HONGFU LIU


Michtom School of Computer Science, Brandeis University                                                        hongfuliu@brandeis.edu
I am currently a tenure-track assistant professor in Michtom School of Computer Science at Brandeis University. I received my Ph.D. in Department of Electrical & Computer Engineering, Northeastern University (NEU), supervised by Prof. Yun (Raymond) Fu. Before joining NEU, I got my master and bachelor degrees majored in management at Beihang University with Prof. Junjie Wu. Here is my latest CV.

EDUCATION
PhD, in College of Engineering, Northeastern University, 2018
Master, in Management Science and Engineering, Beihang University, 2014
Bachelor, in Information Systems, Beihang University, 2011
Minor Bachelor, in Applied Mathematics, Beihang University, 2011
Minor Bachelor, in Law, Beihang University, 2011

INTERN
Microsoft Research Asia, 06/2017 - 08/2017
Adobe Research, 05/2016 - 07/2016

INTERESTS
Cluster Analysis: consensus clustering, constrained clustering, balanced clustering, multi-view clustering, interpretable clustering, big data clustering, fair clustering
Outlier Detection: multi-view outlier detection, spammer detection, zombie user detection, bi-sampling outlier detection, jointly clustering and outlier detection
Transfer Learning: heterogenous domain adaptation, unsupervised domain adaptation, structure-preserved domain adaptation
Feature Selection: unsupervised feature selection, multi-view feature selection
HIRING
Ph.D. students, PostDocs, Visiting Scholars, and Research Associates to work on machine learning, data mining, computer vision, business intelligence, and social media analytics are welcome.
INVITED TALKS
I am inviting researchers from adademia and industry to Brandeis for talks.
APPOITMENT
For face-to-face online visitors, please make an appointment by email.

NEWS

2024/1
One ICLR oral accepted. Congrats to Peizhao~
2023/11
Congrats to Peizhao's NIJ Fellowship~
2023/11
Congrats to Peizhao's graduation~
2023/9
Welcome my new student Ziao Yang.
2023/8
One TPMAI accepted. Congrats to Han~





Associate Editor
IEEE Computational Intelligence Magazine (IEEE CIM)
Area Chair
Neural Information Processing Systems (NeurIPS)
International Conference on Learning Representations (ICLR)
International Conference on Machine Learning (ICML)

Google Scholar
Codes
Han Yue, Jundong Li, and Hongfu Liu
Second-Order Unsupervised Feature Selection via Knowledge Contrastive Distillation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.
Hongfu Liu, Junxiang Chen, Jennifer Dy and Yun Fu
Transforming Complex Problems into K-means Solutions. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI-Survey), 2023.
Han Yue, Pengyu Hong and Hongfu Liu
Graph-Graph Similarity Network. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022.
Mingyu Wan, Han Yue; Jaime Notarangelo, Hongfu Liu and Fanglin Che
Deep Learning Assisted Investigation of Electric Field-Dipole Effects on Catalytic Ammonia Synthesis.JACS Au, 2022.
Wenguang Wang, Wenhong Wang and Hongfu Liu
Correlation Guided Ensemble Clustering for Hyperspectral Band Selection.Remote Sensing (RS), 2022.
Zihao Wang, Kun Li, Steve Xia and Hongfu Liu
Economic Recession Prediction Using Deep Neural Network.The Journal of Financial Data Science (JFDS), 2021.
Han Yue, Hongfu Liu and Jian Chen
A Gospel for MOBA Game: Ranking-Preserved Hero Change Prediction in Dota 2.IEEE Transactions on Games (TC), 2021.
Kai Li, Hongfu Liu, Yulun Zhang, Kunpeng Li and Yun Fu
Self-guided Deep Multi-view Subspace Clustering via Consensus Affinity Regularization.IEEE Transactions on Cybernetics (TC), 2021.
Hangyu Du, Feng Chen, Hongfu Liu and Pengyu Hong
Network-based Virus-Host Interaction Prediction with Application to SARS-CoV-2.Patterns, 2021.
Xiaochen Lv, Wenhong Wang and Hongfu Liu
Cluster-Wise Weighted NMF for Hyperspectral Images Unmixing with Imbalanced Data.Remote Sensing (RS), 2021.
Jun Li, Hongfu Liu, Zhiqiang Tao, Handong Zhao and Yun Fu
Learnable Subspace Clustering. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2020.
Wenhong Wang and Hongfu Liu
Deep Nonnegative Dictionary Factorization for Hyperspectral Unmixing. Remote Sensing (RS), 2020.
Wenhong Wang, Yuntao Qian and Hongfu Liu
Multiple Clustering Guided Nonnegative Matrix Factorization for Hyperspectral Unmixing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS), 2020.
Xue Li, Hongfu Liu, and Bin Zhu
Evolutive Preference Analysis with Online Consumer Ratings. Information Sciences (IS), 2020.
Hongfu Liu, Jun Li, Yue Wu and Yun Fu
Clustering with Outlier Removal. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2019.
Zhiqiang Tao, Hongfu Liu, Huazhu Fu and Yun Fu
Multi-View Saliency-Guided Clustering for Image Cosegmentation. IEEE Transactions on Image Processing (TIP), 2019.
Weiren Yu, Zhengming Ding, Chunming Hu and Hongfu Liu
Knowledge ReusedOutlier Detection. IEEE Access, 2019.
Zhiqiang Tao, Hongfu Liu, Sheng Li, Zhengming Ding and Yun Fu
Marginalized Multi-View Ensemble Clustering. IEEE Transactions on Neural Networks and Learn-ing Systems (TNNLS), 2019.
Deqing Wang, Chenwei Lu, Junjie Wu, Hongfu Liu, Wenjie Zhang and Fuzhen Zhuang
Softly Associative Transfer Learning for Cross-domain Classification. IEEE Transactions on Cybernetics (TC), 2019.
Hongfu Liu, Ming Shao and Yun Fu
Feature Selection with Unsupervised Consensus Guidance. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2018.
Zhiqiang Tao, Hongfu Liu, Sheng Li, Zhengming Ding and Yun Fu
Robust Spectral Ensemble Clustering via Rank Minimization. ACM Transactions on Knowledge Discovery from Data (TKDD), 2018.
Yue Wu, Hongfu Liu, Jun Li and Yun Fu
Improving Face Representation Learning with Center Invariant Loss. Image and Vision Computing (IVC), 2018.
Hongfu Liu, Ming Shao, Zhengming Ding and Yun Fu
Structure-Preserved Unsupervised Domain Adaptation. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2018.
Joseph Robinson, Ming Shao, Yue Wu, Hongfu Liu, Timothy Gillis and Yun Fu
Visual Kinship Recognition of Families in the Wild. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2018.
Hongfu Liu and Yun Fu
Consensus Guided Multi-View Clustering. ACM Transactions on Knowledge Discovery from Data (TKDD), 2018.
Xue Li and Hongfu Liu
Greedy Optimization for K-means-based Consensus Clustering. Tsinghua Science and Technology (TST), 2018.
Hongfu Liu, Zhiqiang Tao and Yun Fu
Partition Level Constrained Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2017.
Hongfu Liu, Ming Shao, Sheng Li and Yun Fu
Infinite Ensemble Clustering. Data Mining and Knowledge Discovery (DMKD), 2017.
Hongfu Liu, Junjie Wu, Tongliang Liu, Dacheng Tao and Yun Fu
Spectral Ensemble Clustering via Weighted K-means: Theoretical and Practical Evidence. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2017.
Hongfu Liu, Rui Zhao, Hongsheng Fang, Feixiong Chen, Yun Fu and Yuyang Liu
Entrpoy-based Consensus Clustering for Patient Stratfcation. Bioinformatics (BIOINF), 2017.
Handong Zhao, Hongfu Liu, Zhengming Ding and Yun Fu
Consensus Regularized Multi-View Outlier Detection. IEEE Transactions on Image Processing (TIP), 2017.
Junjie Wu, Ziang Wu, Jie Cao, Hongfu Liu, Guoqing Chen and Yanchuan Zhang
Fuzzy Consensus Clustering with Applications on Big Data. IEEE Transactions on Fuzzy Systems (TFS), 2017.
Junjie Wu, Hongfu Liu, Hui Xiong, Jie Cao and Jian Chen
K-means-based Con-sensus Clustering: A Unifeded View. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2015.
Junjie Wu, Shiwei Zhu, Hongfu Liu and Guoping Xia
Cosine Interesting Pattern Discovery. Information Sciences (IS), 2012.
Anshuman Chhabra, Peizhao Li, Prasant Mohapatra, and Hongfu Liu
“What Data Benefits My Classifier?” Enhancing Model Performance and Interpretability through Influence-Based Data Selection. International Conference on Learning Representations (ICLR), 2024.
Zizhang Chen, Peizhao Li, Hongfu Liu and Pengyu Hong
Characterizing the Influence of Graph Elements. International Conference on Learning Representations (ICLR), 2023.
Anshuman Chhabra, Peizhao Li, Prasant Mohapatra and Hongfu Liu
Robust Fair Clustering: A Novel Fairness Attack and Defense Framework. International Conference on Learning Representations (ICLR), 2023.
Haiyi Mao, Hongfu Liu, Jason Xiaotian Dou, and Panayiotis V. Benos.
Towards Cross-Modal Causal Structure and Representation Learning. Machine Learning for Health Workshop, 2022.
Chunhui Zhang, Hongfu Liu, Jundong Li, Yanfang Ye and Chuxu Zhang
Contrastive Graph Few-Shot Learning. NeurIPS GLFrontiers Workshop, 2022.
Han Yue, Chunhui Zhang, Chuxu Zhang and Hongfu Liu
Label-invariant Augmentation for Semi-Supervised Graph Classification. Neural Information Processing Systems (NeurIPS), 2022.
Han Yue, Steve Xia and Hongfu Liu
Multi-task Envisioning Transformer-based Autoencoder for Corporate Credit Rating Migration Early Prediction. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2021.
Peizhao Li and Hongfu Liu
Achieving Fairness at No Utility Cost via Data Reweighing. International Conference on Machine Learning (ICML), 2022.
Peizhao Li, Pu Wang, Karl Berntorp and Hongfu Liu
Exploiting Temporal Relations on Radar Perception for Autonomous Driving. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
Songyao Jiang, Hongfu Liu, Yue Wu and Yun Fu
Spatially Constrained GAN for Face and Fashion Synthesis. IEEE International Conference on Automatic Face and Gesture Recognition (FG), 2021.
Wenxiao Xiao, Zhengming Ding and Hongfu Liu
Implicit Semantic Response Alignment for Partial Domain Adaptation. Neural Information Processing Systems (NeurIPS), 2021.
Xiaoying Xing, Hongfu Liu, Chen Chen and Jundong Li
Fairness-Aware Unsupervised Feature Selection. Conference on Information and Knowledge Management (CIKM), 2021.
Taotao Jing, Hongfu Liu and Zhengming Ding
Towards Novel Target Discovery Through Open-Set Domain Adaptation. International Conference on Computer Vision (ICCV), 2021.
Hanyu Du, Peizhao Li and Hongfu Liu
Deep Clustering-based Fair Outlier Detection. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2021.
Peizhao Li, Jiuxiang Gu, Jason Kuen, Vlad Morariu, Handong Zhao, Rajiv Jain, Varun Manjunatha and Hongfu Liu
SelfDoc: Self-Supervised Document Representation Learning. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
Peizhao Li, Han Zhao and Hongfu Liu
On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections. International Conference on Learning Representations (ICLR), 2021.
Peizhao Li, Han Zhao and Hongfu Liu
Deep Fair Clustering for Visual Learning. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
Zhiqiang Tao, Hongfu Liu, Jun Li and Yun Fu
Adversarial Graph Embedding for Ensemble Clustering. International Joint Conference on Artifcialcial Intelligence (IJCAI), 2019.
Zhengming Ding and Hongfu Liu
Marginalized Latent Semantic Encoder for Zero-Shot Learning. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
Haitao Xiong, Hongfu Liu, Bineng Zhong and Yun Fu
Structured and Sparse Annotations for Image Emotion Distribution Learning. AAAI Conference on Artficial Intelligence (AAAI), 2019.
Hongfu Liu, Ziming Huang, Yun Fu, Qi Chen, Mingqin Li, and Lintao Zhang
Fast Clustering with Flexible Balance Constraints. IEEE International Conference on Big Data (IEEE BigData), 2018.
Jun Li, Hongfu Liu and Yun Fu
Predictive Coding Machine for Compressed Sensing and Image Denoising. AAAI Conference on Artficial Intelligence (AAAI), 2018.
Yue Wu, Zhengming Ding, Hongfu Liu, Joseph Robinson and Yun Fu
Kinship Classification through Latent Adaptive Subspace. IEEE Conference on Automatic Face and Gesture Recognition (FG), 2018.
Jun Li, Hongfu Liu, Handong Zhao and Yun Fu
Projective Low-rank Subspace Clustering via Learning Deep Encoder. International Joint Conference on Artifcialcial Intelligence (IJCAI), 2017.
Zhiqiang Tao, Hongfu Liu, Sheng Li, Zhengming Ding and Yun Fu
From Ensemble Clustering to Multi-View Clustering. International Joint Conference on Artifcialcial Intelligence (IJCAI), 2017.
Zhiqiang Tao*, Hongfu Liu*, Huazhu Fu and Yun Fu (* means equal contribution)
Image Cosegmentation via Saliency-Guided Constraint Clustering with Cosine Similarity. AAAI Conference on Artficial Intelligence (AAAI), 2017.
Zhiqiang Tao, Hongfu Liu and Yun Fu
Simultaneous Clustering and Ensemble. AAAI Conference on Artficial Intelligence (AAAI), 2017.
Sheng Li, Hongfu Liu, Zhiqiang Tao and Yun Fu
Multi-View Graph Learning with Adaptive Label Propagation. IEEE International Conference on Big Data (IEEE BigData), 2017.
Yue Wu, Hongfu Liu, Yun Fu
Low-shot Face Recognition with Hybrid Classifiers. International Conference on Computer Vision Workshop (ICCV-W), 2017.
Yue Wu, Hongfu Liu, Jun Li and Yun Fu
Deep Face Recognition with Center Invariant Loss. ACM Multimedia Thematic Workshop (MM-W), 2017.
Hongfu Liu, Ming Shao, Sheng Li and Yun Fu
Inifnite Ensemble for Image Clustering. ACM SIGKDD international conference on Knowledge Discovery and Data mining (KDD), 2016.
Hongfu Liu, Haiyi Mao and Yun Fu
Robust Multi-View Feature Selection. IEEE International Conference on Data Mining (ICDM), 2016.
Hongfu Liu, Ming Shao and Yun Fu
Structure-Preserved Multi-Source Domain Adaptation. IEEE International Conference on Data Mining (ICDM), 2016.
Hongfu Liu, Yuchao Zhang, Bo Deng and Yun Fu
Bi-sampling Ensemble Outlier Detection. IEEE International Conference on Big Data (IEEE BigData), 2016.
Zhiqiang Tao, Hongfu Liu, Sheng Li and Yun Fu
Robust Spectral Ensemble Clustering. ACM International Conference on Information and Knowledge Management (CIKM), 2016.
Handong Zhao, Hongfu Liu and Yun Fu
Incomplete Multi-modal Visual Data Grouping. International Joint Conference on Artifcialcial Intelligence (IJCAI), 2016.
Hongfu Liu, Ming Shao and Yun Fu
Consensus Guided Unsupervised Feature Selection. AAAI Conference on Artficial Intelligence (AAAI), 2016.
Hongfu Liu*, Tongliang Liu*, JunjieWu, Dacheng Tao and Yun Fu (* means equal contribution)
Spectral Ensemble Clustering. ACM SIGKDD international conference on Knowledge Discovery and Data mining (KDD), 2015.
Hongfu Liu and Yun Fu
Clustering with Partition Level Side Information. IEEE International Conference on Data Mining (ICDM), 2015.
Hongfu Liu, JunjieWu, Dacheng Tao, Yuchao Zhang and Yun Fu
DIAS: A Disassemble-Assemble Framework for Highly Sparse Text Clustering. SIAM International Conference on Data Mining (SDM), 2015.
Hongfu Liu, Cheng Gong and Junjie Wu
Consensus Clustering on Big Data. IEEE International Conference on Service Systems and Service Management (ICSSSM Best Paper), 2015.
Hongfu Liu,Yuchao Zhang,Hao Lin and Junjie Wu
How Many Zombies Around You. IEEE International Conference on Data Mining (ICDM), 2013.
Junjie Wu, Hongfu Liu, Hui Xiong and Jie Cao
Theoretic Framework of K-means-based Consensus Clustering. International Joint Conference on Artifcialcial Intelligence (IJCAI), 2013.
Yaqiong Wang, Hongfu Liu, Hao Lin, Junjie Wu, Zhiang Wu and Jie Cao
SEA: A System for Event Analysis on Chinese Tweets. ACM SIGKDD international conference on Knowledge Discovery and Data mining (KDD), 2013.
Yuchao Zhang and Hongfu Liu
A Reliable QoE-aware Framework for Cloud Service Monitoring and Ranking. International Conference on Electrical and Information Technologies for Rail Transportation (EITRT), 2013.
Yuchao Zhang, Hongfu Liu and Bo Deng
SLA-Driven State Monitoring for Cloud Services. IEEE International Conference on High Performance Computing and Communications (HPCC), 2013.
Yuchao Zhang, Hongfu Liu and Bo Deng
Evolutionary Clustering with DBSCAN. International Conference on Natural Computation (ICNC), 2013.
Di Zhao, Hongfu Liu, Hongyi Li
A Method Based on Grey Correlation Clustering for Improving the Hierarchy of Analytic Hierarchy Process. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010.

Top 100 Young Chinese Scholars (Data Mining Area), 2022
Baidu Talent
Highlighted Area Chair, 2022
International Conference on Learning Representations
Best Student Paper, 2021
Automatic Face and Gesture Recognition
Outstanding Self-Financed Students Abroad, 2018
Chinese Government Award
First Place Award in MS-Celel-1M Grand Challenge, 2017
International Conference on Computer Vision
IJCAI Student Travel Award, 2017
International Joint Conference on Artifcial Intelligence
KDD Student Travel Award, 2016
ACM SIGKDD Conference on Knowledge Discovery and Data Mining
SIGIR Student Travel Award, 2016
ACM International Conference Information and Knowledge Management
ICDM Student Travel Award, 2016
IEEE Conference on Data Mining
AAAI Student Travel Award, 2016
AAAI Conference on Artificial Intelligence
IEEE BigData Student Travel Award, 2016
IEEE Conference on Big Data
Top 20 New Star in Data Mining, 2016
Microsoft Academic Search
The inaugural Adobe Research Fellowship Finalist, 2016
Adobe Research
ICDM Student Travel Award, 2015
IEEE Conference on Data Mining
Dean Fellowship, 2014
Northeastern University
Outstanding Graduate, 2014
Beijing Education Commission
Outstanding Graduate Thesis, 2014
Beihang University
National Scholarship, 2013
Ministry of Education of China
Top 10 Graduate, 2013
School of Economics and Management in Beihang University
Data Mining Innovation Award, 2013
China Internet Network Information Center
Airbus Scholarship, 2012
Beihang University
Outstanding Volunteer Prize, 2012
School of Economics and Management in Beihang University
Outstanding Undergraduate, 2011
Beijing Education Commission
Outstanding Undergraduate Thesis, 2011
Beihang University
Board Scholarships, 2011
School of Economics and Management in Beihang University
Google Student Travel Award, 2010
Student Symposium on Machine Learning and Applications
Citigroup Scholarship, 2010
Beihang University
Kwang-Hua Scholarship, 2009
Beihang University
Samsung Scholarship, 2008
Beihang University

Visting Scholar
PhD Students
Wenxiao Xiao 9/2020-now
Han Yue 9/2019-now
Peizhao Li 9/2019-now
Alumni
Hangyu Du 3/2020-5/2021 (Brown, Master)
Feng Chen 3/2020-5/2021 (Harvard Medical School, Master)
Hanyu Song 9/2020-5/2021 (Salesforce)
Elizabeth Fong 9/2020-12/2020
Kun Li 9/2018-6/2020
Zihao Wang 9/2019-6/2020
Sibo Zhu 9/2018-12/2019 (Univeristy of Toronto, PhD)
Runjie Lu 9/2018-6/2019 (Wayfair)
Hongwen Wang 3/2019-2/2020
Yufan Song 7/2019-2/2020 (CMU, Master)
Chenning Yu 7/2019-12/2019 (UCSD, PhD)
Haochen Wang 7/2019-8/2019
Yanjing Li 7/2019-8/2019
Sponsors
Adobe
Brandeis Univeristy
Department of Energy
MERL
Nvidia
Google
Guardian



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