Dániel Sajben
Computer Science Engineering student specializing in Deep Learning and Full-Stack Development. Passionate about AI, neural networks, and creating innovative solutions.
View My Work
Computer Science Engineering student specializing in Deep Learning and Full-Stack Development. Passionate about AI, neural networks, and creating innovative solutions.
View My WorkI'm a passionate Computer Science student at Budapest University of Technology and Economics, specializing in Deep Learning and Full-Stack Development. My academic journey includes an exchange program at the Technical University of Munich, where I focused on AI and Graph Neural Networks.
Currently, I'm working on my thesis in FMRI Super-Resolution using Generative AI and diffusion models, while also gaining practical experience as a full-stack developer at Morgan Stanley.
Built a self-driving car simulation with high accuracy in obstacle detection, leveraging neural networks in JavaScript without external libraries. Capable of detecting surroundings using sensors and makes decisions through self-learning.
Developed a CNN-based model enhanced with random forest search that predicts whether a mole is cancerous, achieving 90% accuracy on a balanced dataset. Collaborative academic project.
A powerful fitness tracker web app designed to help users organize workouts and exercises. Features exercise search, logging capabilities, and advanced filtering for easy workout management.
Thesis work on FMRI Super-Resolution using Generative AI and diffusion models to generate FMRI images for data augmentation and improve quality of existing images, assisting medical decision making.
Full-Stack Software Developer
Working as a full-stack software developer with Java Spring backend and Angular frontend, developing financial applications with relational databases. Focus on enterprise-level software development and financial technology solutions.
FMRI Super-Resolution with Generative AI
Developing a Generative AI model using diffusion and LDM architecture to generate FMRI images for data augmentation and improve quality of existing images, ultimately assisting medical decision making and research in neuroscience. I will be competing at the prestigious TDK (Scientific Students' Associations) conference in Hungary in the domain of medical AI with a subtopic of my thesis on super-resolution.
B.Sc. Computer Science Engineering • 2022-2026
Concentrations: Deep Learning and Full-Stack Development
GPA: 4.15/5.00 (Aggregate adjusted credit index: 4.63)
Exchange Program • Computer Science • 2025
QS Top 22 University
Concentrations: Artificial Intelligence and Deep Learning
I'm always interested in discussing new opportunities, innovative projects, and the latest developments in AI and software engineering. Feel free to reach out!