Welcome!
I'm Subin Park, an engineering student with a strong background in system-level programming and a growing interest in low-level machine learning systems.
My work mainly focuses on memory management, parallel processing, and efficient low-level computation, often implementing projects without relying on external libraries to build a deeper understanding of system behavior.

• LiDAR Obstacle Detection (MORAI Simulator + ROS)
  Real-time LiDAR point cloud processing and object clustering.
  Focus: Sensor data subscription, RANSAC plane fitting, Euclidean clustering, ROS modular design.

• Machine Learning Coursework (Inha Univ. EEC3400)
  Covered topics including convex optimization, kernel methods (SVM, Kernel PCA), unsupervised learning (Spectral Clustering, GMM, EM), and theoretical foundations (MLE, MAP, Lagrangian duality). Implemented and analyzed models in Python using NumPy and scikit-learn.

• Deep Learning Coursework (Inha Univ. ECE4456)
  Neural network architecture design and training for image classification.
  Focus: regularization techniques, performance evaluation.

• Dive into Deep Learning (D2L.ai) Study
  Hands-on deep learning experiments using PyTorch and theoretical study.
  Focus: Understanding from low-level tensor operations to network optimization.

• Webserv
  Implementation of a lightweight HTTP/1.1 web server in C++.
  Focus: Low-level socket programming, select-based I/O multiplexing, manual HTTP request parsing and response generation without using high-level frameworks.
  Achieved concurrent client handling and static content serving with minimal external dependencies.

• minishell
  A minimal Unix shell built in C.
  Focus: Lexer/parser construction, abstract syntax tree (AST) execution, built-in command support, signal handling.

• philosopher
  Classic Dining Philosophers problem solved with multithreading (pthreads).
  Focus: Mutex usage, deadlock prevention, precise thread synchronization.

• fdf
  3D wireframe visualization of terrain data using 2D projection techniques.
  Focus: 3D to 2D transformation, matrix operations, Bresenham’s line algorithm.

etc...

• Languages: C, C++, Python
• Key Areas: System programming, memory management, network programming, parallel/concurrent programming, low-level ML systems
• Mindset:
  • Build from first principles.
  • Understand system internals.
  • Optimize for efficiency and clarity.

I am passionate about bridging the gap between low-level system understanding and machine learning execution efficiency.
Currently, I'm exploring compiler technologies (TVM, MLIR) and hardware-aware ML optimization as future research directions.

Thanks for visiting!