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