Grants per year
Personal profile
Research Interests
As a computer scientist and statistician, I use computation and data as a lens to explore science and intelligence. To make progress, I examines this with the point of view provided by the twin windows of modern nonparametric method and probabilistic graphical model. My specific research focuses on nonparametric structure learning and representation learning. Success on this research has the potential to revolutionarize the foundation of the second generation of artificial intelligence (i.e., statistical machine learning) and push the frontier of the third generation of artificial intelligence (i.e., deep learning). My applied research interest is to develop a unified set of computational, statistical, and software tools to extract and interpret significant information from the data collected from a variety of scientific areas.
Education/Academic qualification
Machine Learning and Statistics, PhD, Carnegie Mellon University
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Collaborations and top research areas from the last five years
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Accelerator Real-time Edge AI for Distributed Systems (READS)
Fermi Research Alliance, LLC, Fermi National Accelerator Laboratory, Department of Energy
8/2/21 → 12/31/23
Project: Research project
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Developing novel deep-learning based methods for deciphering non-coding gene regulatory code
State University of New York at Stony Brook , National Library of Medicine
8/1/21 → 4/30/25
Project: Research project
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SHIELD: A Statistical Machine Learning Framework for Diversity Enabled Ensemble Robustness
Defense Advanced Research Projects Agency (DARPA)
9/27/19 → 7/31/21
Project: Research project
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Collaborative Research: TRIPODS Institute for Optimization and Learning
1/1/18 → 12/31/22
Project: Research project
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EMS®: A Massive Computational Experiment Management System towards Data-driven Robotics
Lin, Q., Ye, G. & Liu, H., 2023, Proceedings - ICRA 2023: IEEE International Conference on Robotics and Automation. Institute of Electrical and Electronics Engineers Inc., p. 9068-9075 8 p. (Proceedings - IEEE International Conference on Robotics and Automation; vol. 2023-May).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Feature Programming for Multivariate Time Series Prediction
Reneau, A., Hu, J. Y. C., Gilani, A. & Liu, H., 2023, In: Proceedings of Machine Learning Research. 202, p. 29009-29029 21 p.Research output: Contribution to journal › Conference article › peer-review
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Ising-Traffic: Using Ising Machine Learning to Predict Traffic Congestion under Uncertainty
Pan, Z., Sharma, A., Hu, J. Y. C., Liu, Z., Li, A., Liu, H., Huang, M. & Geng, T., Jun 27 2023, AAAI-23 Technical Tracks 8. Williams, B., Chen, Y. & Neville, J. (eds.). AAAI Press, p. 9354-9363 10 p. (Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023; vol. 37).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
4 Scopus citations -
KGML-xDTD: a knowledge graph-based machine learning framework for drug treatment prediction and mechanism description
Ma, C., Zhou, Z., Liu, H. & Koslicki, D., 2023, In: GigaScience. 12, giad057.Research output: Contribution to journal › Article › peer-review
Open Access -
Bregman Proximal Langevin Monte Carlo via Bregman-Moreau Envelopes
Lau, T. T. K. & Liu, H., 2022, In: Proceedings of Machine Learning Research. 162, p. 12049-12077 29 p.Research output: Contribution to journal › Conference article › peer-review
Datasets
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Kernel Meets Sieve: Post-Regularization Confidence Bands for Sparse Additive Model
Lu, J. (Creator), Kolar, M. (Creator) & Liu, H. (Creator), Taylor & Francis, 2019
DOI: 10.6084/m9.figshare.10274576.v1, https://tandf.figshare.com/articles/online_resource/Kernel_Meets_Sieve_Post-Regularization_Confidence_Bands_for_Sparse_Additive_Model/10274576/1
Dataset
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Covariance-based sample selection for heterogeneous data: Applications to gene expression and autism risk gene detection
Lin, K. Z. (Creator), Liu, H. (Creator) & Roeder, K. (Creator), Taylor & Francis, 2020
DOI: 10.6084/m9.figshare.11944581.v1, https://tandf.figshare.com/articles/online_resource/Covariance-based_sample_selection_for_heterogeneous_data_Applications_to_gene_expression_and_autism_risk_gene_detection/11944581/1
Dataset
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Patterns and rates of exonic de novo mutations in autism spectrum disorders.
Campbell, N. G. (Creator), Samocha, K. (Creator), Kou, Y. (Creator), Liu, L. (Creator), Crawford, E. L. (Creator), Geller, E. (Creator), Lin, C. (Creator), Ma'ayan, A. (Creator), Neale, B. (Creator), Sabo, A. (Creator), Schafer, C. (Creator), Stevens, C. (Creator), Valladares, O. (Creator), Wang, L. (Creator), Daly, M. J. (Creator), Polak, P. P. (Creator), Wang, L. (Creator), Sato, A. (Contributor), Liu, H. (Creator), Cai, G. (Creator), Lin, C. (Creator), Stevens, C. (Creator), Makarov, V. (Creator), Yoon, S. (Creator), Maguire, J. (Creator), Liu, H. (Creator), Zhao, T. (Creator), Lihm, J. (Creator), Dannenfelser, R. (Creator), Jabado, O. (Creator), Peralta, Z. (Creator), Yoon, S. (Creator), Lau, L. (Contributor), Liu, H. (Creator), Wang, L. (Creator), Makarov, V. (Creator), Wang, L. (Creator) & Lin, C. (Creator), NIMH Data Archive, 2012
DOI: 10.15154/1163544, https://nda.nih.gov/study.html?id=317
Dataset
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Positive Semidefinite Rank-Based Correlation Matrix Estimation With Application to Semiparametric Graph Estimation
Liu, H. (Creator), Zhao, T. (Creator) & Roeder, K. (Creator), Taylor & Francis, 2014
DOI: 10.6084/m9.figshare.1209702.v3, https://tandf.figshare.com/articles/dataset/Positive_Semidefinite_Rank_Based_Correlation_Matrix_Estimation_With_Application_to_Semiparametric_Graph_Estimation/1209702/3
Dataset
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Inter-Subject Analysis: A Partial Gaussian Graphical Model Approach
Ma, C. (Creator), Lu, J. (Creator) & Liu, H. (Creator), Taylor & Francis, 2020
DOI: 10.6084/m9.figshare.13157833.v2, https://tandf.figshare.com/articles/online_resource/Inter-Subject_Analysis_A_Partial_Gaussian_Graphical_Model_Approach/13157833/2
Dataset