Nathan Ong

Nathan Ong is an Assistant Professor of Computer Science in the Department of Mathematics and Computer Science at Duquesne University's School of Science and Engineering. He was a postdoctoral associate at the University of Pittsburgh under a partnership between the Learning Research and Development Center and the University Center for Teaching and Learning, aiming to reexamine and improve the process of student evaluations of teaching for both students and instructors, by providing students a conversational agent to provide their feedback and providing instructors helpful summaries of their evaluations. Nathan did his Ph.D. in computer science at the same institution, focusing on concept map extraction and student trajectory clustering in a given program, geared towards assisting academic advisors. He also completed his B.S. in computer science at the University of Pittsburgh.

Nathan Ong believes in a strong foundation for students in both learning and research, expecting students to obtain the basic knowledge and skills in an era where Large Language Models seem to be an easy substitute. He frequently tries different methodologies for his classes, as a way for students to get exposure to different ways of thinking and learning, and have students develop their critical thinking skills. He hopes that he can impart this philosophy to students, even if it seems difficult, so that they can become more adaptable and resilient in an ever faster moving world.  As part of this process, he also writes for an educational newspaper in South Korea, attempting to prepare educators for the implications of the onset of generative AI and future technological advances.

• Ph.D. in Computer Science, University of Pittsburgh
• B.S. in Computer Science, University of Pittsburgh

Nathan Ong is interested in applications of machine learning. Typically, this includes traditional supervised and unsupervised learning, deep learning, and LLMs and transformer models in subfields like intelligent tutoring systems, natural language processing, computer vision and synthetic dataset generation.  Usually projects are focused on the educational domain, but he has done projects in cancer detection and generation, image understanding, topic modeling, and concept extraction. Nathan also has a continued interest in theoretical computer science, combinatorics, and behavior modeling via Markov and related models.