Jacob Schrum

Notable Achievements

Five math majors presented at the Texas Undergraduate Mathematics Conference (TUMC), held at Stephen F. Austin State University, in Nacogdoches, TX, Nov. 2 3, 2018.

  • Aiden Steinle, class of 2020, presented “Staying in Shape with Real-World Mappings.” Steinle’s work was supervised by Professor of Mathematics and Chair of Mathematics and Computer Science Fumiko Futamura.
  • Gillian Glover , class of 2019, presented “Make Money with Linear Algebra: A Model of Portfolio Analysis.” Glover’s math capstone is an extension of work supervised by Dr. Futamura.
  • Stan Kannegieter , class of 2019, presented “The Kissing Disease and Differential Equations.”
  • Will Price, class of 2019, presented “Ms. Pac-Man Eats AI for Breakfast.” Price’s math capstone is an extension of his SCOPE 2018 research under Assistant Professor of Computer Science Jacob Schrum.
  • Mercedes Gonzalez, class of 2021, presented “Restrictions on HOMFLYPT and Kauffman Polynomials Arising from Local Moves.” Gonzalez presented work from a National Science Foundation Research Experience for Undergraduates program, supervised by Dr. Sandy Ganzell from St. Mary’s College of Maryland.
  • Associate Professor of Mathematics Therese Shelton supervised the math capstone work of Glover, Kannegieter, and Price, and she moderated a session of presentations at the TUMC.  
  • Other attendees included Zariah Whyte, class of 2021, and Assistant Professor of Mathematics John Ross.
  • Student lodging, registration, and meals was provided by National Science Foundation award no. DMS-1834888 for 2018. Additional funding was provided by a Faculty–Student Project award, the Fleming Student Travel Fund, and the Department of Mathematics and Computer Science. Southwestern students accounted for five of the 57 presentations given by students representing 21 colleges and universities.
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Expertise

Artificial Intelligence, specifically Evolutionary Computation, Neural Networks, Neuroevolution, Artificial Life, Multiobjective Optimization, and Reinforcement Learning.

I believe that students learn best by doing. A student may forget what he or she has been told in a lecture, but active participation in an activity creates true experience, and leads to habits of thought that are more likely to endure. Students are also particularly apt to learn well from close one-on-one interaction, be it with a professor or a fellow classmate. Therefore, I encourage interaction between students both in and out of class, and also make myself available outside of class for the kind of close professor/student interaction that defines the SU experience.

Education

Ph.D., University of Texas at Austin 2014
MS, University of Texas at Austin 2009
BS, Southwestern University 2006

Honors & Awards

  • Grand prize in BotPrize 2012, a competition to design human-like agents for video games.
  • UTCS Department’s 2012 Teaching Assistant Excellence Award.
  • Inaugural Paideia Scholar at SU in 2006.

Affiliations

  • Member, Association of Computing Machinery (ACM)
  • SIGEVO: Special Interest Group on Genetic and Evolutionary Computation
  • SIGCSE: Special Interest Group on Computer Science Education
  • Member, Institute of Electrical and Electronics Engineers (IEEE)
  • IEEE Computational Intelligence Society
  • Member, Upsilon Pi Epsilon (UPE), Computer Science Honorary Society
  • Member, Pi Mu Epsilon (PME), Mathematics Honorary Society
  • Member, Phi Beta Kappa (PBK), Academic Honor Society

In the News

  • Jacob Schrum
    Hands-on Learning

    Alumnus Jacob Schrum ’06, Assistant Professor of Computer Science, reflects on his time as a student at SU, what it’s like to be on the other side of the desk, and chimes in with his thoughts on Artificial Intelligence. Spoiler alert: we’re not doomed.

  • Assistant Professor of Math and Computer Science, Jacob Schrum, and Lauren Gillespie, Class of 2019.
    Computer Science Majors Develop Artificial Intelligence for Video Games

    SCOPE project focuses on developing intelligent agents using video games and creating visual patterns on a computer screen.