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📧 yuting.yang@childrens.harvard.edu
I'm a postdoctoral research fellow in the Computational Health Informatics Program, Boston Children’s Hospital (BCH), Harvard Medical School (HMS), under the guidance of Timothy Miller, PhD and William La Cava, PhD.
My current research focuses on the development of foundation models in health. Specifically, I am working on multi-modal foundation models for cardiology medical records. I'm also affiliated with the Congenital Heart Artificial Intelligence (CHAI) Lab, BCH,
where I work closely with Joshua Mayourian, MD, PhD on buliding AI tools specific for pediatric and congenital heart care.
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| 2017-2023 |
University of Chinese Academy of Sciences (UCAS)
PhD in Computer Science and Technology Trustworthy AI, Dialogue System Supervisor: Jintao Li and Juan Cao |
| 2021-2022 |
National University of Singapore (NUS)
Visiting Research Scholar in NExT++ Research Center Supervisor: Tat-Seng Chua |
| 2013-2017 |
Jilin University (JLU)
BS in Computer Science and Technology School of Computer Science and Technology |
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Zhen Zhang, Peng Wu, Yuting Yang and Xuran Li. TOSEM 2025 Proposed a relative activation–deactivation abstraction approach to characterize the decision logic of a deep learning model. |
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Yuting Yang, Pei Huang, Feifei Ma, Juan Cao and Jintao Li. LREC-COLING 2024 Proposed attention-based diversity to promote model diversity in ensemble, which consistently improves the general robustness against various types of adversarial perturbation and presents good interpretability. |
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Pei Huang, Haoze Wu, Yuting Yang, Ieva Daukantas, Min Wu, Yedi Zhang and Clark Barrett AAAI 2024 Proposed an efficient framework for formally verifying quantized neural networks. |
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Yuting Yang, Pei Huang, Juan Cao, Jintao Li, Yun Lin and Feifei Ma Frontier of Computer Sciences 2024 Among first to propose that prompt can be maliciously constructed to arise robustness issues of pre-trained language models, which later became a popular research topic (LLM alignment) with the popularity of large models and prompt learning. |
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Yuting Yang, Wenqiang Lei, Pei Huang, Juan Cao, Jintao Li and Tat-Seng Chua WWW 2023 To improve generation ability of pre-trained language models in few-shot scenerios, designed a dual prompt learning framework. |
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Yuting Yang, Pei Huang, Feifei Ma, Juan Cao, Jintao Li and Jian Zhang ECML-PKDD 2023 Proposed “weak robustness” to evaluate DNN’s capability of resisting perturbation and establish the concept of “sufficiently robust”. Proposed a formal framework to evaluate word-level robustness of NLP models including estimating bounds for robust regions and quantifying robustness outside robust regions. |
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Pei Huang*, Yuting Yang*, Fuqi Jia, Minghao Liu, Feifei Ma, and Jian Zhang (*Co-First Author) AAAI 2022 Proposed a model-agnostic method for enhancing the word-level robustness of deep NLP models. Via input perturbation, the method can significantly decrease the rate of successful perturbation and maintain generalization to a great extent. |
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Pei Huang*, Yuting Yang*, Minghao Liu, Fuqi Jia, Feifei Ma, and Jian Zhang (*Co-First Author) ISSTA 2022 Introduced a notion of ε-weakened robustness for analyzing the reliability and some related quality issues of deep neural networks. |
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| 2018 Spring | Teaching Assistant, Multimedia Technology, UCAS |
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| Reviewer/PC member | AAAI 2022-2023, ACL Rolling Review (2021-2022), WWW 2022, EAAI 2022, KDD 2023 |
| Conference Volunteer | ICDM 2019 |
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| 2020 | Merit Student, UCAS |
| 2017-2023 | Academic Scholarship, UCAS |
| 2014-2015 | National Undergraduate Scholarship, Ministry of Education of China |