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.
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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NSF-Simons AI Institute for the Sky (SkAI Institute)
Kalogera, V. (PD/PI), Katsaggelos, A. K. (Co-PD/PI), Alexander, E. (Other), Faucher-Giguere, C.-A. (Other), Fong, W.-F. (Other), Hullman, J. R. (Other), Kokkori, M. (Other), Liu, H. (Other), Miller, A. A. (Other), Rasio, F. A. (Other), Samia, N. I. (Other), Starkenburg, T. (Other), Strom, A. (Other), Tchekhovskoy, S. (Other), Vijayaraghavan, A. (Other), Wei, E. (Other) & Zissimopoulos, K. (Other)
10/1/24 → 9/30/29
Project: Research project
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AI for the Sky - The SkAI Institute
Kalogera, V. (PD/PI), Katsaggelos, A. K. (Co-Investigator), Alexander, E. (Other), Faucher-Giguere, C.-A. (Other), Fong, W.-F. (Other), Hullman, J. R. (Other), Kokkori, M. (Other), Liu, H. (Other), Miller, A. A. (Other), Rasio, F. A. (Other), Samia, N. I. (Other), Starkenburg, T. (Other), Strom, A. (Other), Tchekhovskoy, S. (Other), Vijayaraghavan, A. (Other) & Wei, E. (Other)
10/1/24 → 9/30/29
Project: Research project
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Accelerator Real-time Edge AI for Distributed Systems (READS)
Ogrenci, S. (PD/PI), Ogrenci, S. (PD/PI), Liu, H. (Co-PD/PI) & Liu, H. (Co-PD/PI)
Fermi Research Alliance, LLC, Fermi National Accelerator Laboratory, Department of Energy
8/2/21 → 8/31/24
Project: Research project
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Developing novel deep-learning based methods for deciphering non-coding gene regulatory code
Liu, H. (PD/PI) & Liu, H. (PD/PI)
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
Liu, H. (PD/PI)
Defense Advanced Research Projects Agency (DARPA)
9/27/19 → 7/31/21
Project: Research project
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A community effort to optimize sequence-based deep learning models of gene regulation
Random Promoter DREAM Challenge Consortium, 2024, (Accepted/In press) In: Nature biotechnology.Research output: Contribution to journal › Article › peer-review
Open Access1 Scopus citations -
BiSHop: Bi-Directional Cellular Learning for Tabular Data with Generalized Sparse Modern Hopfield Model
Xu, C., Huang, Y. C., Hu, J. Y. C., Li, W., Gilani, A., Goan, H. S. & Liu, H., 2024, In: Proceedings of Machine Learning Research. 235, p. 55048-55075 28 p.Research output: Contribution to journal › Conference article › peer-review
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DNABERT-2: EFFICIENT FOUNDATION MODEL AND BENCHMARK FOR MULTI-SPECIES GENOMES
Zhou, Z., Ji, Y., Li, W., Dutta, P., Davuluri, R. V. & Liu, H., 2024.Research output: Contribution to conference › Paper › peer-review
13 Scopus citations -
DOS®: A Deployment Operating System for Robots
Ye, G., Lin, Q., Luo, Z. & Liu, H., 2024, 2024 IEEE International Conference on Robotics and Automation, ICRA 2024. Institute of Electrical and Electronics Engineers Inc., p. 14086-14092 7 p. (Proceedings - IEEE International Conference on Robotics and Automation).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Efficient Action Robust Reinforcement Learning with Probabilistic Policy Execution Uncertainty
Liu, G., Zhou, Z., Liu, H. & Lai, L., 2024, In: Transactions on Machine Learning Research. 2024Research output: Contribution to journal › Article › peer-review
Datasets
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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
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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.v4, https://tandf.figshare.com/articles/dataset/Covariance-based_sample_selection_for_heterogeneous_data_Applications_to_gene_expression_and_autism_risk_gene_detection/11944581/4
Dataset
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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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Patterns and rates of exonic de novo mutations in autism spectrum disorders.
Liu, L. (Creator), Sabo, A. (Creator), Neale, B. M. (Creator), Stevens, C. (Creator), Makarov, V. (Creator), Maguire, J. (Creator), Samocha, K. E. (Creator), Daly, M. J. (Creator), Crawford, E. L. (Creator), Liu, H.-K. (Creator), Kou, Y. (Creator), Campbell, N. G. (Creator), Geller, E. (Creator), Lin, C.-F. (Creator), Ma'ayan, A. (Creator), Schafer, C. (Creator), Valladares, O. (Creator), Wang, L.-S. (Creator), Polak, P. (Creator), Yoon, S. (Creator), Zhao, T. (Creator), Cai, G. (Creator), Lihm, J. (Creator), Dannenfelser, R. (Creator), Jabado, O. (Creator) & Peralta, Z. (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