La Salle University Student Candice Schumann to Attend Math Program Sponsored by National Science Foundation and Oregon State University
February 15, 2013
La Salle University sophomore Candice Schumann has been accepted into a course next month on Monte Carlo Methods in Artificial Intelligence sponsored by the National Science Foundation and Oregon State University (OSU).
Out of more than 200 applicants nationwide, only 20 were accepted into the all-expenses-paid course, which will take place from March 18 to 22 at OSU in Corvallis, Ore.
“I hope to learn more about artificial intelligence and its applications. I hope to be able to use this knowledge in my Honors project in my senior year,” said Schumann, who is a double major in mathematics and computer science.
Last summer, Schumann and T.J. Highley, Ph.D., associate professor of computer science at La Salle, received a grant from the University’s School of Arts and Sciences to do research together, which helped prepare Schumann for the short course on Monte Carlo algorithms.
“We developed a complete formal mathematical description of the card game Fairy Tale, implemented it as a computer program, and developed some elementary artificial intelligence strategies for the game,” Highley said.
Highley said the results of the research included implementations of several strategies for the game and a demonstration of a particular anomaly regarding game strategies.
“I like being able to solve problems and make sense out of problems that at first seem complicated,” Schumann said. “Following graduation, I hope to further my studies in computer science in graduate school, focusing on artificial intelligence.”
La Salle University Mathematics Department Chair Jonathan Knappenberger, Ph.D., said Schumann was “an outstanding student” who came to the University with such a strong background in math that she bypassed freshman-level courses in her first year. She’s currently taking courses at the junior level, he said.
Monte Carlo methods are search algorithms based on repeated random sampling. Originally invented in physics to optimize nuclear reactions, they are used in many fields, such as computational biology, finance, astrophysics, and microelectronics. Monte Carlo methods are also being applied to many more practical problems, such as robot planning, species conservation, weather forecasting, and air traffic control.