I am interested in projects with a substantial theoretical component as well as a practical one. This includes projects on:

• algorithms analysis (complexity, worst-case and in particular average-case time analysis of algorithms)

• AI assisted code generation

• optimal control, dynamic programming

• Markov chains and shuffling

• entropy and file compression (willing to supervise self-proposed projects in the area)

• Mathematica programming language applications

I teach both in the CS BSc (Theory of Computation) and in the CS DSA (the course Algorithms and Data Structures for Analytics).

DSA students: note that I cover Markov chains in CS4704 (Algorithms and Data Structures for Analytics). It is a second semester course, but you can meet me in semester 1 and I can share the notes you need for Markov chain projects. Also, I wrote a Mathematica language tutorial to get you up to speed (in case your project involves Mathematica). You can access R from Mathematica as well in case you want to check something in a more familiar environment.

Some projects, as specified, are to be programmed in the Mathematica programming language (part of these project is to learn the language and I will supply a tutorial file) or can be Python based (in which case the project description states it).

I appreciate enthusiasm, commitment and self-motivation. Due to the high percentage the project represents in the final grade, a timely start and a consistent work ethic are required.