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🎯 About Me

• 🧠 Working Student — AI/ML Researcher @ Fraunhofer FOKUS
• 📱 Former Software Engineer @ KIDA Studios
• 🎓 Master's in Computer Science @ GISMA, Berlin
• 🔬 Active Researcher: Published preprints on Apple MLX benchmarking
• 🌍 Full-stack focus: ML systems + backend APIs + mobile platforms + distributed networks

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🔧 Tech Stack

Languages

ML & AI

Backend & Systems

Mobile & Frontend

Cloud & DevOps

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🚀 Featured Projects

🤖 AI & Agents

• Agentic AI Debate Simulator – Multi-agent orchestration with tool-use
  • Tech: Python, Pydantic AI, DuckDuckGo API, Vertex AI
  • Impact: Autonomous agents dynamically research & debate complex topics
  • ⭐ Best for: Understanding multi-agent systems & LLM orchestration

🌐 Full-Stack

• ScrollCast – News/weather aggregation platform
  • Tech: React, Express.js, Prisma ORM, JWT, Analytics Dashboard
  • Impact: End-to-end architecture with security & data insights
  • ⭐ Best for: Full-stack + backend design

📱 Shipped Products

• Lookout – iOS dashcam with tactical overlays

  • Tech: SwiftUI, AVFoundation, MapKit, picture-in-picture recording
  • Impact: Published on App Store; real-time GPS overlays & dual-camera recording
  • ⭐ Best for: iOS production + real-time systems

• ideaDe – Interactive brainstorming platform

  • Tech: Flutter, Firebase Realtime Database
  • Impact: Published on App Store; real-time collaborative features
  • ⭐ Best for: Cross-platform mobile + Firebase integration

• eli5-extension – Chrome extension for AI explanations

  • Tech: JavaScript, Chrome API, AI integration
  • Impact: Published on Chrome Web Store
  • ⭐ Best for: Browser automation & productivity tools

👁️ Computer Vision & ML

• Face Age Estimation (ONNX) – Multi-task CV pipeline

  • Tech: EfficientNet-B0, ONNX export, C++ OpenCV inference
  • Metrics: <50ms latency, production-ready inference
  • ⭐ Best for: Model deployment & optimization

• B&W Colorization – PyTorch to iOS

  • Tech: PyTorch → CoreML → SwiftUI app
  • Metrics: 91% accuracy on colorization task
  • ⭐ Best for: Mobile ML deployment

• On-Device GANs with MLX – Generative models on Apple Silicon

  • Tech: PyTorch, Apple MLX, GPU acceleration
  • Impact: Research-grade GANs/VAEs; benchmarked vs PyTorch
  • ⭐ Best for: Efficient ML on edge devices

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📚 Research & Publications

• Evaluating Apple's MLX Framework for ML – Comparative benchmarking of K-Means on Apple Silicon

  • Focus: GPU acceleration performance, efficiency vs PyTorch/Scikit-Learn/NumPy

• Impact of Gamification on Learning Retention – Co-authored preprint

  • Focus: Educational technology

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🔭 Current Focus

• 🛰️ 5G Standalone network simulation – Virtualized network orchestration at Fraunhofer FOKUS
• 🌍 LEO satellite communications – Propagation modeling & Doppler shift simulation
• 🤖 Multi-agent AI systems – Tool-use orchestration & agentic workflows
• 📊 Production ML pipelines – Optimization, deployment, benchmarking

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📈 GitHub Stats

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🔗 Connect

LinkedIn