I obtained my Ph.D. (2023), M.S. (2020), and B.S. (2016), all in Electrical and Computer Engineering at Rice University, advised by Richard Baraniuk. Previously, I have also worked at Google AI, NVIDIA Research, and Microsoft Research Cambridge as a research intern.

My research focuses on machine intelligence for human intelligence, with applications in large-scale, personalized learning in education. My goal is to develop novel Intelligent systems to transform the largely static, one-size-fit-all learning practices and enable new opportunities for more effective, personalized human learning. Current research themes include:

  1. Representation and generation methods to enable adaptive learning content (e.g., assessment quiz questions) that personalizes to each learner
  2. Algorithms and frameworks for modeling human (e.g., learners and instructors) behaviors and preferences in large-scale learning scenarios

At the same time, I am broadly interested in natural language processing, generative modeling, and representation learning.

Contact: zw16 at rice edu


  • Apr 2023: One paper accepted at ACL'23.
  • Apr 2023: I successfully defended my thesis!
  • Jan 2023: One paper accepted at ICLR 2023 as spotlight (top 25%). 👉 Code 👈
  • Jan 2023: Invited talk at NWEA and Apple.
  • Oct 2022: Honored and humbled to be chosen as a rising star in data science by the Data Science Institute at the University of Chicago.
  • Oct 2022: One paper accepted at EMNLP'22.
  • Jul 2022: Invited talk at the PhD intern research conference at Google Research.
  • Jun 2022: I am co-organizing an education competition at NeurIPS 2022: Causal Insights for Learning Paths in Education. Check it out here and here!