{"id":81,"date":"2025-03-20T23:26:16","date_gmt":"2025-03-20T23:26:16","guid":{"rendered":"https:\/\/zhijing-jin.com\/home\/?page_id=81"},"modified":"2026-03-16T06:47:50","modified_gmt":"2026-03-16T06:47:50","slug":"bio","status":"publish","type":"page","link":"https:\/\/zhijing-jin.com\/home\/index.php\/bio\/","title":{"rendered":"Bio"},"content":{"rendered":"\n
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Bio for Invited Talks: <\/strong>Zhijing Jin (she\/her) is an Assistant Professor in Computer Science at the University of Toronto, and also a Research Scientist at Max Planck Institute in Germany. She is a faculty member at the Vector Institute, a CIFAR AI Chair, an ELLIS advisor, and faculty affiliate at CHAI at UC Berkeley, Schwartz Reisman Institute in Toronto, and Future of Life Institute. She co-chairs the ACL Ethics Committee, and the ACL Year-Round Mentorship. Her research focuses on Causal Reasoning with LLMs, and AI Safety in Multi-Agent LLMs. She has received the ELLIS PhD Award<\/a>,\u00a0three Rising Star awards<\/a>,\u00a0two Best Paper awards<\/a>\u00a0at NeurIPS 2024 Workshops, two\u00a0PhD Fellowships<\/a>, and a postdoc fellowship. She has authored over 100 papers, many of which appear at top AI conferences (e.g., ACL, EMNLP, NAACL, NeurIPS, ICLR, AAAI), and her work have been featured in CHIP Magazine, WIRED, and MIT News. She co-organizes many workshops (e.g., several\u00a0NLP for Positive Impact Workshop<\/a>s\u00a0at ACL and EMNLP, and\u00a0Causal Representation Learning Workshop<\/a>\u00a0at NeurIPS 2024), and leads the\u00a0Tutorial on Causality for LLMs<\/a>\u00a0at NeurIPS 2024, and\u00a0Tutorial on CausalNLP<\/a>\u00a0at EMNLP 2022. See more info at\u00a0zhijing-jin.com<\/a><\/p>\n\n\n\n

Shorter Bio: <\/strong>Zhijing Jin<\/a> (she\/her) is an Assistant Professor at the University of Toronto and Research Scientist at the Max Planck Institute. She serves as a CIFAR AI Chair, ELLIS advisor, faculty member at the Vector Institute, and faculty affiliate of CHAI at UC Berkeley. She co-chairs the ACL Ethics Committee, and ACL Year-Round Mentorship. Her research focuses on Causal Reasoning with LLMs, and AI Safety in Multi-Agent LLMs. She has over 100 papers and received the ELLIS PhD Award<\/a>,\u00a0three Rising Star awards<\/a>, and\u00a0two Best Paper awards<\/a>\u00a0at NeurIPS 2024 Workshops.<\/p>\n\n\n\n

Talk Abstract<\/strong><\/h2>\n\n\n\n

Can LLMs Achieve Causal Reasoning and Cooperation?<\/em><\/em><\/h4>\n\n\n\n

Causal reasoning is a cornerstone of human intelligence and a critical capability for artificial systems aiming to achieve advanced understanding and decision-making. While large language models (LLMs) excel on many tasks, a key question remains: How can these models reason better about causality? Causal questions that humans can pose span a wide range of fields, from Newton\u2019s fundamental question, \u201cWhy do apples fall?\u201d which LLMs can now retrieve from standard textbook knowledge, to complex inquiries such as, \u201cWhat are the causal effects of minimum wage introduction?\u201d\u2014a topic recognized with the 2021 Nobel Prize in Economics. My research focuses on automating causal reasoning across all types of questions. To achieve this, I explore the causal reasoning capabilities that have emerged in state-of-the-art LLMs, and enhance their ability to perform causal inference by guiding them through structured, formal steps. Further, I also introduce how causality of individual behavior can link to group outcomes, and cover findings in our multi-agent simulacra work about whether LLMs learn to cooperate. Finally, I will outline a future research agenda for building the next generation of LLMs capable of scientific-level causal reasoning.<\/p>\n\n\n\n

Photo<\/strong><\/h2>\n\n\n\n

Here are two photos of mine: (1) a talk photo<\/a>, and (2) a close-up profile photo<\/a>. Feel free to select the one that best suits your use case, and don\u2019t hesitate to crop it if necessary.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Our Funders<\/h2>\n\n\n\n

We are thankful to entities who fund our research.<\/p>\n\n\n\n

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Bio for Invited Talks: Zhijing Jin (she\/her) is an Assistant Professor in Computer Science at the University of Toronto, and also a Research Scientist at Max Planck Institute in Germany. She is a faculty member at the Vector Institute, a CIFAR AI Chair, an ELLIS advisor, and faculty affiliate at CHAI at UC Berkeley, Schwartz […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-81","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/zhijing-jin.com\/home\/index.php\/wp-json\/wp\/v2\/pages\/81","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/zhijing-jin.com\/home\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/zhijing-jin.com\/home\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/zhijing-jin.com\/home\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/zhijing-jin.com\/home\/index.php\/wp-json\/wp\/v2\/comments?post=81"}],"version-history":[{"count":28,"href":"https:\/\/zhijing-jin.com\/home\/index.php\/wp-json\/wp\/v2\/pages\/81\/revisions"}],"predecessor-version":[{"id":259,"href":"https:\/\/zhijing-jin.com\/home\/index.php\/wp-json\/wp\/v2\/pages\/81\/revisions\/259"}],"wp:attachment":[{"href":"https:\/\/zhijing-jin.com\/home\/index.php\/wp-json\/wp\/v2\/media?parent=81"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}