Research

Convex optimization as a powerful modeling language: express problems as objectives and constraints, then solve efficiently. Demonstrated with image denoising using CVX.

Demonstrates automatic CAPTCHA recognition using image binarization, digit splitting, and K-nearest neighbor classification, achieving perfect accuracy on EMS package tracking system.

Collecting mouse positions throughout the day and visualizing them as heatmaps reveals user behavior patterns that could potentially be used for person identification.

Implementing Loopy Belief Propagation as a more general optimization approach for MRF, supporting non-grid graphs and non-binary cases, with image restoration experiment results.

Deep dive into what energy functions can be minimized via graph-cut, with the necessary and sufficient condition f(0,0)+f(1,1)≤f(0,1)+f(1,0) and application to image segmentation.

Introducing how to formulate image restoration as a Markov Random Field optimization problem and solve it using graph-cut algorithm to achieve global optimal results efficiently.

No single language fits all scientific computing needs. Following UNIX philosophy—combining C#, MATLAB, and Python through files and system calls—achieves both development and runtime efficiency.