Hello! I would really appreciate it if you could suggest some improvements to the following letter. My goal is to make it as compelling as possible for the Erasmus Mundus scholarship admissions committee.
Any feedback or suggestions would be greatly appreciated. Thanks so much for your help!
Honoured Members of the Admission Committee,
Growing up in Kapuas Hulu, a remote area of Kalimantan, I experienced how acne could affect a young person's daily life and self-confidence. Like many teenagers, I struggled with acne in junior secondary school, but poor access to healthcare in my area, particularly dermatologist clinics, made the experience more difficult. This showed me that people in remote areas face not only personal challenges from health conditions but also barriers to appropriate care. It later formed my interest in Artificial Intelligence and Machine Learning. During university, I joined the Bangkit Academy programme organised by Google Indonesia. I chose the Machine Learning path because I was fascinated by how data trains machines to perform tasks that traditionally require human intelligence. In this programme, the area that stood out most to me was computer vision and its prospective applications in healthcare. For the final project, my team and I developed a computer vision-based application to detect acne types and classify acne severity, aiming to provide users with accessible preliminary information about their skin condition. The project received positive feedback from the programme judges, and our team received USD 10,000 and Google Cloud credits for further development. We also surveyed students at my university and received positive feedback indicating that many students faced a similar challenge. This project became a turning point for me. It showed me how AI and computer vision could turn my personal experience into a solution with the potential to address a wider healthcare challenge.
My Computer Science education gave me a theoretical foundation in programming, probability and statistics, artificial intelligence, and other areas of computing, which I later applied to practical projects. In my final year of study, I further explored computer vision by developing a solution to detect eye health conditions. This project gave me practical experience in transfer learning, fine-tuning, and model deployment. It additionally deepened my interest in the technical challenges of building computer vision systems reliable enough for everyday use.
After graduating, I began my career as an AI Engineer. In this role, I learnt to design, develop and implement AI solutions for real-life users. Working in a professional environment helped me understand the challenges of developing and integrating AI models into truly useful systems. My professional experience made me realise that building effective AI systems requires more than integrating existing models. I increasingly faced problems involving model architecture, data quality, optimisation, and experimental design. These experiences made me realise I need a deeper theoretical background and research training to understand why models work, what their limitations are, and how to improve them, so I can progress toward my goal of becoming an AI researcher.
My professional experience has motivated me to deepen my knowledge and enrol in a master's degree in AI and Computer Vision. I'm particularly interested in the Acquisition, Reconstruction and Medical Imaging course at the University of Bordeaux because it would enable me to understand how medical images are acquired, processed, and analysed. Learning about MRI and CT image reconstruction would help me understand how medical data are prepared before AI systems analyse them. It would also strengthen my understanding of the challenges of applying computer vision and AI to medical image analysis and disease detection, which directly supports my goal of working in medical image analysis.
Meanwhile, the Biomedical Signal Processing course at Pázmány Péter Catholic University would broaden my understanding of biomedical data beyond images. For example, learning to analyse ECG and EEG signals using Fourier analysis, time-frequency analysis, and signal decomposition would help me understand how AI can extract meaningful patterns from physiological data for healthcare applications. The practical laboratory component would enable me to apply these techniques to biomedical data. Together, these two courses would strengthen my ability to work with different forms of healthcare data and support my long-term goals within medical image analysis and AI for healthcare.
Previously, I worked on projects related to acne and eye health; those experiences showed me the potential of computer vision to support healthcare, particularly by helping medical professionals perform their duties, specifically in environments with limited access to specialists or medical equipment. These experiences inspired me to view AI as more than just a tool for building applications. I became motivated to understand the core concepts underlying AI systems and models. I want to study different methodologies to address these limitations and improve the capabilities of AI models.
I want to be more than just a user who integrates AI into existing applications. This realisation has shaped my long-term goal: to become an AI researcher who explores the boundaries of current technology and advances the field of artificial intelligence, ultimately creating solutions that help the wider public. Through the IPCVAI program, I plan to conduct research within medical image analysis, specifically exploring methods for disease detection or classification using technologies such as MRI. I want to investigate how advanced image processing and computer vision techniques can reveal meaningful patterns and details in medical images to support more accurate interpretation and disease detection. Ultimately, I do not want to limit my role to simply using existing AI; I also seek to refine and optimise it to exceed present constraints, building on the experience I have gained so far.
I view IPCVAI as a bridge between the experience I have gained so far and the research career I aspire to pursue. My experiences have made me see the potential of AI and computer vision to tackle healthcare challenges. At the same time, IPCVAI would provide the advanced knowledge and research environment I need to pursue this goal. After graduating, I hope to gain professional and research experience in Europe, particularly in AI and medical imaging, where I can deepen my expertise and apply the knowledge and research skills I develop through IPCVAI. In the long term, I hope to bring this knowledge and experience to Indonesia and contribute to medical advancement, particularly in communities where access to specialised healthcare remains limited.
Any feedback or suggestions would be greatly appreciated. Thanks so much for your help!
Honoured Members of the Admission Committee,
Growing up in Kapuas Hulu, a remote area of Kalimantan, I experienced how acne could affect a young person's daily life and self-confidence. Like many teenagers, I struggled with acne in junior secondary school, but poor access to healthcare in my area, particularly dermatologist clinics, made the experience more difficult. This showed me that people in remote areas face not only personal challenges from health conditions but also barriers to appropriate care. It later formed my interest in Artificial Intelligence and Machine Learning. During university, I joined the Bangkit Academy programme organised by Google Indonesia. I chose the Machine Learning path because I was fascinated by how data trains machines to perform tasks that traditionally require human intelligence. In this programme, the area that stood out most to me was computer vision and its prospective applications in healthcare. For the final project, my team and I developed a computer vision-based application to detect acne types and classify acne severity, aiming to provide users with accessible preliminary information about their skin condition. The project received positive feedback from the programme judges, and our team received USD 10,000 and Google Cloud credits for further development. We also surveyed students at my university and received positive feedback indicating that many students faced a similar challenge. This project became a turning point for me. It showed me how AI and computer vision could turn my personal experience into a solution with the potential to address a wider healthcare challenge.
My Computer Science education gave me a theoretical foundation in programming, probability and statistics, artificial intelligence, and other areas of computing, which I later applied to practical projects. In my final year of study, I further explored computer vision by developing a solution to detect eye health conditions. This project gave me practical experience in transfer learning, fine-tuning, and model deployment. It additionally deepened my interest in the technical challenges of building computer vision systems reliable enough for everyday use.
After graduating, I began my career as an AI Engineer. In this role, I learnt to design, develop and implement AI solutions for real-life users. Working in a professional environment helped me understand the challenges of developing and integrating AI models into truly useful systems. My professional experience made me realise that building effective AI systems requires more than integrating existing models. I increasingly faced problems involving model architecture, data quality, optimisation, and experimental design. These experiences made me realise I need a deeper theoretical background and research training to understand why models work, what their limitations are, and how to improve them, so I can progress toward my goal of becoming an AI researcher.
My professional experience has motivated me to deepen my knowledge and enrol in a master's degree in AI and Computer Vision. I'm particularly interested in the Acquisition, Reconstruction and Medical Imaging course at the University of Bordeaux because it would enable me to understand how medical images are acquired, processed, and analysed. Learning about MRI and CT image reconstruction would help me understand how medical data are prepared before AI systems analyse them. It would also strengthen my understanding of the challenges of applying computer vision and AI to medical image analysis and disease detection, which directly supports my goal of working in medical image analysis.
Meanwhile, the Biomedical Signal Processing course at Pázmány Péter Catholic University would broaden my understanding of biomedical data beyond images. For example, learning to analyse ECG and EEG signals using Fourier analysis, time-frequency analysis, and signal decomposition would help me understand how AI can extract meaningful patterns from physiological data for healthcare applications. The practical laboratory component would enable me to apply these techniques to biomedical data. Together, these two courses would strengthen my ability to work with different forms of healthcare data and support my long-term goals within medical image analysis and AI for healthcare.
Previously, I worked on projects related to acne and eye health; those experiences showed me the potential of computer vision to support healthcare, particularly by helping medical professionals perform their duties, specifically in environments with limited access to specialists or medical equipment. These experiences inspired me to view AI as more than just a tool for building applications. I became motivated to understand the core concepts underlying AI systems and models. I want to study different methodologies to address these limitations and improve the capabilities of AI models.
I want to be more than just a user who integrates AI into existing applications. This realisation has shaped my long-term goal: to become an AI researcher who explores the boundaries of current technology and advances the field of artificial intelligence, ultimately creating solutions that help the wider public. Through the IPCVAI program, I plan to conduct research within medical image analysis, specifically exploring methods for disease detection or classification using technologies such as MRI. I want to investigate how advanced image processing and computer vision techniques can reveal meaningful patterns and details in medical images to support more accurate interpretation and disease detection. Ultimately, I do not want to limit my role to simply using existing AI; I also seek to refine and optimise it to exceed present constraints, building on the experience I have gained so far.
I view IPCVAI as a bridge between the experience I have gained so far and the research career I aspire to pursue. My experiences have made me see the potential of AI and computer vision to tackle healthcare challenges. At the same time, IPCVAI would provide the advanced knowledge and research environment I need to pursue this goal. After graduating, I hope to gain professional and research experience in Europe, particularly in AI and medical imaging, where I can deepen my expertise and apply the knowledge and research skills I develop through IPCVAI. In the long term, I hope to bring this knowledge and experience to Indonesia and contribute to medical advancement, particularly in communities where access to specialised healthcare remains limited.
