifying market trends and generating insights useful for the company to analyze their clients' behaviors and properties performance. In addition, my passion for sports and data science led me to found, last year, a students union that focuses on analyzing artificial intelligence and data science topics applied to the sports world. We are currently working as a team on the development of a predictive machine learning model which can be used to determine the objective value of a football player in the market.
Looking ahead, over the next two years of studies at EPFL, my primary objective is to expand my knowledge of machine learning, mastering advanced machine learning techniques, such as deep learning and reinforcement learning to be able to use them to solve complex challenges in areas such as image and video analysis and natural language processing. I am enthusiastic about continuing my research in the domains of computer vision, further deepening my studies in image and video processing, collaborating with experts in the field such as the already mentioned professor Cavallaro and Professor Pascal Fua, supervisor of the Computer Vision Laboratory. Simultaneously, I am keen on gaining further hands-on experience through research projects on deep learning and internships, where I can apply my theoretical knowledge to real-world problems, specifically those related to environmental sustainability. Indeed, my career plan is to leverage the power of data science with the goal of tackling global challenges, particularly in the context of climate change. I am drawn to develop advanced machine learning models that can revolutionize weather forecasting and climate modeling, aiming to minimize the effects of climate change on human societies and ecosystems. To this regard, I truly believe that the Master of Data Science at EPFL is the ideal path not only because of its global standing, excellence and opportunities for research, but particularly due to the diversity of its courses. Indeed, courses like Risk, rare events and extremes and Large-scale data science for real-world data will provide me with the fundamental knowledge necessary for my career aspirations: they offer a unique blend of theoretical rigor and practical application in risk modeling and large-scale data analysis for climate change research. Leveraging the expertise and the knowledge that I am acquiring thanks to the students union that I am currently running, I aspire to found, within the next ten years, a platform that will utilize advanced data science techniques to optimize various aspects of the sports industry, from performance analysis and injury prevention to player trading, integrating big data analytics, machine learning, predictive modeling and computer vision. To this regard, I am looking forward to taking advantage of the numerous labs that the school has to offer to conduct R&D.
The choice of Data Science as my academic and professional path is rooted in the passion developed, during my bachelor in Economics and Computer Science at Bocconi University, for quantitative classes like machine learning, statistics, and computer programming. Specifically, in the machine learning course I have learned the theoretical foundations of machine learning and their practical applications, being able to build end-to-end machine learning pipelines using Python. To this regard, I applied what I studied developing a predictive model able to predict the price of residential buildings in three major Italian cities based on the features of such houses. I obtained a satisfying result, placing in the top 5% of the leaderboard in such project. In addition, computer programming has given me the knowledge of the fundamental optimization techniques, both theoretical, and practical when it came to coding. Multivariate distributions and Bayesian and frequentist approaches to statistical inferences are the topics I explored deeper in the statistics course: in one project, I employed Bayesian methods to incorporate prior information in predicting financial market trends, highlighting the applicability of Bayesian inference in dynamic and uncertain environments.
Research-wise, the study of image processing and pattern recognition during my exchange semester spent at Sydney University of Technology, deeply attracted me towards the world of computer vision. During my stay in Sydney, I engaged in research focused on face recognition and biometric authentication using machine learning techniques, and EPFL caught my attention thanks to the groundbreaking work of Professor Andrea Cavallaro in the field of person identification: I am truly eager to collaborate with him to delve deeper into biometric authentication using computer vision techniques, after having read his groundbreaking research on the use of Omni-Scale Feature Learning for Person Re-Identification. To this regard, I am currently focusing on my thesis, which consists of the development of a sophisticated age estimation system using Convolutional Neural Networks to predict the age range of users based on facial features. With this project, I aim at developing a system that protects minors by restricting access to websites considered inappropriate for their age group.
he passion for data science led me to work as a data analyst intern at Cushman and Wakefield (a global leader in commercial real estate services), where I am responsible for collecting and analyzing data with the goal of identifying market trends and generating insights useful for the company to analyze their clients' behaviors and properties performance. In addition, my passion for sports and data science led me to found, last year, a students union that focuses on analyzing artificial intelligence and data science topics applied to the sports world. We are currently working as a team on the development of a predictive machine learning model which can be used to determine the objective value of a football player in the market.
Looking ahead, over the next two years of studies at EPFL, my primary objective is to expand my knowledge of machine learning, mastering advanced machine learning techniques, such as deep learning and reinforcement learning to be able to use them to solve complex challenges in areas such as image and video analysis and natural language processing. I am enthusiastic about continuing my research in the domains of computer vision, further deepening my studies in image and video processing, collaborating with experts in the field such as the already mentioned professor Cavallaro and Professor Pascal Fua, supervisor of the Computer Vision Laboratory. Simultaneously, I am keen on gaining further hands-on experience through research projects on deep learning and internships, where I can apply my theoretical knowledge to real-world problems, specifically those related to environmental sustainability. Indeed, my career plan is to leverage the power of data science with the goal of tackling global challenges, particularly in the context of climate change. I am drawn to develop advanced machine learning models that can revolutionize weather forecasting and climate modeling, aiming to minimize the effects of climate change on human societies and ecosystems. To this regard, I truly believe that the Master of Data Science at EPFL is the ideal path not only because of its global standing, excellence and opportunities for research, but particularly due to the diversity of its courses. Indeed, courses like Risk, rare events and extremes and Large-scale data science for real-world data will provide me with the fundamental knowledge necessary for my career aspirations: they offer a unique blend of theoretical rigor and practical application in risk modeling and large-scale data analysis for climate change research. Leveraging the expertise and the knowledge that I am acquiring thanks to the students union that I am currently running, I aspire to found, within the next ten years, a platform that will utilize advanced data science techniques to optimize various aspects of the sports industry, from performance analysis and injury prevention to player trading, integrating big data analytics, machine learning, predictive modeling and computer vision. To this regard, I am looking forward to taking advantage of the numerous labs that the school has to offer to conduct R&D.
