Denizhan Dakılır

Hello! I’m Denizhan Dakılır, a Senior Full Stack Engineer and Machine Learning enthusiast who thrives on turning complex ideas into elegant, high-performing solutions. My journey into tech wasn’t just about code—it was shaped by a deep curiosity about the world and how we understand it. Before diving into the trenches of software engineering and data science, I spent years studying philosophy, honing my ability to dissect problems, reason critically, and navigate uncertainty. Today, these analytical skills help me solve intricate technical challenges with clarity and creativity.

I’ve built a career architecting cloud-native applications, designing robust ETL pipelines, and implementing ML models that uncover patterns hidden in massive datasets. Whether I’m using .NET Core, React, Python, or Julia, I’m driven by the thrill of crafting systems that not only work seamlessly but also push boundaries—delivering speed, scalability, and meaningful insights that shape strategic decisions.

Beyond technology, I’m fascinated by the interplay between logic, human language, and intelligence. Having explored fields like computational linguistics and natural language processing, I often find myself at the crossroads of theory and application. This balance drives me to experiment with new frameworks, refine best practices, and continuously evolve my skill set.

When I’m not coding or training models, you might find me reading about emerging AI research, analyzing financial data for hidden signals, or experimenting with cryptographic randomness projects. I’m always on the lookout for new challenges—whether it’s optimizing a microservice architecture, contributing to open-source initiatives, or collaborating with multidisciplinary teams to bring ambitious ideas to life.

Ultimately, my work is about more than just engineering. It’s about understanding how people and machines learn, communicate, and interact. I believe that by blending philosophical insight with technical rigor, we can build solutions that are not only powerful but also meaningful.

Technical Skills

  • Languages & Frameworks: C#, Python, JavaScript/TypeScript, SQL, Julia, R, Matlab, F#, Haskell, .NET Core, ASP.NET Core, Entity Framework Core, React.js, Next.js
  • ML & Data: TensorFlow, PyTorch, Scikit-learn, Computer Vision, ETL Pipelines, SQL Server, MongoDB, ML.NET, Kafka, Power BI
  • Cloud & DevOps: Azure (App Services, Functions, DevOps), AWS (Lambda, EC2, S3), Docker, Kubernetes, Jenkins, GitLab CI/CD, Terraform
  • Tools & Practices: Git, Azure DevOps, Jira, Confluence, SonarQube, Microservices, REST APIs, Automated Testing, Agile/Scrum

Notable Projects

  • True Atmospheric Randomness Tool: Developed an advanced file selection system implementing true random number generation via Random.org’s quantum-based API. Utilized PowerShell, Python, and complex mathematical modeling libraries to ensure unparalleled randomness and reliability.

  • I-Ching: Engineered a sophisticated I Ching divination system leveraging cryptographic-grade randomness from atmospheric noise measurements. Implemented using JavaScript, HTML5, CSS3, and REST APIs, featuring seamless binary-decimal transformations.

  • GAS Price Levels: Created a GAS price level indicator by integrating three proprietary alpha factors with dynamic Information Coefficient weighting through rolling 20-period correlations. Developed using Pine Script and TradingView.

  • Oracle - Modern PGM: Built a modern divination system with Next.js and TypeScript, integrating Random.org’s atmospheric noise for true randomization. Enhanced user interaction with the Web Speech API and implemented the Fisher-Yates algorithm.

  • Financial Data Analysis: Developed a REST API for financial time series correlation analysis, featuring automated data processing, Pearson correlation calculations, and interactive Plotly visualizations. Utilized C#, ASP.NET Core, ML.NET, and Plotly.js for comprehensive data insights.

  • BTC Price Prediction: Implemented a cryptocurrency price prediction system using stochastic calculus and ARIMA-GARCH modeling. Achieved 90% (Hey, I made very specific cases against overfitting in my code!) accuracy in backtesting through the integration of Monte Carlo methods and technical analysis tools, developed with Julia and Binance API.