PhD at Max Planck Institute & ETH, affiliated at UMich (2020-2024 expected)
Email: jinzhi@ethz.ch
đ©âđGoogle Scholar / đCV / 
@ZhijingJin
(How to pronounce “Zhijing”: like “G-Jing”, or “Z-Jing”.)
I am an enthusiastic AI researcher and social good promoter. I am currently a PhD supervised by Prof Bernhard Schoelkopf at Max Planck Institute & ETH, mentored by Prof Rada Mihalcea (UMich), and co-supervised through the ELLIS PhD Program by Prof Mrinmaya Sachan and Prof Ryan Cotterell at ETH. My research is supported by the Open Philanthropy AI Fellowship and Future of Life Institute PhD Fellowship.
My research has two goals: (1) to promote NLP for social good, and (2) to use CausalNLP to improve robustness, fairness, and interpretability of NLP models. In the long run, my career goal is to be a professor and provide suggestions for social changes. And one of my life goals is to write a book to the world that can inspire generations of people. (Quick pointers: my bio, and publications.)
Starting from Fall 2023, I will be on the academic job market (for the upcoming round, not yet this round) looking for assistant professorship positions in the US, Canada and Switzerland. My pitch should be around “Causal and Moral Reasoning for Safe LLMs“.
News
For master/undergrad students looking for research experience: We are only selecting the best/most suitable students to our project teams. If you think you are an exceptionally good fit, feel free to apply through this form :).
Research Highlights
- For my NLP for Social Good line of work:
- Check out our overview work: our NLP4SG website, framework of NLP4SG (ACL 2021), tracking how NLP papers align with UN SDGs (2022), our Workshops on NLP for Positive Impact (EMNLP 2022, ACL 2021), and my talk (video, slides) at EMNLP 2022.
- Check out our specific work on COVID policies (EMNLP 2021 Findings), moral exceptions (NeurIPS 2022 Oral), misinformation in climate claims (EMNLP 2022 Findings), healthcare (AAHPM 2020, JPSM 2020), and policymaking (book chapter, 2023)
- For my Causality+NLP line of work:
- Check out our papers using CausalNLP to understand datasets (EMNLP 2021 oral), model performance (NAACL 2022 oral), policies (EMNLP 2021 Findings), and linguistic phenomena (ACL 2022)
- Check out our EMNLP 2022 Tutorial (video, slides for Part1, slides for Part 2, PDF)
- For my efforts to establish a global supportive network for NLP researchers: Check out our ACL Year-Round Mentorship (a network of 650+ mentees and 90+ NLP mentors), and my list of useful resources for career advice in NLP/AI.
Selected Publications
For the complete list of my 30+ papers, please see my CV. To easily cite the papers, feel free to add this .bib file to your overleaf.
When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment
Zhijing Jin*, Sydney Levine*, Fernando Gonzalez*, Ojasv Kamal, Maarten Sap, Mrinmaya Sachanâ , Rada Mihalceaâ , Josh Tenenbaumâ , Bernhard Schoelkopfâ
NeurIPS 2022 (Oral) / CogSci 2022 (Disciplinary Diversity and Integration Award) / Paper / Code / Slides / 5-Min Talk@NeurIPS / Twitter Thread / Invited Talk@CyberValley, Germany
How Is NLP Addressing the UN Sustainable Development Goals?
Fernando Gonzalez*, Zhijing Jin*, Jad Beydoun, Bernhard Schölkopf, Tom Hope, Mrinmaya Sachanâ , Rada Mihalceaâ
2022 / Paper / Code / Slides
Invited Talk at AI+X Summit at ETH
How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact
Zhijing Jin, Geeticka Chauhan, Brian Tse, Mrinmaya Sachan, Rada Mihalcea
Findings of ACL 2021 / Paper / Code / Poster / Slides / Talk / Tweet
MIT News article
Invited Talk at CyberValley, Germany
Mining the Cause of Political Decision-Making from Social Media: A Case Study of COVID-19 Policies across the US States
Zhijing Jin, Zeyu Peng, Tejas Vaidhya, Bernhard Schoelkopf, Rada Mihalcea
Findings of EMNLP 2021 / Paper / Code / Poster / Slides / 7-Min Video
Original or Translated? A Causal Analysis of the Impact of Translationese on Machine Translation Performance
Jingwei Ni*, Zhijing Jin*, Markus Freitag, Mrinmaya Sachan, Bernhard Schölkopf
NAACL 2022 (Oral) / Paper / Code / Slides / Twitter Thread / 13-Min Video
State-of-the-art generalisation research in NLP: a taxonomy and review
Dieuwke Hupkes, Mario Giulianelli, Verna Dankers, Mikel Artetxe, Yanai Elazar, Tiago Pimentel, …, Ryan Cotterell, Zhijing Jin
arXiv 2022 / Paper
Natural Language Processing for Policymaking
Zhijing Jin, Rada Mihalcea
Book Chapter of Handbook of Computational Social Science for Policy 2023 (European Commission Joint Research Center)
Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP
Zhijing Jin*, Julius von KĂŒgelgen*, Jingwei Ni, Tejas Vaidhya, Ayush Kaushal, Mrinmaya Sachan, Bernhard Schölkopf
EMNLP 2021 (Oral) / Paper / Code / Poster / Slides / 12-Min Video / Twitter Thread
TextGen Deep Learning for Text Style Transfer: A Survey
Di Jin*, Zhijing Jin*, Zhiting Hu, Olga Vechtomova, Rada Mihalcea
CL Journal 2022 / Paper / Slides
Robust Tasty Burgers, Soggy Fries: Probing Aspect Robustness in Aspect-Based Sentiment Analysis
Xiaoyu Xing*, Zhijing Jin*, Di Jin, Bingning Wang, Qi Zhang, Xuanjing Huang
EMNLP 2020 / Paper / Code / Slides
Robust Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment
Di Jin*, Zhijing Jin*, Tianyi Joey Zhou, Peter Szolovits
AAAI 2020 (20.6%) / Paper / Code / Slides / Poster / MIT News / ACM TechNews / WeVolver / VentureBeat / Synced (Chinese)
Oral Presentation
TextGen IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation
Zhijing Jin*, Di Jin*, Jonas Mueller, Nicholas Matthews, Enrico Santus
EMNLP 2019 / Paper / Code / Poster
To follow talks on my research, please check out my YouTube.