I am a PhD student in Electrical and Computer Engineering at Rice University, advised by Prof. Richard Baraniuk. Previously, I obtained M.S and B.S in Electrical and Computer Engineering at Rice University in 2020 and 2016, respectively. I have been a research intern at Google AI (2022), NVIDIA Research (2022), and Microsoft Research Cambridge (2019).

My research focuses on machine intelligence for human intelligence. Specifically, I develop machine learning and natural language processing methods 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. I am currently focusing on the following two directions:

  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 also broadly interested in natural language processing, generative modeling, and representation learning.

I am on the industry and academic job market this year.

Contact: zw16 at rice edu


Jan 2023: Our paper on retrieval-based controllable generation method, with application to molecule generation for drug discovery, is accepted as a spotlight (top 25%) at ICLR 2023. Camera-ready copy to come but a preview is now available on arxiv.

Jan 2023: Invited talk at NWEA.

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. Thanks for the recognition! I will present my research at the two-day workshop in November.

Oct 2022: Our paper on open-ended knowledge tracing is accepted at EMNLP’22.

July 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!

Jun 2022: Started a research internship at Google AI. I will work on generative models for education applications.

April 2022: Two papers accepted at AIED'22: one on automatic scoring and one on question generation with large language models.

Jan 2022: Our paper on using deep learning to approximate convex hulls is accepted at ICASSP'22.

Jan 2022: We won one of the grand prizes at the NAEP Reading Automated Scoring Challenge!