Teaching
Scientific Visualization

Scientific Visualization

Course Overview

The Scientific Visualization course provides an in-depth exploration of techniques and tools used to visually analyze scientific data. Students gain hands-on experience implementing visualization algorithms using C++ and OpenGL, while also exploring machine learning applications in visualization using Python.

Topics Covered

Throughout the semester, we covered key topics in scientific visualization, including:

  • Scalar & Surface Visualization: Techniques such as contouring, color mapping, and isosurfaces.
  • Volumetric Visualization: Direct volume rendering, transfer functions, and raycasting.
  • Vector Visualization: Glyph-based, streamline, and LIC (Line Integral Convolution) methods.
  • Tensor Visualization: Diffusion tensor imaging, eigenvector-based methods, and tensor glyphs.
  • Information Visualization: Data representation, interaction, and high-dimensional data visualization.
  • Visual Analytics & Advanced Topics: Integrating visualization with AI/ML, interactive exploration, and large-scale data visualization.

Programming & Assignments

Students implemented visualization techniques using C++ and OpenGL, gaining low-level control over rendering and graphical computations. In addition, I introduced machine learning-based visualization assignments in Python, utilizing frameworks like PyTorch and OpenCV to explore AI-driven visualization methods.

Course Materials

Lecture slides, assignments, and resources are available on the course webpage.