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Wolfram Summer SchoolFounded in 2003

17th Annual Wolfram Summer School, held at Bentley University June 23–July 12, 2019 Apply Now


Max Yazhbin

Class of 2014


Max graduated with a BS and MEng in chemical engineering, but really wanted to study applied and engineering physics and computer science. He continued on with his education at UIUC as a PhD student for one year until the university no longer had sufficient funds to keep him. He then taught AP calculus BC in Beijing, China, and came back to the USA to attend the 2014 Wolfram Science Summer School. His interests in the past few years have changed to engineering physics, computer science, business, and Chinese. When not in intellectual pursuit, Max runs, swims, and does any other challenging physical activity. He hopes to apply what he learned in the 2014 Wolfram Science Summer School to his own financial and educational business ideas.

Project: Automated Programming Problem Generator

Towards intelligent computer science learning

Traditional education involves humans' interaction with humans using technology that dates from at least 100 years ago, and there is a push from governments, private educational institutions, and compassionate teachers to use more recent technologies in a seamless and effective manner. There have been many attempts over the past 10–20 years to generate more computer-based evaluations of a student's performance in order to give instant feedback to the student, yet all previous attempts suffered from unintelligent feedback or ineffective means of communication. Just in the last five years, there has been the massive open online courses (MOOCs) movement in order to make the best lectures from top universities available for free, yet smart evaluation systems are not in place to act as if a real instructor was there to assist students. The purpose of this project is to form a set of codes to address those challenges by:

  • Creating a set of problems that are on the same level irrespective of the user.
  • Requesting user input to solve a particular problem.
  • Trying to figure out what the user did incorrectly if s/he made a mistake.
  • Giving a hint to the user to see if s/he can give the correct answer.
  • Keeping track of the user's progress and give a summary of their performance and an in-depth look at what they did wrong.

Favorite Outer Totalistic r=1, k=2 2D Cellular Automaton

Rule 15257