Chulev

I am Chulev—a multidisciplinary aspiring engineer, data scientist, and problem-solver who thrives on projects that sit at the intersection of software, electronics, and advanced mathematics. My background spans a wide range of domains, from machine learning and audio data analysis to designing integrated electronic systems, simulating complex fluid dynamics, and leveraging cutting-edge tools for precise engineering work. I earned my Bachelor of Science in Computer Science with a focus on AI and Data Science from Utrecht University and minored in Mathematics, and I'm currently pursuing a Master of Engineering in Systems of Systems Engineering (Erasmus Mundus Master - EMSSE).

In the realm of machine learning, I have leveraged numerous techniques, including supervised/unsupervised learning, deep neural networks, GANs, CNNs, RNNs, reinforcement learning, the modern double deep Q learning and computer vision algorithms to tackle real-world data problems. My focus on audio data has involved signal processing tasks—isolating patterns within sound, optimizing feature extraction, and fine-tuning neural architectures for robust and adaptable models. I’ve also worked extensively with electronic hardware, designing op-amp circuits, signal conditioning modules, and advanced filters using simulation software like LTSpice to ensure reliability and performance.

On the software development front, I have a strong background in programming across multiple languages and platforms. I have developed applications ranging from web and desktop solutions to custom software for data processing and automation. Python is one of my primary tools—its versatility in data analysis, machine learning, and general-purpose programming has been central to many of my projects. Whether I’m scripting automation tasks, building robust backend systems, or experimenting with innovative AI algorithms, I leverage Python’s rich ecosystem to transform ideas into reality.

A significant part of my skill set lies in the modeling and analysis of logical and sometimes physical systems. My experience with ANSYS and Computational Fluid Dynamics has allowed me to investigate fluid flows, turbulence models, and solid-structure interactions. Coupled with proficiency in AutoCAD, I can not only simulate but also propose schematics and models for practical engineering problems.

Whether I’m analyzing data in Python, R or even SPSS, designing circuit boards in LTSpice, decoding puzzles, or phenomena through differential equations and problem modeling, I continually rely on my strong mathematical and logical foundation—because, in the end, everything is math. Combining this core principle with modern technology, I bridge the gap between theoretical insight and hands-on implementation, consistently delivering innovative solutions that span hardware, software, and data-focused disciplines.