Courses across undergraduate, graduate, and doctoral levels, together with graduate supervision.
An undergraduate course introducing the core paradigms of computational intelligence — artificial neural networks, fuzzy systems, evolutionary computation, and swarm intelligence — with emphasis on the principles behind intelligent optimization...
A graduate course on algorithm design strategies — divide and conquer, dynamic programming, greedy methods, and backtracking — together with rigorous complexity analysis of fundamental problems in graph theory, combinatorics,...
An undergraduate course on object-oriented programming. Topics include classes and objects, encapsulation, inheritance, polymorphism, interfaces, generics, exception handling, and design patterns. Students build hands-on programming projects that reinforce software-engineering fundamentals...
A doctoral course covering classical and modern optimization theory and methods, including convex analysis, nonlinear programming, metaheuristics, and recent advances in learning-assisted optimization. The course bridges theoretical foundations with frontier...
I have supervised more than 20 master’s students. My students have won the National Graduate Scholarship and Excellent Master’s Thesis awards, and have published dozens of papers in top-tier SCI journals (JCR Q1/Q2). Recruitment. I recruit 3-4 master’s students per year in: Computer Science and Technology (academic track) Computer Technology (professional track) Students with a solid foundation in computer science, artificial intelligence, mathematics, or bioinformatics — and with strong motivation for academic research or engineering development — are warmly welcome...