World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
46
Citations
14157
World Ranking
6685
National Ranking
2951

Mathematics

D-Index
44
Citations
11664
World Ranking
1549
National Ranking
668

Overview

Han Liu is a researcher affiliated with Northwestern University in the United States, specializing primarily in the field of Computer Science with a focus on Artificial Intelligence. Their scholarly contributions span multiple subfields, including Computer Vision and Pattern Recognition, Signal Processing, Radiology, Nuclear Medicine and Imaging, and Information Systems.

Their research topics cover a variety of areas related to machine intelligence and data analysis. These include:

  • Topic Modeling
  • Machine Learning and Data Classification
  • Advanced Neural Network Applications
  • Anomaly Detection Techniques and Applications
  • Natural Language Processing Techniques
  • Machine Learning and Algorithms
  • Domain Adaptation and Few-Shot Learning

Han Liu has published numerous papers in well-recognized venues. Notable recent publications include:

  • "Deep Learning Based Fusion Approach for Hate Speech Detection," 2020, IEEE Access
  • "A survey on epistemic (model) uncertainty in supervised learning: Recent advances and applications," 2021, Neurocomputing
  • "Human posture recognition based on multiple features and rule learning," 2020, International Journal of Machine Learning and Cybernetics
  • "Multi-Feature Input Deep Forest for EEG-Based Emotion Recognition," 2021, Frontiers in Neurorobotics
  • "Feature Selection Using Enhanced Particle Swarm Optimisation for Classification Models," 2021, Sensors

Their work has appeared frequently in journals and conferences such as arXiv, the International Journal of Machine Learning and Cybernetics, Sensors, the Proceedings of the AAAI Conference on Artificial Intelligence, and IEEE Access. Among these venues, arXiv has been the platform for 24 of their publications.

Collaborations with other researchers form an important component of Han Liu's work. Frequent co-authors include İpek Oğuz, Dewei Hu, Benoît M. Dawant, Qin Zhang, and Xizhao Wang, with collaborative counts ranging from three to eight papers.

Best Publications

  • Patterns and rates of exonic de novo mutations in autism spectrum disorders

    Benjamin M. Neale;Yan Kou;Li Liu;Avi Ma'Ayan

  • Challenges of Big Data analysis

    Jianqing Fan;Fang Han;Han Liu

  • The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs

    Han Liu;John Lafferty;Larry Wasserman

  • Sparse Additive Models

    Pradeep Ravikumar;John Lafferty;Han Liu;Larry Wasserman

  • High Dimensional Semiparametric Gaussian Copula Graphical Models.

    Han Liu;Fang Han;Ming Yuan;John D. Lafferty

  • The huge package for high-dimensional undirected graph estimation in R

    Tuo Zhao;Han Liu;Kathryn Roeder;John Lafferty

  • Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models

    Han Liu;Kathryn Roeder;Larry Wasserman

  • An overview of the estimation of large covariance and precision matrices

    Jianqing Fan;Yuan Liao;Han Liu

  • Fully decentralized multi-agent reinforcement learning with networked agents

    Kaiqing Zhang;Zhuoran Yang;Han Liu;Tong Zhang

  • A general theory of hypothesis tests and confidence regions for sparse high dimensional models

    Yang Ning;Yang Ning;Han Liu

  • Blockwise coordinate descent procedures for the multi-task lasso, with applications to neural semantic basis discovery

    Han Liu;Mark Palatucci;Jian Zhang

  • Stochastic compositional gradient descent: algorithms for minimizing compositions of expected-value functions

    Mengdi Wang;Ethan X. Fang;Han Liu

  • OPTIMAL COMPUTATIONAL AND STATISTICAL RATES OF CONVERGENCE FOR SPARSE NONCONVEX LEARNING PROBLEMS.

    Zhaoran Wang;Han Liu;Tong Zhang

  • A STRICTLY CONTRACTIVE PEACEMAN-RACHFORD SPLITTING METHOD FOR CONVEX PROGRAMMING.

    Bingsheng He;Han Liu;Zhaoran Wang;Xiaoming Yuan

  • DISTRIBUTED TESTING AND ESTIMATION UNDER SPARSE HIGH DIMENSIONAL MODELS.

    Heather Battey;Heather Battey;Jianqing Fan;Jianqing Fan;Han Liu;Junwei Lu

  • SpAM: Sparse Additive Models

    Han Liu;Larry Wasserman;John D. Lafferty;Pradeep K. Ravikumar

  • Automated diagnoses of attention deficit hyperactive disorder using magnetic resonance imaging.

    Ani Eloyan;John Muschelli;John Muschelli;Mary Beth Nebel;Han Liu

  • A nonconvex optimization framework for low rank matrix estimation

    Tuo Zhao;Zhaoran Wang;Han Liu

  • Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space

    Jiechao Xiong;Qing Wang;Zhuoran Yang;Peng Sun

  • A PARTIALLY LINEAR FRAMEWORK FOR MASSIVE HETEROGENEOUS DATA.

    Tianqi Zhao;Guang Cheng;Han Liu

  • An Overview on the Estimation of Large Covariance and Precision Matrices

    Jianqing Fan;Yuan Liao;Han Liu

  • The Nonparanormal SKEPTIC

    Han Liu;Fang Han;Ming Yuan;Larry Wasserman

Frequent Co-Authors

Larry Wasserman
Larry Wasserman Carnegie Mellon University
Tong Zhang
Tong Zhang University of Illinois at Urbana-Champaign
John Lafferty
John Lafferty Yale University
Jianqing Fan
Jianqing Fan Princeton University
Quanquan Gu
Quanquan Gu University of California, Los Angeles
Kathryn Roeder
Kathryn Roeder Carnegie Mellon University
Xi Chen
Xi Chen Columbia University
Tok Wang Ling
Tok Wang Ling National University of Singapore
Ming Yuan
Ming Yuan Columbia University
Ji Liu
Ji Liu Facebook (United States)

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