Faculty

Institution

Unit

Venue

Yang Liu

Assistant Professor

James Madison University

Affiliations

College of Health and Behavioral Studies

Department of Psychology

Yang Liu is currently serving as a College Of Health And Behavioral Studies Assistant Professor at James Madison University.

Honors

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Experiences

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Education

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Yang Liu

James Madison University

Assistant Professor

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machine learning computer science deep learning applied mathematics data science computational intelligence machine learning research neuroimaging neuroscience machine intelligence artificial intelligence computational imaging medical image computing soft computing manifold learning neuroinformatics brain mapping multi-task learning flow-based generative models future prediction pattern recognition data mining systems neuroscience machine vision image representation web search semantic evaluation applied cognitive psychology knowledge discovery mathematical optimization computational optimization approximate reasoning online learning pattern discovery pattern analysis geometric learning point clouds point cloud processing llm-based agent digital learning integral transforms preference modeling preference learning learning theory sequential learning retrieval technique recurrent neural networks supervised learning machine learning models brain modeling neuroimaging modality prediction modelling predictive learning predictive modeling probabilistic forecasting predictability manifold modeling generative models nonlinear subspace learning intrinsic grassmann averages online linear rare gems learn multiple tasks looped transformers deep neural networks proximal gradient algorithm weight decay llm-enhanced generative retrieval task vectors dense retrieval sequential recommendation neural nets

Notable Scholarly Works

Notable Federal Grants

Collaborators

Task Vectors in In-Context Learning: Emergence, Formation, and Benefit. ArXiv.org (2025)

learning theorylearning sciencestask vectorscontext (linguistics)machine learningapplied cognitive psychologycognitioncognitive sciencemulti-task learning

An Empirical Study on the Power of Future Prediction in Partially Observable Environments. arXiv (Cornell University) (2024)

Kwon, JeongyeolYang, LiuNowak, RobertHanna, Josiah

machine learningcognitive sciencefuture prediction

Everything Everywhere All at Once: Llms Can In-Context Learn Multiple Tasks in Superposition. arXiv (Cornell University) (2024)

Xiong, ZheyangCai, ZiyangCooper, JohnGe, AlbertPapageorgiou, VasilisSifakis, ZackGiannou, AngelikiLin, ZiqianYang, LiuAgarwal, SaurabhChrysos, Grigorios GOymak, SametLee, KangwookPapailiopoulos, Dimitris

deep learningcomputer sciencellm-based agentmachine learningnatural language processingdata scienceneural computationlearn multiple tasksdigital learningmulti-task learning

How Well Can Transformers Emulate In-Context Newton's Method?. arXiv (Cornell University) (2024)

Giannou, AngelikiYang, LiuWang, TianhaoPapailiopoulos, DimitrisLee, Jason D.

deep learningcomputer scienceintegral transformsmachine learningsystems engineeringapplied mathematicsextended reality

Preference Discerning With Llm-Enhanced Generative Retrieval. arXiv (Cornell University) (2024)

web searchinformation retrievallanguage modelssemantic evaluationmachine learningnatural language processingdata sciencepreference modelingllm-enhanced generative retrievalpreference learningtext mining

Unifying Generative and Dense Retrieval for Sequential Recommendation. arXiv (Cornell University) (2024)

Yang, LiuPaischer, FabianHassani, KavehLi, JiachengShao, ShuaiLi, Zhang GabrielHe, YunFeng, XueNoorshams, NimaPark, SemLong, BoNowak, Robert DGao, XiaoliEghbalzadeh, Hamid

computer scienceinformation retrievalmachine learningdense retrievalknowledge discoverydata sciencesequential learningretrieval techniquesequential recommendation

Looped Transformers Are Better at Learning Learning Algorithms. arXiv (Cornell University) (2023)

Yang, LiuLee, KangwookNowak, RobertPapailiopoulos, Dimitris

machine learninglooped transformers

PATHPROX: A Proximal Gradient Algorithm for Weight Decay Regularized Deep Neural Networks. arXiv (Cornell University) (2022)

Yang, LiuZhang, JifanShenouda, JosephPapailiopoulos, DimitrisLee, KangwookNowak, Robert D.

deep learningdeep neural networksproximal gradient algorithmweight decaymachine learningapplied mathematics

Rare Gems: Finding Lottery Tickets at Initialization. arXiv (Cornell University) (2022)

Sreenivasan, KartikSohn, Jy-yongYang, LiuGrinde, MatthewNagle, AlliotWang, HongyiLee, KangwookPapailiopoulos, Dimitris

deep learningcomputer sciencerare gems

Flow-Based Generative Models for Learning Manifold to Manifold Mappings. Proceedings of the AAAI Conference on Artificial Intelligence (2021)

Xingjian ZhenRudrasis ChakrabortyLiu YangVikas Singh

deep learningcomputer sciencecomputational intelligenceflow-based generative modelsmachine learningmanifold learninggeometric learningmachine intelligenceartificial intelligence

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Yang Liu

Assistant Professor

James Madison University

Affiliations

College of Health and Behavioral Studies

Department of Psychology

Yang Liu is currently serving as a College Of Health And Behavioral Studies Assistant Professor at James Madison University.

Honors

Create free account to unlock more features

Experiences

Create free account to unlock more features

Education

Create free account to unlock more features

Yang Liu

James Madison University

Assistant Professor

You can click a keyword to filter the displayed items

Notable Scholarly Works

Notable Federal Grants

Collaborators

Task Vectors in In-Context Learning: Emergence, Formation, and Benefit. ArXiv.org (2025)

learning theorylearning sciencestask vectorscontext (linguistics)machine learningapplied cognitive psychologycognitioncognitive sciencemulti-task learning

An Empirical Study on the Power of Future Prediction in Partially Observable Environments. arXiv (Cornell University) (2024)

Kwon, JeongyeolYang, LiuNowak, RobertHanna, Josiah

machine learningcognitive sciencefuture prediction

Everything Everywhere All at Once: Llms Can In-Context Learn Multiple Tasks in Superposition. arXiv (Cornell University) (2024)

deep learningcomputer sciencellm-based agentmachine learningnatural language processingdata scienceneural computationlearn multiple tasksdigital learningmulti-task learning

How Well Can Transformers Emulate In-Context Newton's Method?. arXiv (Cornell University) (2024)

Giannou, AngelikiYang, LiuWang, TianhaoPapailiopoulos, DimitrisLee, Jason D.

deep learningcomputer scienceintegral transformsmachine learningsystems engineeringapplied mathematicsextended reality

Preference Discerning With Llm-Enhanced Generative Retrieval. arXiv (Cornell University) (2024)

Unifying Generative and Dense Retrieval for Sequential Recommendation. arXiv (Cornell University) (2024)

Yang, LiuPaischer, FabianHassani, KavehLi, JiachengShao, ShuaiLi, Zhang GabrielHe, YunFeng, XueNoorshams, NimaPark, SemLong, BoNowak, Robert DGao, XiaoliEghbalzadeh, Hamid

computer scienceinformation retrievalmachine learningdense retrievalknowledge discoverydata sciencesequential learningretrieval techniquesequential recommendation

Looped Transformers Are Better at Learning Learning Algorithms. arXiv (Cornell University) (2023)

Yang, LiuLee, KangwookNowak, RobertPapailiopoulos, Dimitris

machine learninglooped transformers

PATHPROX: A Proximal Gradient Algorithm for Weight Decay Regularized Deep Neural Networks. arXiv (Cornell University) (2022)

Yang, LiuZhang, JifanShenouda, JosephPapailiopoulos, DimitrisLee, KangwookNowak, Robert D.

deep learningdeep neural networksproximal gradient algorithmweight decaymachine learningapplied mathematics

Rare Gems: Finding Lottery Tickets at Initialization. arXiv (Cornell University) (2022)

Sreenivasan, KartikSohn, Jy-yongYang, LiuGrinde, MatthewNagle, AlliotWang, HongyiLee, KangwookPapailiopoulos, Dimitris

deep learningcomputer sciencerare gems

Flow-Based Generative Models for Learning Manifold to Manifold Mappings. Proceedings of the AAAI Conference on Artificial Intelligence (2021)

Xingjian ZhenRudrasis ChakrabortyLiu YangVikas Singh

deep learningcomputer sciencecomputational intelligenceflow-based generative modelsmachine learningmanifold learninggeometric learningmachine intelligenceartificial intelligence

1
2
10 / page

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Similar Faculty Members

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Institution

Florida Atlantic University

design automation

low power cmos circuit design

Feng Liu

Institution

Stevens Institute of Technology

neuroimaging

applied artificial intelligence

Sergey Plis

Institution

Georgia State University

neuroscience

neuroimaging

Hanqi Zhuang

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Florida Atlantic University

marine animal sound detection and classification

deep learning

Anand Rangarajan

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University of Florida

visual diagnosis

medical image computing

Ipek Oguz

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Vanderbilt University

medical imaging

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Khan Iftekharuddin

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Old Dominion University

digital image processing

image processor

Pew-Thian Yap

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University of North Carolina at Chapel Hill

radiology

biomedical imaging

SueYeon Chung

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New York University

neural networks

neuroscience

Yuankai Huo

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biomedical data integration

computational biology