Assistant Professor
National University of Singapore

E-mail: bhooi@comp.nus.edu.sg

I'm an Assistant Professor in the School of Computing and the Institute of Data Science in National University of Singapore. My research interests include scalable machine learning, deep learning, graph algorithms, anomaly detection, and biomedical applications of AI.

I am actively recruiting postdoctoral fellows as well as NUS students of all levels interested in the above topics. Please email me if you are interested.

I received my Ph.D. in Machine Learning from Carnegie Mellon University, where I was advised by Christos Faloutsos. I received my M.S. in Computer Science and my B.S. in Mathematics from Stanford University.

2023

• Miao Xiong, Ailin Deng, Pang Wei Koh, Jiaying Wu, Shen Li, Jianqing Xu, Bryan Hooi. Proximity-Informed Calibration for Deep Neural Networks, Neural Information Processing Systems (NeurIPS) 2023 (Spotlight).
• Yifan Zhang, Daquan Zhou, Bryan Hooi, Kai Wang, Jiashi Feng. Expanding Small-Scale Datasets with Guided Imagination, Neural Information Processing Systems (NeurIPS) 2023.
• Yiwei Wang, Yujun Cai, Muhao Chen, Yuxuan Liang, Bryan Hooi. Primacy Effect of ChatGPT, Conference on Empirical Methods in Natural Language Processing (EMNLP) 2023.
• Yiwei Wang, Bryan Hooi, Fei Wang, Yujun Cai, Yuxuan Liang, Wenxuan Zhou, Jing Tang, Manjuan Duan, Muhao Chen. How Fragile is Relation Extraction under Entity Replacements?, SIGNLL Conference on Computational Natural Language Learning (CoNLL) 2023.
• Xu Liu, Yutong Xia, Yuxuan Liang, Junfeng Hu, Yiwei Wang, Lei Bai, Chao Huang, Zhenguang Liu, Bryan Hooi, Roger Zimmermann. LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting, Neural Information Processing Systems (NeurIPS) 2023 (Datasets and Benchmarks Track).
• Baixiang Huang, Bryan Hooi and Kai Shu. TAP: A Comprehensive Data Repository for Traffic Accident Prediction in Road Networks, ACM International Conference on Advances in Geographic Information Systems (SIGSPATIAL) 2023.
• Jiaying Wu, Shen Li, Ailin Deng, Miao Xiong, Bryan Hooi. Prompt-and-Align: Prompt-Based Social Alignment for Few-Shot Fake News Detection, ACM Conference on Information and Knowledge Management (CIKM) 2023.
• Zhiyuan Hu, Yue Feng, Anh Tuan Luu, Bryan Hooi, Aldo Lipani. Unlocking the Potential of User Feedback: Leveraging Large Language Model as User Simulators to Enhance Dialogue System, ACM Conference on Information and Knowledge Management (CIKM) 2023 (Short Paper).
• Jiaying Wu, Bryan Hooi. DECOR: Degree-Corrected Social Graph Refinement for Fake News Detection, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2023.
• Siddharth Bhatia, Mohit Wadhwa, Kenji Kawaguchi, Neil Shah, Philip S. Yu, Bryan Hooi. Sketch-Based Anomaly Detection in Streaming Graphs, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2023.
• Shumin Deng, Shengyu Mao, Ningyu Zhang, Bryan Hooi. SPEECH: Structured Prediction with Energy-Based Event-Centric Hyperspheres, Annual Meeting of the Association for Computational Linguistics (ACL) 2023.
• Ailin Deng, Miao Xiong, Bryan Hooi. Great Models Think Alike: Improving Model Reliability via Inter-Model Latent Agreement, International Conference on Machine Learning (ICML) 2023.
• Xiaoxin He, Bryan Hooi, Thomas Laurent, Adam Perold, Yann LeCun, Xavier Bresson. A Generalization of ViT/MLP-Mixer to Graphs, International Conference on Machine Learning (ICML) 2023.
• Kaixin Wang, Kuangqi Zhou, Jiashi Feng, Bryan Hooi, Xinchao Wang. Reachability-Aware Laplacian Representation in Reinforcement Learning, International Conference on Machine Learning (ICML) 2023.
• Yuwen Li, Bryan Hooi, Miao Xiong. GraphCleaner: Detecting Mislabelled Samples in Popular Graph Learning Benchmarks, International Conference on Machine Learning (ICML) 2023.
• Mingqi Yang, Wenjie Feng, Yanming Shen, Bryan Hooi. Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering, International Conference on Machine Learning (ICML) 2023.
• Yifan Zhang, Bingyi Kang, Bryan Hooi, Shuicheng Yan, Jiashi Feng Deep Long-Tailed Learning: A Survey, Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2023.
• Jianqing Xu, Shen Li, Ailin Deng, Miao Xiong, Jiaying Wu, Jiaxiang Wu, Shouhong Ding, Bryan Hooi Probabilistic Knowledge Distillation for Face Ensembles, Conference on Computer Vision and Pattern Recognition (CVPR) 2023.
• Shumin Deng, Chengming Wang, Zhoubo Li, Ningyu Zhang, Zelin Dai, Hehong Chen, Feiyu Xiong, Ming Yan, Qiang Chen, Mosha Chen, Jiaoyan Chen, Jeff Z. Pan, Bryan Hooi, Huajun Chen. Construction and Applications of Billion-Scale Pre-trained Multimodal Business Knowledge Graph, IEEE International Conference on Data Engineering (ICDE) 2023.
• Jiaxin Jiang, Yuan Li, Bingsheng He, Bryan Hooi, Jia Chen, Johan Kok Zhi Kang. Spade: A real-time fraud detection framework on evolving graphs, International Conference on Very Large Data Bases (VLDB) 2023.
• Yiwei Wang, Bryan Hooi, Yozen Liu, Neil Shah. Graph Explicit Neural Networks: Explicitly Encoding Graphs for Efficient and Accurate Inference, ACM International Conference on Web Search and Data Mining (WSDM) 2023.

  2022

• Yiwei Wang, Bryan Hooi, Yozen Liu, Tong Zhao, Zhichun Guo, Neil Shah. Flashlight: Scalable Link Prediction with Effective Decoders, Learning On Graphs Conference (LOG) 2022.
• Kuangqi Zhou, Kaixin Wang, Jian Tang, Jiashi Feng, Bryan Hooi, Peilin Zhao, Tingyang Xu, Xinchao Wang. Jointly Modelling Uncertainty and Diversity for Active Molecular Property Prediction, Learning On Graphs Conference (LOG) 2022.
• Yifan Zhang, Bryan Hooi, Lanqing Hong, Jiashi Feng. Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition, Neural Information Processing Systems (NeurIPS) 2022.
• Juncheng Liu, Bryan Hooi, Kenji Kawaguchi, Xiaokui Xiao. MGNNI: Multiscale Graph Neural Networks with Implicit Layers, Neural Information Processing Systems (NeurIPS) 2022.
• Miao Xiong, Shen Li, Wenjie Feng, Ailin Deng, Jihai Zhang, Bryan Hooi. Birds of a Feather Trust Together: Knowing When to Trust a Classifier via Adaptive Neighborhood Aggregation, Transactions on Machine Learning Research (TMLR) 2022.
• Aashish Kolluri, Teodora Baluta, Bryan Hooi, Prateek Saxena. LPGNet: Link Private Graph Networks for Node Classification, ACM Conference on Computer and Communications Security (CCS) 2022.
• Xu Liu, Yuxuan Liang, Chao Huang, Yu Zheng, Bryan Hooi and Roger Zimmermann. When Do Contrastive Learning Signals Help Spatio-Temporal Graph Forecasting?, ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL) 2022.
• Ailin Deng, Shen Li, Miao Xiong, Zhirui Chen, Bryan Hooi. Trust, but Verify: Using Self-Supervised Probing to Improve Trustworthiness, European Conference on Computer Vision (ECCV) 2022.
• Yiwei Wang, Yujun Cai, Yuxuan Liang, Henghui Ding, Changhu Wang, Bryan Hooi. Time-Aware Neighbor Sampling on Temporal Graphs, International Joint Conference on Neural Networks (IJCNN) 2022.
• Jiaying Wu, Bryan Hooi. Probing Spurious Correlations in Popular Event-Based Rumor Detection Benchmarks, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 2022.
• Juncheng Liu, Yiwei Wang, Bryan Hooi, Renchi Yang, Xiaokui Xiao. LSCALE: Latent Space Clustering-Based Active Learning for Node Classification, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 2022.
• Adam Goodge, Bryan Hooi, See Kiong Ng, Wee Siong Ng. ARES: Locally Adaptive Reconstruction-based Anomaly Scoring, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 2022.
• Kaixin Wang, Navdeep Kumar, Kuangqi Zhou, Bryan Hooi, Jiashi Feng, Shie Mannor. The Geometry of Robust Value Functions, International Conference on Machine Learning (ICML) 2022.
• Shen Li, Bryan Hooi. Neural PCA for Flow-Based Representation Learning, International Joint Conference on Artificial Intelligence (IJCAI) 2022.
• Ailin Deng, Adam Goodge, Lang Yi Ang, Bryan Hooi. CADET: Calibrated Anomaly Detection for Mitigating Hardness Bias, International Joint Conference on Artificial Intelligence (IJCAI) 2022.
• Yiwei Wang, Muhao Chen, Wenxuan Zhou, Yujun Cai, Yuxuan Liang, Dayiheng Liu, Baosong Yang, Juncheng Liu, Bryan Hooi. Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis, North American Chapter of the Association for Computational Linguistics (NAACL) 2022.
• Nicholas Lim, Bryan Hooi, See-Kiong Ng, Yong Liang Goh, Renrong Weng, Rui Tan. Hierarchical Multi-Task Graph Recurrent Network for Next POI Recommendation, ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) 2022.
• Baixiang Huang, Bryan Hooi. Traffic Accident Prediction using Graph Neural Networks: New Datasets and the TRAVEL Model, The Web Conference (WebConf) Workshop on Graph Learning Benchmarks 2022.
• Siddharth Bhatia, Arjit Jain, Shivin Srivastava, Kenji Kawaguchi, Bryan Hooi. MemStream: Memory-Based Streaming Anomaly Detection, The Web Conference (WebConf) 2022.
• Adam Goodge, Bryan Hooi, See Kiong Ng, Wee Siong Ng. LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks, AAAI Conference on Artificial Intelligence (AAAI) 2022.
• Shenghua Liu, Bin Zhou, Quan Ding, Bryan Hooi, Zhengbo Zhang, Huawei Shen, Xueqi Cheng. Time Series Anomaly Detection with Adversarial Reconstruction Networks, IEEE Transactions on Knowledge and Data Engineering (TKDE) 2022.
• Shimiao Li, Amritanshu Pandey, Bryan Hooi, Christos Faloutsos, Larry Pileggi Dynamic Graph-Based Anomaly Detection in the Electrical Grid, IEEE Transactions on Power Systems 2022.
• Xiaobing Sun, Wenjie Feng, Shenghua Liu, Yuyang Xie, Siddharth Bhatia, Bryan Hooi, Wenhan Wang, Xueqi Cheng. MonLAD: Money Laundering Agents Detection in Transaction Streams, ACM International Conference on Web Search and Data Mining (WSDM) 2022.
• Siddharth Bhatia, Rui Liu, Bryan Hooi, Minji Yoon, Kijung Shin, Christos Faloutsos. Real-Time Anomaly Detection in Edge Streams, ACM Transactions on Knowledge Discovery from Data (TKDD) 2022.

  2021

• Yifan Zhang, Bryan Hooi, Dapeng Hu, Jian Liang, Jiashi Feng. Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning, Neural Information Processing Systems (NeurIPS) 2021.
• Juncheng Liu, Kenji Kawaguchi, Bryan Hooi, Yiwei Wang, Xiaokui Xiao. EIGNN: Efficient Infinite-Depth Graph Neural Networks, Neural Information Processing Systems (NeurIPS) 2021.
• Yiwei Wang, Yujun Cai, Yuxuan Liang, Henghui Ding, Changhu Wang, Siddharth Bhatia, Bryan Hooi. Adaptive Data Augmentation on Temporal Graphs, Neural Information Processing Systems (NeurIPS) 2021.
• Koki Kawabata, Siddharth Bhatia, Rui Liu, Mohit Wadhwa, Bryan Hooi. SSMF: Shifting Seasonal Matrix Factorization, Neural Information Processing Systems (NeurIPS) 2021.
• Kuangqi Zhou*, Yanfei Dong*, Kaixin Wang, Wee Sun Lee, Bryan Hooi, Huan Xu, Jiashi Feng. Understanding and Resolving Performance Degradation in Deep Graph Convolutional Networks, ACM Conference on Information and Knowledge Management (CIKM) 2021.
• Siddharth Bhatia, Yiwei Wang, Bryan Hooi, Tanmoy Chakraborty. GraphAnoGAN: Detecting Anomalous Snapshots from Attributed Graphs, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 2021.
• Kaixin Wang*, Kuangqi Zhou*, Qixin Zhang, Jie Shao, Bryan Hooi, Jiashi Feng. Towards Better Laplacian Representation in Reinforcement Learning with Generalized Graph Drawing, International Conference on Machine Learning (ICML) 2021.
• Shixuan Sun, Yuhang Chen, Bingsheng He, and Bryan Hooi. PathEnum: Towards Real-Time Hop-Constrained s-t Path Enumeration, ACM SIGMOD International Conference on Management of Data 2021 (SIGMOD) 2021.
• Shen Li, Xu Jianqing, Xiaqing Xu, Pengcheng Shen, Shaoxin Li, and Bryan Hooi. Spherical Confidence Learning for Face Recognition, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
• Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai and Bryan Hooi. Mixup for Node and Graph Classification, The Web Conference (WebConf) 2021.
• Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai and Bryan Hooi. CurGraph: Curriculum Learning for Graph Classification, The Web Conference (WebConf) 2021.
• Siddharth Bhatia, Arjit Jain, Pan Li, Ritesh Kumar and Bryan Hooi. MSTREAM: Fast Anomaly Detection in Multi-Aspect Streams, The Web Conference (WebConf) 2021. Best paper finalist.
• Siddharth Bhatia*, Arjit Jain*, Bryan Hooi. ExGAN: Adversarial Generation of Extreme Samples, AAAI Conference on Artificial Intelligence (AAAI) 2021.
• Ailin Deng, Bryan Hooi. Graph Neural Network-Based Anomaly Detection in Multivariate Time Series, AAAI Conference on Artificial Intelligence (AAAI) 2021.
• Nicholas Lim, Bryan Hooi, See Kiong Ng, Xueou Wang, Yong Liang Goh, Renrong Weng and Rui Tan. Origin-Aware Next Destination Recommendation with Personalized Preference Attention, ACM International Conference on Web Search and Data Mining (WSDM) 2021.

  2020

• Minji Yoon, Theophile Gervet, Bryan Hooi, and Christos Faloutsos. Autonomous Graph Mining Algorithm Search with Best Speed/Accuracy Trade-off, IEEE International Conference on Data Mining (ICDM) 2020. Selected as one of the best papers of ICDM’20 for journal invitation to special issue of KAIS
• Yiwei Wang, Shenghua Liu, Minji Yoon, Hemank Lamba, Wei Wang, Christos Faloutsos, and Bryan Hooi. Provably Robust Node Classification via Low-Pass Message Passing, IEEE International Conference on Data Mining (ICDM) 2020.
• Yiwei Wang, Wei Wang, Yujun Cai, Bryan Hooi and Beng Chin Ooi. Detecting Implementation Bugs in Graph Convolutional Network based Node Classifiers, ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) 2020.
• Nicholas Lim, Bryan Hooi, See Kiong Ng, Xueou Wang, Yong Liang Goh, Renrong Weng and Jagannadan Varadarajan. STP-UDGAT: Spatial-Temporal-Preference User Dimensional Graph Attention Network for Next POI Recommendation, ACM Conference on Information and Knowledge Management (CIKM) 2020.
• Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai and Bryan Hooi. Progressive Supervision for Node Classification, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 2020.
• Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, Juncheng Liu and Bryan Hooi. NodeAug: Semi-Supervised Node Classification with Data Augmentation, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2020
• Manh Tuan Do, Se-eun Yoon, Bryan Hooi, and Kijung Shin. Structural Patterns and Generative Models of Real-world Hypergraphs, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2020
• Adam Goodge, Bryan Hooi, See Kiong Ng, Wee Siong Ng. Robustness of Autoencoders for Anomaly Detection Under Adversarial Impact, International Joint Conference on Artificial Intelligence (IJCAI) 2020.
• Shen Li, Bryan Hooi, Gim Hee Lee. Identifying through Flows for Recovering Latent Representations, International Conference on Learning Representations (ICLR) 2020.
• Xiangfeng Li, Shenghua Liu, Zifeng Li, Xiaotian Han, Chuan Shi, Bryan Hooi, He Huang, Xueqi Cheng. FlowScope: Spotting Money Laundering Based on Graphs, Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI) 2020.
• Bryan Hooi, Kijung Shin, Hemank Lamba, and Christos Faloutsos. TellTail: Fast Scoring and Detection of Dense Subgraphs, Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI) 2020.
• Siddharth Bhatia, Bryan Hooi, Minji Yoon, Kijung Shin, and Christos Faloutsos. MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams, Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI) 2020.
• Kijung Shin, Sejoon Oh, Jisu Kim, Bryan Hooi, and Christos Faloutsos. Fast, Accurate and Provable Triangle Counting in Fully Dynamic Graph Streams, ACM Transactions on Knowledge Discovery from Data (TKDD) 2020.

  2019

• Bin Zhou, Shenghua Liu, Bryan Hooi, Xueqi Cheng, and Jing Ye. BeatGAN: Anomalous Rhythm Detection using Adversarially Generated Time Series, International Joint Conference on Artificial Intellince (IJCAI) 2019.
• Minji Yoon, Bryan Hooi, Kijung Shin and Christos Faloutsos. Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2019.
• Marko Jereminov, Bryan Hooi, Amritanshu Pandey, Hyun Ah Song, Christos Faloutsos, and Larry Pileggi. Impact of Load Models on Power Flow Optimization, IEEE Power & Energy Society General Meeting (PES) 2019.
• Bryan Hooi, Kijung Shin, Shenghua Liu, and Christos Faloutsos. SMF: Drift Aware Matrix Factorization with Seasonal Patterns, SIAM International Conference in Data Mining (SDM), 2019.
• Bryan Hooi and Christos Faloutsos. Branch and Border: Partition-Based Change Detection in Multivariate Time Series, SIAM International Conference in Data Mining (SDM), 2019.
• Wenjie Feng, Shenghua Liu, Christos Faloutsos, Bryan Hooi, Huawei Shen, Xueqi Cheng. Anomaly Detection in Large Graphs based on Vision-guided Summarization, Pacific-Asia Conference in Knowledge Discovery and Data Mining (PAKDD), 2019.

  2018

• Bryan Hooi, Dhivya Eswaran, Amritanshu Pandey, Marko Jereminov, Larry Pileggi, and Christos Faloutsos. ChangeDAR: Online Localized Change Detection for Sensor Data on a Graph. ACM International Conference on Information and Knowledge Management (CIKM), 2018.
• Shenghua Liu, Bryan Hooi, and Christos Faloutsos. A Contrast Metric for Fraud Detection in Rich Graphs, Transactions on Knowledge and Data Engineering (TKDE) 2018.
• Bryan Hooi, Dhivya Eswaran, Hyun Ah Song, Amritanshu Pandey, Marko Jereminov, Larry Pileggi, and Christos Faloutsos GridWatch: Sensor Placement and Anomaly Detection in the Electrical Grid. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 2018. Runner-up Best Student Data Mining Paper Award
• Kijung Shin, Jisu Kim, Bryan Hooi, and Christos Faloutsos. Think before You Discard: Accurate Triangle Counting in Graph Streams with Deletions. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 2018.
• Aastha Nigam, Kijung Shin, Ashwin Bahulkar, Bryan Hooi, David Hachen, Boleslaw K. Szymanski, Christos Faloutsos, Nitesh V. Chawla. ONE-M: Modeling the Co-evolution of Opinions and Network Connections European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 2018.
• Wenjie Feng, Shenghua Liu, Christos Faloutsos, Bryan Hooi, Huawei Shen and Xueqi Cheng EagleMine: Vision-Guided Mining in Large Graphs. KDD Outlier Detection De-constructed (ODD) Workshop 2018.
• Kijung Shin, Bryan Hooi, and Christos Faloutsos Fast, Accurate and Flexible Algorithms for Dense Subtensor Mining. ACM Transactions on Knowledge Discovery from Data (TKDD) 2018.
• Pudi Chen, Shenghua Liu, Chuan Shi, Bryan Hooi, Bai Wang, Xueqi Cheng. NeuCast: Seasonal Neural Forecast of Power Grid Time Series. International Joint Conference on Artificial Intelligence (IJCAI), 2018.
• Bryan Hooi, Hyun Ah Song, Amritanshu Pandey, Marko Jereminov, Larry Pileggi, and Christos Faloutsos. StreamCast: Fast and Online Mining of Power Grid Time Sequences. SIAM International Conference on Data Mining (SDM), 2018.
• Srijan Kumar, Bryan Hooi, Disha Makhija, Mohit Kumar, Christos Faloutsos, and V.S. Subrahmanian. REV2: Fraudulent User Prediction in Rating Platforms. ACM International Conference on Web Search and Data Mining (WSDM) 2018.

  2017

• Pedro Costa, Aurelio Campilho, Bryan Hooi, Asim Smailagic, Kris Kitani, Shenghua Liu, Christos Faloutsos and Adrian Galdran EyeQual: Accurate, Explainable, Retinal Image Quality Assessment. 16th IEEE International Conference On Machine Learning and Applications 2017.
• Shenghua Liu, Bryan Hooi, and Christos Faloutsos Fraud Detection using Graph Topology and Temporal Spikes. KDD Mining and Learning with Graphs (MLG) Workshop 2017.
• Shenghua Liu, Bryan Hooi, Christos Faloutsos. HoloScope: Topology-and-Spike Aware Fraud Detection. ACM International Conference on Information and Knowledge Management (CIKM) 2017.
• Hemank Lamba, Bryan Hooi, Kijung Shin, Christos Faloutsos, and Jurgen Pfeffer. ZOORANK: Ranking Suspicious Entities in Time-Evolving Tensors. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 2017.
• Bryan Hooi, Shenghua Liu, Asim Smailagic, and Christos Faloutsos BEATLEX: Summarizing and Forecasting Time Series with Patterns. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 2017.
• Hyun Ah Song, Bryan Hooi, Marko Jereminov, Amritanshu Pandey, Larry Pileggi, and Christos Faloutsos PowerCast: Mining and Forecasting Power Grid Sequences. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 2017.
• Chi Ling Chan, Justin Lai, Bryan Hooi and Todd Davies The Message or the Messenger? Inferring Virality and Diffusion Structure from Online Petition Signature Data. Social Informatics (SocInfo) 2017.
• Marko Jereminov, Amritanshu Pandey, Hyun Ah Song, Bryan Hooi, Larry Pileggi, and Christos Faloutsos. Linear Load Model for Robust Power System Analysis. IEEE International Conference on Innovative Smart Grid Technologies (ISGT) 2017.
• Kijung Shin, Bryan Hooi, Jisu Kim, and Christos Faloutsos. DenseAlert: Incremental Dense-Subtensor Detection in Tensor Streams. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2017.
• Bryan Hooi, Kijung Shin, Hyun Ah Song, Alex Beutel, Neil Shah, and Christos Faloutsos. Graph-Based Fraud Detection in the Face of Camouflage. ACM Transactions on Knowledge Discovery from Data (TKDD), 2017.
• Tsubasa Takahashi, Bryan Hooi, and Christos Faloutsos. AutoCyclone: Automatic Mining of Cyclic Patterns and Anomalies by Robust Tensor Factorization. World Wide Web (WWW) Conference 2017.
• Kijung Shin, Bryan Hooi, Jisu Kim and Christos Faloutsos. D-Cube: Dense-Block Detection in Terabyte-Scale Tensors. ACM International Conference on Web Search and Data Mining (WSDM) 2017.

  2016

• Neil Shah, Alex Beutel, Bryan Hooi, Leman Akoglu, Stephan Gunnemann, Disha Makhija, Mohit Kumar, and Christos Faloutsos. EdgeCentric: Anomaly Detection in Edge-Attributed Networks. IEEE International Conference on Data Mining (ICDM) DMCS Workshop 2016.
• Kijung Shin, Bryan Hooi, and Christos Faloutsos. M-Zoom: Fast Dense-Block Detection in Tensors with Quality Guarantees. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 2016.
• Bryan Hooi, Hyun Ah Song, Alex Beutel, Neil Shah, Kijung Shin, and Christos Faloutsos. FRAUDAR: Bounding Graph Fraud in the Face of Camouflage. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2016. KDD Best Paper Award (Research Track)
• Meng Jiang, Alex Beutel, Peng Cui, Bryan Hooi, Shiqiang Yang, and Christos Faloutsos. Spotting Suspicious Behaviors in Multimodal Data: A General Metric and Algortihms. IEEE Transactions on Knowledge and Data Engineering (TKDE) 2016.
• Evangelos E. Papalexakis, Bryan Hooi, Konstantinos Pelechrinis, and Christos Faloutsos. Power-Hop: A Pervasive Observation for Real Complex Networks. PLoS ONE 11(3) 2016.
• Bryan Hooi, Neil Shah, Alex Beutel, Stephan Gunnemann, Leman Akoglu, Mohit Kumar, Disha Makhija, and Christos Faloutsos. BIRDNEST: Bayesian Inference for Ratings-Fraud Detection. SIAM International Conference on Data Mining (SDM) 2016.
• Bryan Hooi, Hyun Ah Song, Evangelos Papalexakis, Rakesh Agrawal, and Christos Faloutsos. Matrices, Compression, Learning Curves: formulation, and the GROUPNTEACH algorithms. Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2016.

  2015

• Bryan Hooi, Hyun Ah Song, Evangelos Papalexakis, Rakesh Agrawal, and Christos Faloutsos. Education, Learning and Information Theory. IEEE ICDM Workshop on Data Mining for Educational Assessment and Feedback 2015.
• Meng Jiang, Alex Beutel, Peng Cui, Bryan Hooi, Shiqiang Yang, and Christos Faloutsos. A General Suspiciousness Metric for Dense Blocks in Multimodal Data. IEEE International Conference on Data Mining (ICDM) 2015.

Branch and Border: Partition-Based Change Detection in Multivariate Time Series.
BNB (Branch and Border) is a nonparametric multivariate change detection method. BNB approaches change detection by separating points before and after the change using an ensemble of random partitions.

BeatLex: Summarizing and Forecasting Time Series with Patterns.
BeatLex is an algorithm that succintly summarizes and forecasts time series data. It is designed for data containing patterns that occur repeatedly, especially if these patterns are complex and nonlinear, change over time, and may distortions in their shape or length.

FRAUDAR: Bounding Graph Fraud in the Face of Camouflage.
FRAUDAR is an algorithm for detecting graph fraud based on dense subgraph detection, which is aimed at being robust to camouflage (i.e. attackers which add false edges in order to mask their presence)

BIRDNEST: Bayesian Inference for Ratings-Fraud Detection
BIRDNEST is an algorithm for detecting fraudulent users in timestamped ratings data (e.g. users ratings products with 1 to 5 stars), based on detecting users that differ excessively from the norm, in terms of what ratings they give and the time distribution of their ratings.

Matrices, Compression, Learning Curves: formulation, and the GROUPNTEACH algorithms.
GNT takes a collection of facts, arranged in a binary matrix, and reorders the rows and columns for the purpose of teaching and visualization.