About Me

📊 Computer Engineering graduate transitioning into data science with a strong foundation in statistical methods and machine learning. Currently advancing my expertise through a Master’s in Statistical Methods and Applications at Sapienza University of Rome, focusing on healthcare analytics and predictive modeling.

🔬 Published researcher with hands-on experience applying statistical analysis to medical data. My work includes developing novel machine learning formulas for LDL-C estimation, published in Turkish Journal of Biochemistry. Passionate about bridging statistical theory with practical healthcare applications.

💻 Software developer with proven ability to build data-driven applications across multiple platforms. Created applications for Android, iOS, and Steam including a medical calculator with 1000+ downloads, demonstrating my ability to translate statistical models into user-friendly tools. Continuously learning and applying new data science techniques to create meaningful solutions.

Technical Skills

📊 Data Analysis & Statistics
NumPy, pandas, Bayesian & frequentist inference, hypothesis testing, Monte Carlo methods

💻 Programming Languages
Python, R, SQL (MS SQL), Kotlin

🤖 Machine Learning
Regression & classification (scikit-learn), time series forecasting (ARIMA, Prophet)

📈 Data Visualization
Matplotlib, Seaborn, Plotly

🗄️ Databases
SQLite, MS SQL

📱 Mobile Development
Android (Kotlin), Android Studio, Xcode

Developing and Evaluation of a Novel Machine Learning–Driven Formula for LDL-C Estimation Published in Turkish Journal of Biochemistry

Published research introducing the Yayla Formula, a machine learning–derived LDL-C equation developed via symbolic regression on 2,270 patients and validated on 89,274 cases across major lab platforms. The formula achieved 96.3% precision, lower error rates, and outperformed Friedewald and Sampson, especially in high-triglyceride scenarios.

Training Patients: 2,270 Validation Cases: 89,274 Precision: 96.3% Method: ML Driven

LDL Cholesterol & Dyslipidemia Type Analyzer (with Yayla Formula Integration) Published on Google Play Store & Apple App Store – 2025

Clinician-focused app for calculating LDL-C using multiple formulas including the Yayla Formula, and identifying dyslipidemia types from lipid profiles. Provides instant clinical insights and abnormal value flags to support real-time decision-making.

Tech Stack: Kotlin Multiplatform (Android) & Swift (iOS) Features: Multi-formula LDL calculator, dyslipidemia classifier, built-in clinical interpretation Impact: Improves accessibility of advanced lipid analysis tools at the point of care

Handere Commerce: Mobile E-Commerce with Barcode Integration Published on Google Play Store & Apple App Store

Mobile application with native development and advanced barcode scanning functionality. Developed using Kotlin (Android Studio) and Swift (Xcode) with real-time product search integration validated across mobile commerce workflows.

Platforms: Android Studio & Xcode Languages: Kotlin & Swift Integration: Barcode Scanner API Method: Native Mobile Commerce Implementation

Tech Stack: Android SDK, iOS SDK, Kotlin & Swift Features: Real-time barcode scanning, product database integration, mobile commerce interface with search optimization. Impact: Seamless barcode-to-product matching with optimized user experience across both platforms.

Achievement Monster: Steam Achievement Collection System Published on Steam Platform

Steam game demonstrating Unity-Steamworks SDK integration for achievement collection system. Developed using C# scripting and Steam API with 500+ unique achievements validated on Steam platform.

Platform: Unity 2022.3 Integration: Steamworks API Method: C# Steam Communication

Tech Stack: Unity Game Engine & Steamworks SDK Features: Real-time achievement unlocking, Steam overlay integration, persistent tracking system. Impact: Streamlined Steam API implementation with optimized user feedback mechanisms.

Study with Me: Pomodoro Sessions Will be Published on Steam

Steam productivity application demonstrating Unity UI/UX design with comprehensive study session management. Developed using C# scripting and Unity framework with multiple timing modes, animated UI elements, and integrated database system for user progress tracking.

Platform: Unity 2022.3 Integration: Steam API & Database Method: C# Productivity Application Development

Tech Stack: Unity Game Engine, C# & Steam SDK Features: Multiple Pomodoro timers, dynamic animations, integrated music playlists, todo list management, statistics tracking with persistent database storage, Steam achievements integration. Impact: Comprehensive study productivity tool with gamified progress tracking and seamless Steam platform integration.