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.
Contact: zw16 at rice edu
Aug 2022: New preprint on retrieval-based controllable generation method, with application to molecule generation for drug discovery; check it out here!
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!
Jan 2022: Started a research internship at NVIDIA Research. I will work on developing new generative models.
Dec 2021: Awarded the Loewenstern Expanding Horizons fellowship! I will be travelling to Alaska in Spring, 2023, to work on Open Education Resources (OER) projects.
Dec 2021: Awarded a fellowship from the Ken Kennedy Institute and Schlumberger. Thanks for the support!
Dec 2021: Our paper on Math formula representation accepted at IEEE BigData'21.
Oct 2021: Invited talk at Nvidia ML Research and at the Ken Kennedy AI and Data Science Conference.
Sep 2021: Our paper on generating math word problems is accepted at EMNLP’21.