About
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:
- Representation and generation methods to enable adaptive learning content (e.g., assessment quiz questions) that personalizes to each learner
- 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
News
- 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!