Zijian Wang – AWS AI Labs – Natural Language Processing

Zijian Wang

I am currently an Applied Scientist at AWS AI Labs, working on large language models (LLM) and their application to code.

I graduated with a research-based master's degree in Symbolic Systems from Stanford University in 2020. My research focuses on Natural Language Processing, with an emphasis on Computational Social Science, Computational Pragmatics, and Question Answering. I was a member of the Stanford NLP Group, advised by Prof. Chris Potts.

Previously, I received my Bachelor's degree in Computer Science and Linguistics from the University of Michigan, where I worked with Prof. David Jurgens and Prof. Kevyn Collins-Thompson. I received a second Bachelor's degree in Electrical and Computer Engineering from Shanghai Jiao Tong University via the dual degree program.

Email: zijwang@cs.stanford.edu | Twitter | Google Scholar | CV

2023/01: I am co-organizing the second Deep Learning for Code (DL4C) workshop at ICLR'23. The deadline is Feb. 3rd, 2023. Please consider submitting your work to our workshop :)

Publications

*=equal contribution; ^†=author is an intern

	CoCoMIC: Code Completion By Jointly Modeling In-file and Cross-file Context Yangruibo Ding^†, Zijian Wang, Wasi Ahmad, Murali Krishna Ramanathan, Ramesh Nallapati, Parminder Bhatia, Dan Roth, and Bing Xiang arXiv*, 2022 paper
	Multi-lingual Evaluation of Code Generation Models Ben Athiwaratkun, Sanjay Krishna Gouda, Zijian Wang, ..., Sudipta Sengupta, Dan Roth, and Bing Xiang ICLR, 2023 paper / code + data
	ReCode: Robustness Evaluation of Code Generation Models Shiqi Wang, Zheng Li^†, Haifeng Qian, Chenghao Yang, Zijian Wang, Mingyue Shang, Varun Kumar, Samson Tan, Baishakhi Ray, Parminder Bhatia, Ramesh Nallapati, Murali Krishna Ramanathan, Dan Roth, and Bing Xiang arXiv, 2022 paper / code + data
	ContraGen: Effective Contrastive Learning For Causal Language Model Nihal Jain^†, Dejiao Zhang, Wasi Ahmad, Zijian Wang, Feng Nan, Xiaopeng Li, Ming Tan, Ramesh Nallapati, Baishakhi Ray, Parminder Bhatia, Xiaofei Ma, and Bing Xiang arXiv*, 2022 paper
	Debiasing Neural Retrieval via In-batch Balancing Regularization Yuantong Li^†, Xiaokai Wei, Zijian Wang, Shen Wang, Xiaofei Ma, Parminder Bhatia, and Andrew Arnold 4th Workshop on Gender Bias in Natural Language Processing at NAACL*, 2022 paper
	Towards Differential Relational Privacy and its use in Question Answering Simone Bombari^†, Alessandro Achille, Zijian Wang, Yu-Xiang Wang, Yusheng Xie, Kunwar Yashraj Singh, Srikar Appalaraju, Vijay Mahadevan, and Stefano Soatto arXiv, 2022 paper
	DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and Quantization Zheng Li^†, Zijian Wang, Ming Tan, Ramesh Nallapati, Parminder Bhatia, Andrew Arnold, Bing Xiang, and Dan Roth ACL, 2022 paper / code
	NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation The NL-Augmenter Team arXiv, 2021 paper / code + data
	Modeling Subjective Assessments of Guilt in Newspaper Crime Narratives Elisa Kreiss, Zijian Wang, and Christopher Potts CoNLL, 2020 paper / code + data / video
	TalkDown: A Corpus for Condescension Detection in Context Zijian Wang and Christopher Potts EMNLP-IJCNLP, 2019 paper / code + data
	Answering Complex Open-Domain Questions Through Iterative Query Generation Peng Qi, Xianwen Lin, Leo Mehr, Zijian Wang, and Christopher D. Manning EMNLP-IJCNLP*, 2019 paper / code / blog post
	Demographic Inference and Representative Population Estimates from \ Social Media Data (Best Poster Award) Zijian Wang, Scott A. Hale, David Adelani, Przemyslaw A. Grabowicz, Timo Hartmann, Fabian Flöck, and David Jurgens TheWebConf (WWW), 2019 (also presented at IC2S2 2019) paper / demo / code / pip-installable package / poster
	It's going to be okay: Measuring Access to Support in Online Communities Zijian Wang and David Jurgens EMNLP, 2018 paper / project webpage / pip-installable package
	Social work in the classroom? A tool to evaluate topical relevance in student writing Heeryung Choi, Zijian Wang, Christopher Brooks, Kevyn Collins-Thompson, Beth Glover Reed, and Dale Fitch EDM, 2017 paper

Academic Services

Organizer/Program Committee/Reviewer:

Co-organizer of the second Deep Learning for Code (DL4C) workshop at ICLR'23
ACL, EMNLP, NAACL, ARR, ICML, NeurIPS, ICWSM, WebSci, AAAI, IJCAI, and their workshops 19'-now
Outstanding Reviewer at ACL'21

Teaching Assistant:

CS 224U Natural Language Understanding, Spring 2020, Stanford University
Applied Machine Learning (Coursera MOOC by the University of Michigan), 2017-2018, University of Michigan (founding and head TA, 250k+ enrollment)
Introduction to Programming, Summer 2015, Shanghai Jiao Tong University
Academic Writing II, Spring 2015, Shanghai Jiao Tong University
Academic Writing I, Fall 2014, Shanghai Jiao Tong University

Other:

Volunteer: EMNLP 19
Webmaster: Stanford NLP Group (2019-2020)
Admission Reader: Stanford Symbolic Systems Program (2019 & 2020)
Transfer Student Leader: University of Michigan (2017-2018)

Homepage credits: Jon Barron