About
I am currently a research scientist at Adobe Research. Previously, 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 broadly focuses on machine intelligence for human intelligence, with applications to (human) learning, productivity, and creativity. My Ph.D. work contributes to this direction in the context of education by developing 1) novel representation and generation methods for (semi-)automatic pesonalized educational content generation, and 2) new algorithms and frameworks for modeling learners and instructors.
At the same time, I am also interested in natural language processing, generative models, and human-computer interaction.
Contact: jackwa at adobe com
News
- Mar 2024: One paper accepted at NAACL 2024 demo track.
- Sept 2023: We are organizing a 2-day workshop on AI for Education at AAAI 2024.
- June 2023: Two papers accepted at BEA workshop @ACL'23.
- June 2023: One paper accepted at SustaiNLP Workshop @ACL'23.
- May 2023: I joined Adobe Research as a research scientist.
- May 2023: I graduated from Rice (BS + MS + PhD). What a ride (2012 - 2023)!
- Apr 2023: One paper accepted at ACL'23.
- Apr 2023: I successfully defended my thesis!
- Mar 2023: One paper accepted at CLeaR'23.
- 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!