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Bio: Zichao (Jack) Wang is a final-year Ph.D. student in electrical and computer engineering at Rice University, advised by Prof. Richard Baraniuk. Previously, he obtained M.S. (2020) and B.S. (2016), both in electrical and computer engineering at Rice University. His research focuses on developing artificial intelligence, machine learning, and natural language processing methods with applications in education and human learning. He has been a research intern with Microsoft Research, NVIDIA Research, and Google AI.

Talk Title: Machine learning for human learning

Talk Abstract: Despite the recent advances in artificial intelligence and machine learning, we have yet to witness the transformative breakthroughs that they can bring to education, and more broadly, to how humans learn. In this talk, I will introduce our two recent research directions in developing AI/ML methods to enable more personalized learning experiences on a large scale. First, I will describe generative modeling and representation learning techniques to enable adaptive learning materials, such as assessment questions and scientific equations, that are personalized to each learner. Second, I will describe a framework for analyzing learners’ open-ended solutions to assessment questions such as code submissions in computer science education. I will also discuss my vision for AI/ML methods for education and human learning, highlighting the fundamental technical challenges, my preliminary attempts, and their potential real-world impacts.