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Research Interests

Our research is dedicated to enhancing AI safety and ethics by crafting responsible machine learning techniques. We aim to build resilient AI systems capable of identifying and adapting to novel scenarios, quantifying and managing uncertainties for safety applications, and promoting fairness and equity in various domains. Our objective is to reliably detect and address safety concerns, ethical dilemmas, biases, and anomalies within complex datasets. Our work leverages AI to improve healthcare diagnostics, ensure the safety of autonomous systems, uphold fairness in financial services, strengthen cybersecurity, tackle environmental issues, ethically moderate digital content, reform the criminal justice system, and tailor educational experiences, all while prioritizing safety, equity, and tangible benefits for society.

Quote:

"The basic guidelines of an ethical AI system refer to those values which can be implemented at the core of every AI algorithm to bring out the safety, security, and fundamental goodness of artificial intelligence for all beings and human society at large."

~~~ Amit Ray

Background

I joined the Computer Science Department at UT Dallas as an Associate Professor with tenure in 2019-Fall. I was previously an assistant professor in the Computer Science department at University at Albany - SUNY from 2014-Spring to 2019-Spring. I was a postdoctoral researcher in the Event and Pattern Detection (EPD) Laboratory and the iLab at Carnegie Mellon University in 2013, where I worked with Daniel B. Neill and Ramayya Krishnan . I got my Ph.D. from the Computer Science Depatment at Virginia Polytechnic Institute and State University under the advising of Chang-Tien Lu in Dec. 2012; M.S. from the School of Computer Science at Beijing University of Aeronautics & Astronautics University in Mar. 2004; and B.S. from the School of Computer and Communication at Hunan University in Jul. 2001. During my doctoral studies, I also worked at IBM T.J. Watson Research Labs, Hawthorne, NY during 2011 summer.

Teaching

2019 Spring, ICSI431/ICSI531 Data Mining [Link]

2018 Fall, ICSI660 Anomalous Pattern Detection [Link]

2018 Spring, ICSI431/ICSI531 Data Mining [Link]

2017 Spring, ICSI431/ICSI531 Data Mining [Link]

2016 Fall, ICSI660 Anomalous Pattern Detection [Link]

2016 Fall, ICSI 535 Artificial Intelligence I [Link]

2016 Spring, ICSI431/ICSI531 Data Mining [Link]

2015 Spring, ICSI431/ICSI531 Data Mining [Link]

2015 Fall, ICSI660 Anomalous Pattern Detection [Link]

2014 Spring, ICSI431/ICSI531 Data Mining

2014 Fall, ICSI660 Anomalous Pattern Detection 

Publications

2024

1. Algorithmic Fairness Generalization under Covariate and Dependence Shifts Simultaneously.
   Chen Zhao, Kai Jiang, Xintao Wu, Haolinag Wang, Latifur Khan, Christian Grant, Feng Chen.
   Proceedings of the 30th ACM SIGKDD conference on knowledge discovery and data mining (KDD'24), 2024. (To Appear)
2. From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling.
   Aneesh Komanduri, Xintao Wu, Yongkai Wu, Feng Chen.
   Transactions on Machine Learning Research (TMLR) , 2024. (To Appear)
3. Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms
   Aneesh Komanduri, Xintao Wu, Yongkai Wu, Feng Chen
   in Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI'24), 2024. (To Appear)
4. Uncertainty-aware Graph-based Hyperspectral Image Classification.
   Linlin Yu, Yifei Lou, and Feng Chen.
   Proceeding of the International Conference on Learning Representations (ICLR), 2024 (To Appear)
5. Hyper Evidential Deep Learning to Quantify Composite Classification Uncertainty.
   Changbin Li, Kangshuo Li, Yuzhe Ou, Lance M. Kaplan, Audun Jøsang , Dong H. Jeong, Jin-Hee Cho, Feng Chen.
   Proceeding of the International Conference on Learning Representations (ICLR), 2024 (To Appear)
6. VGX: Large-Scale Sample Generation for Boosting Learning-Based Software Vulnerability Analyses
   Yu Nong, Richard Fang, Guangbei Yi, Kunsong Zhao, Xiapu Luo, Feng Chen, and Haipeng Cai.
   In IEEE/ACM International Conference on Software Engineering (ICSE), 2024. (To Appear)
7. Bridging the gap between spatial and spectral domains: A unified framework for graph neural networks
   Zhiqian Chen, Fanglan Chen, Lei Zhang, Taoran Ji, Kaiqun Fu, Liang Zhao, Feng Chen, Lingfei Wu, Charu Aggarwal, and Chang-Tien Lu
   (ACM Computing Survey), 2024 (Impact factor: 16.6). (To Appear)
8. A Survey on Uncertainty Reasoning and Quantification in Belief Theory and Its Application to Deep Learning  
   Zhen Guo, Zelin Wan, Qisheng Zhang, Xujiang Zhao, Qi Zhang, Lance M. Kaplan, Dong H. Jeong, Feng Chen, Jin-Hee Cho
   (Information Fusion), 2024 (Impact factor: 17.564). (To Appear)
9. Dynamic Environment Responsive Online Meta-Learning with Fairness Awareness
   Chen Zhao, Feng Mi, Xintao Wu, Kai Jiang, Latifur Khan, Feng Chen
   ACM Transactions on Knowledge Discovery from Data (TKDD), 2024. (To Appear)

2023

1. Improvements on Uncertainty Quantification for Node Classification via Distance Based Regularization
   Russell Alan Hart, Linlin Yu, Yifei Lou, and Feng Chen
   Thirty-Seventh Conference on Neural Information Processing Systems (NeurIPS), 2023.
2. Towards Fair Disentangled Online Learning for Changing Environments.
   Chen Zhao, Feng Mi, Xintao Wu, Kai Jiang, Latifur Khan, Christian Grant, Feng Chen.
   Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining (KDD 2023), 2023. (To Appear)
3. Evading Provenance-Based ML Detectors with Adversarial System Actions
   Kunal Mukherjee, Joshua Wiedemeier, Tianhao Wang, James Wei, Feng Chen, Muhyun Kim, Murat Kantarcioglu, Kangkook Jee
   Proceedings of The 32nd USENIX Security Symposium (USENIX 2023), 2023. (To Appear)
4. VulGen: Realistic Vulnerable Sample Generation via Pattern Mining and Deep Learning.
   Yu Nong, Yuzhe Ou, Michael Pradel, Feng Chen, and Haipeng Cai.
   Proceedings of IEEE/ACM International Conference on Software Engineering (ICSE 2023), 2023. (To Appear)
5. Multi-Label Temporal Evidential Neural Networks for Early Event Detection.
   Xujiang Zhao, Xuchao Zhang, Chen Zhao, Jin-Hee Cho, Lance Kaplan, Dong Hyun Jeong, and Audun Jøsang.
   Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP'23), pages 1-5, 2023.
6. Detecting Intents of Fake News Using Uncertainty-Aware Deep Reinforcement Learning
   Zhen Guo, Qi Zhang, Qisheng Zhang, Lance M. Kaplan, Audun Jøsang, Feng Chen, Dong Hyun Jeong, and Jin-Hee Cho.
   Proceedings of IEEE International Conference on Web Services (ICWS'23), pages 722-724, 2023.
7. Active Learning on Neural Networks through Interactive Generation of Digit Patterns and Visual Representation
   Dong H Jeong, Jin-Hee Cho, Feng Chen, Audun Jøsang, and Soo-Yeon Ji.
   Proceedings of the 2023 IEEE Integrated STEM Education Conference (ISEC'23), pages 143-150, 2023.
8. Interactive Web-Based Visual Analysis on Network Traffic Data.
   Dong Hyun Jeong, Jin-Hee Cho, Feng Chen, Lance M. Kaplan, Audun Jøsang, and Soo-Yeon Ji
   nformation, vol. 14, No. 1, pages 1-16, 2023

2022

1. SCM-VAE: Learning Identifiable Causal Representations via Structural Knowledge.
   Aneesh Komanduri, Yongkai Wu, Wen Huang, Feng Chen, and Xintao Wu
   Proceedings of the IEEE International Conference on Big Data (IEEE BigData), 2022. (Acceptance rate: 17%)
2. Defending Evasion Attacks via Adversarially Adaptive Training.
   Minh-Hao Van, Wei Du, Xintao Wu, Feng Chen, Aidong Lu
   Proceedings of the IEEE International Conference on Big Data (IEEE BigData), 2022. (Acceptance rate: 17%)
3. Generating Realistic Vulnerabilities via Neural Code Editing: An Empirical Study.
   Yu Nong, Yuzhe Ou, Michael Pradel, Feng Chen, and Haipeng Cai.
   Proceedings of ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2022), pages 1097–1109, 2023. (artifact evaluated; badges: Available, Functional).
4. How Out-of-Distribution Data Hurts Semi-Supervised Learning.
   Xujiang Zhao, Krishnateja Killamsetty, Rishabh Iyer, and Feng Chen
   Proceedings of the IEEE International Conference on Data Mining (ICDM 2022), 2022. (To Appear; 9% Acceptance Rate for Regular/Full Papers)
5. Adaptive Fairness-Aware Online Meta-Learning for Changing Environments.
   Chen Zhao, Feng Mi, Xintao Wu, Kai Jiang, Latifur Khan, Feng Chen.
   Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining (KDD 2022), 2022. (Acceptance rate: 14.99%)
6. Framing Algorithmic Recourse for Anomaly Detection.
   Debanjan Datta, Feng Chen, and Naren Ramakrishnan.
   Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining (KDD 2022), 2022. (Acceptance rate: 14.99%)
7. PLATINUM: Semi-Supervised Model Agnostic Meta-Learning using Submodular Mutual Information.
   Changbin Li, Suraj Kothawade, Feng Chen, and Rishabh Iyer.
   Proceedings of the International Conference of Machine Learning (ICML 2022), 2022.
8. Calibrated Nonparametric Scan Statistics for Anomalous Pattern Detection in Graphs.
   Chunpai Wang, Daniel Neill, Feng Chen
   Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI), 2022. (Acceptance Rate: 15%, and Selected for Oral Presentation (< 5%)) [PDF]
9. Nested Bi-level Optimization Framework for Robust Few Shot Learning.
   Krishnateja Killamsetty, Changbin Li, Chen Zhou, Feng Chen, Rishabh Iyer
   Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI), 2022 (Acceptance rate: 15%)
10. Layer Adaptive Deep Neural Networks for Out-of-distribution Detection.
    Haoliang Wang, Chen Zhao, Xujiang Zhao, and Feng Chen.
    Proceedings of the 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), (PAKDD 2022), 2022. (Acceptance rate: 19.3%)

2021

1. Robust Semi-Supervised Learning with Out of Distribution Data.
   Xujiang Zhao, Killamsetty Krishnateja, Rishabh Iyer, Feng Chen
   arXiv:2010.03658, 2021
2. A Reweighted Meta Learning Framework for Robust Few Shot Learning.
   Krishnateja Killamsetty, Changbin Li, Chen Zhou, Rishabh Iyer, and Feng Chen.
   NeurIPS 2021 Workshop on Meta-Learning, (MetaLearn 2021), 2021 (To Appear).
3. RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning.
   Krishnateja Killamsetty, Xujiang Zhou, Feng Chen, and Rishabh Iyer.
   Proceedings of the Thirty-Five Neural Information Processing Systems, (NeurIPS 2021), 2021 (To Appear).
4. Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty Estimation.
   Liyan Xu, Xuchao Zhang, Xujiang Zhao, Haifeng Chen, Feng Chen and Jinho D. Choi.
   Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021), 2021 (To Appear).
5. Fairness-Aware Online Meta-learning.
   Chen Zhao, Feng Chen, and Bhavani Thuraisingham.
   Proceedings of the 27th ACM SIGKDD conference on knowledge discovery and data mining (KDD 2021), 2021. [PDF]
6. Distributionally Robust Optimization for Deep Kernel Multiple Instance Learning.
   Hitesh Sapkota, Yiming Ying, Feng Chen, and Qi Yu.
   Proceedings of the Twenty-Fourth International Conference on Artificial Intelligence and Statistics (AISTATS), 2021 (To Appear).
7. Multidimensional Uncertainty-Aware Evidential Neural Networks.
   Yibo Hu, Yuzhe Ou, Xujiang Zhao, Jin-Hee Cho, and Feng Chen.
   Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), 2021 (To Appear). [PDF]

2020

1. Structured Sparsity Model Based Trajectory Tracking Using Private Location Data Release
   Minglai Zhao, Jianxin Li, Qiben Yan, Feng Chen, Hongyi Huang, and Xunxun Chen
   IEEE Transactions on Dependable and Secure Computing (TDSC), 2020 (To Appear).
2. A Bisubmodular Approach to Event Detection and Prediction in Multivariate Social Graphs
   Shuai Zhang, Haoyi Zhou, Feng Chen, and Jianxin Li
   IEEE Transactions on Computational Social Systems (TCSS), 2020 (To Appear).
3. Online Flu Epidemiological Deep Modeling on Disease Contact Network
   Liang Zhao, Jiangzhuo Chen, Feng Chen, Fang Jin, Wei Wang, Chang-Tien Lu, and Naren Ramakrishnan
   Journal of GeoInformatica, vol. 24, no. 2, pp. 443-475, 2020.
4. Efficient Learning with Exponentially-Many Conjunctive Precursors to Forecast Spatial Events
   Liang Zhao, Feng Chen, and Yanfang Ye,
   IEEE Transactions on Knowledge and Data Engineering (TKDE), vol. 32, iss. 10,pp. 1923-1935, 2019.
5. CSL+: Scalable Collective Subjective Logic under Multidimensional Uncertainty
   Adil Alim, Jin-Hee Cho, and Feng Chen
   ACM Transactions on Intelligent Systems and Technology (TIST), 2020. (To Appear) [PDF]
6. PDFM: A Primal-Dual Fairness-Aware Framework for Meta-learning
   Chen Zhao, Feng Chen, Zhuoyi Wang, and Latifur Khan
   CoRR abs/2009.12675, 2020.
7. Uncertainty Aware Semi-Supervised Learning on Graph Data
   Xujiang Zhao, Feng Chen, Shu Hu, and Jin-Hee Cho
   Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS), 2020. (Spotlight acceptance rate: 4%) [PDF]
8. Multifaceted Uncertainty Estimation for Label-Efficient Deep Learning
   Wei Shi, Xujiang Zhao, Feng Chen, and Qi Yu
   Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS), 2020. [PDF]
9. A Primal-Dual Subgradient Approach for Fair Meta Learning
   Chen Zhao, Feng Chen, Zhuoyi Wang, and Latifur Khan
   in Proceedings of the IEEE International Conference on Data Mining (ICDM 2020), 2020. (Regular paper; acceptance rate: 9.8%) [PDF]
10. Detecting Media Self-Censorship without Explicit Training Data
    Rong Rong Tao, Baojian Zhou, Feng Chen, David Mares, Patrick Butler, Naren Ramakrishnan, and Ryan Kennedy
    in Proceedings of the 2020 SIAM International Conference on Data Mining (SDM), pp. 550-558, 2020. [PDF]
11. Fair Meta-Learning For Few-Shot Classification
    Chen Zhao, Changbin Li, Jincheng Li, and Feng Chen
    in Proceedings of the IEEE International Conference on Knowledge Graph (ICKG), pp. 275-282, 2020. [PDF]
12. Unfairness Discovery and Prevention For Few-Shot Regression
    Chen Zhao and Feng Chen
    in Proceedings of the IEEE International Conference on Knowledge Graph (ICKG), pp. 137-144, 2020. [PDF]

2019

1. Dual Averaging Method for Online Graph-structured Sparsity
   Baojian Zhou, Feng Chen, Yiming Ying,
   in Proceedings of the 25th ACM SIGKDD conference on knowledge discovery and data mining (KDD 2019), pages 436-446, 2019. (Oral Presenation) [PDF]
2. Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization
   Baojian Zhou, Feng Chen, Yiming Ying,
   in Proceedings of the 36th International Conference on Machine Learning (ICML 2019), pp. 7563-7573, 2019. [PDF] [Supplementary] [CODE]
3. Rank-Based Multi-task Learning For Fair Regression
   Chen Zhao and Feng Chen
   in Proceedings of the IEEE International Conference on Data Mining (ICDM 2019) , 2019. (Regular paper; acceptance rate: 9.08%).
4. Block-Structured Optimization for Anomalous Pattern Detection in Interdependent Networks
   Fei Jie, Chunpai Wang, Feng Chen, Lei Li, and Xingdong Wu
   in Proceedings of the IEEE International Conference on Data Mining (ICDM 2019) , 2019. (Short paper; acceptance rate: 18.5%).
5. Near-Optimal and Practical Algorithms for Graph Scan Statistics with Connectivity Constraints
   Jose Cadena, Feng Chen, and Anil Vullikanti,
   ACM Transactions on Knowledge Discovery from Data (TKDD), 2019. (To Appear).
6. Uncovering Specific-Shape Graph Anomalies in Attributed Graphs
   Nanan Wu, Wenjun Wang, Feng Chen, Jianxin Li, Bo Li, and Jinpeng Huai,
   in Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019), 2019. (Full paper; Acceptance rate: 16.2%).

2018

1. A Nonparametric Approach to Uncovering Connected Anomalies by Tree Shaped Priors
   Nannan Wu, Feng Chen, Jianxin Li, Jinpeng Huai, Baojian Zhou, Bo Li, and Naren Ramakrishnan,
   IEEE Transactions on Knowledge and Data Engineering (TKDE), 2018. (Impact Factor 3.438).
2. Deep Learning for Predicting Dynamic Uncertain Opinions in Network Data
   Xujiang Zhao, Feng Chen, and Jin-Hee Cho,
   in Proceedings of the IEEE International Conference on Data Data (BigData 2018), 2018.
3. Deep Learning based Scalable Inference of Uncertain Opinions
   Xujiang Zhao, Feng Chen, and Jin-Hee Cho,
   in Proceedings of the IEEE International Conference on Data Mining (ICDM 2018), 2018. (Full paper; Acceptance rate: 8.86%).
4. Rational Neural Networks for Approximating Jump Discontinuities of Graph Convolution Operator
   Zhiqian Chen, Feng Chen, Rongjie Lai, Xuchao Zhang, and Chang-Tien Lu,
   in Proceedings of the IEEE International Conference on Data Mining (ICDM 2018), 2018. (Full paper; Acceptance rate: 8.86%).
5. A Nonparametric Approach to Uncovering Connected Anomalies by Tree Shaped Priors
   Nannan Wu, Feng Chen, Jianxin Li, Jinpeng Huai, Baojian Zhou, Bo Li, Naren Ramakrishnan,
   IEEE Transactions on Knowledge and Data Engineering (TKDE), 2018 (Impact Factor 3.438). (To Appear)
6. Bi-Submodular Optimization (BSMO) for Detecting Drug-Drug Interactions (DDIs) from On-line Health Forum
   Yan Hu, Rui Wang, and Feng Chen,
   Journal of Healthcare Informatics Research (JHIR'18), 2018. (Minor Revision).
7. Uncertainty Characteristics of Subjective Opinions
   Audun Jøsang, Jin-Hee Cho, and Feng Chen,
   in Proceedings of the IEEE International Conference on Information Fusion (FUSION'18), 2018. (To Appear)
8. PSCluster: Differentially Private Spatial Cluster Detection for Mobile Crowdsourcing Applications
   Boyang Hu (*), Baojian Zhou (*), Qiben Yan, Alim Adil, Feng Chen, and Huacheng Zeng
   in Proceedings of the IEEE International Conference on Computer Communications and Network Security (CNS'18), 2018. (To Appear)
9. Graph Anomaly Detection Based on Steiner Connectivity and Density
   Jose Cadena, Feng Chen, Anil Vullikanti,
   Proceedings of the IEEE, 2018 (Impact Factor 9.237). (To Appear)
10. An Efficient Framework for Detecting Evolving Anomalous Subgraphs in Dynamic Networks
    Minglai Shao, Jianxin Li, Feng Chen, and Xunxun Chen
    in Proceedings of the IEEE International Conference on Computer Communications (INFOCOM'18), 2018. (Regular paper; Acceptance rate: 19.2%; To Appear)

2017

1. Parallel Algorithms for Anomalous Subgraph Detection
   Jieyu Zhao, Jianxin Li, Baojian Zhou, Feng Chen, Paul Tomchik, Wuyang Ju,
   in Journal of Concurrency and Computation: Practice and Experience (JCC), 29(3), 2017.
2. Techniques for Efficient Detection of Rapid Weather Changes and Analysis of Their Impacts on a Highway Network
   Adil Alim, Aparna Joshi, Feng Chen, and Catherine T. Lawson
   in Proceedings of the 2nd IEEE International Workshop on Big Spatial Data (BSD 2017) at IEEE BigData, pages 3378-3387, 2017.
3. Collective Subjective Logic: Scalable Uncertainty-based Opinion Inference
   Feng Chen, Chunpai Wang, and Jin-Hee Cho
   in Proceedings of the IEEE International Conference on Big Data (BigData'17), 2017. (Regular paper; Acceptance rate: 18%; To Appear)
4. Discrn: A distributed storytelling framework for intelligence analysis,
   Manu Shukla, Ray Dos Santos, Feng Chen, and Chang-Tien Lu
   Big Data Journal, 2017 (To Appear)
5. A Generic Framework for Interesting Subspace Cluster Detection in Multi-attributed Networks,
   Feng Chen, Baojian Zhou, Adil Alim, and Liang Zhao
   in Proceedings of the IEEE International Conference on Data Mining (ICDM 2017), pages 41-50, 2017. (Regular paper; Acceptance rate: 9.25%).
6. Drug-Drug Interactions (DDIs) Detection from On-line Health Forums: Bi-Submodular Optimization (BSMO),
   Yan Hu, Rui Wang, and Feng Chen,
   in Proceedings of the IEEE International Conference on Healthcare Informatics (ICHI 2017), 163-170, 2017.
7. Spatial Event Forecasting in Social Media with Geographically Hierarchical Regularization
   Liang Zhao, Junxiang Wang, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan
   Proceedings of the IEEE, 105(10), pages 1953--1970, 2017 (Impact Factor 9.237).
8. Effective Online Software Anomaly Detection
   Yizhen Chen, Ming Ying, Daren Liu, Adil Alim, Feng Chen, and Mei-Hwa Chen
   in Proceedings of the ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA'17), pages 136-146, 2017.
9. Can Self-Censorship in News Media be Detected Algorithmically? A Case Study in Latin America
   Rongrong Tao, Baojian Zhou, Feng Chen, Naifeng Liu, David Mares, Patrick Butler, Naren Ramakrishnan
   arXiv, preprint arXiv:1611.06947, 2017.
10. Making a Difference: Analytics for Quality Knowledge-Building Conversations
    Frank de Jong, Joan van den Ende, Hennie van Heijst, Yoshiaki Matsuzaw, Paul Kirschner, Jianwei Zhang, Mei-Hwa Chen, Feng Chen, et al.
    Philadelphia, PA: International Society of the Learning Sciences, 2017.
11. Query-Driven Discovery of Anomalous subgraphs in Attributed Graphs
    Nannan Wu, Feng Chen, Jianxin Li, Jinpeng Huai, and Bo Li
    in Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), pages 3105-3111, 2017.
12. Road Traffic Speed Prediction: A Probabilistic Model Fusing Multi-Source Data
    Lu Lin, Jianxin Li, Feng Chen, Jieping Ye, Jinpeng huai,
    IEEE Transactions on Knowledge and Data Engineering (TKDE), 2017 (Impact Factor 3.438). (To Appear)
13. Spatial Prediction for Multivariate Non-Gaussian Data
    Xutong Liu, Feng Chen, Kevin Y. Lu, and Chang-Tien Lu
    ACM Transactions on Knowledge Discovery from Data (TKDD), volume 11, issue 3, pages 36:1-36:27, 2017.
14. Tracking Multiple Social Media for Stock Market Event Prediction
    Fang Jin, Wei Wang, Prithwish Chakraborty, Nathan Self, Feng Chen, and Naren Ramakrishnan
    in Proceedings of the 2017 Industrial Conference on Data Mining (ICDM), 16-30, 2017.
15. Feature Constrained Multi-Task Learning Models for Spatiotemporal Event Forecasting
    Liang Zhao, Qian Sun, Jieping Ye, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan
    IEEE Transactions on Knowledge and Data Engineering (TKDE), volume 29, issue 5, pages 1059--1072, 2017 (Impact Factor 3.438).
16. Near Optimal and Practical Algorithms for Graph Scan Statistics
    Jose Cadena, Feng Chen, and Anil Vullikanti
    in Proceedings of the 2017 SIAM International Conference on Data Mining (SDM 2017), pages 624-632, 2017. (acceptance rate: 26%).
17. Absenteeism Detection in Social Media
    Fang Jin, Feng Chen, Rupinder Paul Khandpur, Chang-Tien Lu, and Naren Ramakrishnan
    in Proceedings of the 2017 SIAM International Conference on Data Mining (SDM 2017), pages 606-614, 2017. (acceptance rate: 26%).
18. An Efficient Approach to Event Detection and Forecasting in Dynamic Multivariate Social Media Networks
    Minglai Shao, Jianxin Li, Feng Chen, Hongyi Huang, Shuai Zhang, and Xunxun Chen
    in Proceedings of the 26th World Wide Web Conference (WWW 2017), pages 1631-1639, 2017. (acceptance rate: 17%).
19. Nearest Neighbor Query, Definition
    Feng Chen and Chang-Tien Lu
    Encyclopedia of GIS 2017, pages 1433-1440, 2017.

2016

1. Traffic Flow Prediction for Urban Networks Using a Spatio-Temporal Random Effects Model
   Yao-Jan Wu, Feng Chen, Chang-Tien Lu and Shu Yang,
   Journal of Intelligent Transportation Systems (JITS) , volume 20, issue 3, pages 282-293, 2016.
2. From Twitter to Detector: Real-time Traffic Incident Detection using Social Media Data
   Yiming Gu, Sean Qian, Feng Chen
   Transportation Research Part C: Emerging Technologies, volume 67, pages 321-342, 2016. (Impact factor: 3.805)
3. The Big Data of Violent Events: Algorithms for Association Analysis Using Spatio-Temporal Storytelling
   Raimundo F. Dos Santos, Arnold Boedihardjo, Sumit Shah, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan
   Journal of GeoInformatica, volume 20, issue 4, pages 879-921, 2016.
4. Automatic Targeted-Domain Spatiotemporal Event Detection in Twitter
   Ting Hua, Feng Chen, Liang Zhao, Chang-Tien Lu, and Naren Ramakrishnan
   Journal of GeoInformatica, volume 20, issue 4, pages 765-795, 2016.
5. Discovering Anomalies on Mixed-Type Data using a Generalized Student-t Based Approach
   Kevin Y. Lu, Feng Chen, Yating Wang, and Chang-Tien Lu
   IEEE Transactions on Knowledge and Data Engineering (TKDE), volume 28, issue 10, pages 2582-2595, 2016 (Impact Factor 3.438).
6. Online Spatial Event Forecasting in Microblogs
   Liang Zhao, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan
   ACM Transactions on Spatial Algorithms and Systems (TSAS), 2016. (To Appear)
7. Twitter Popularity Diffusion of Presidential Candidates Through Detection of Twitter Bots
   Akanksha Atrey, Aatman Togadia, and Feng Chen,
   to be presented at the 2016 IEEE MIT Undergraduate Research Technology Conference (URTC 2016), 2016. (Poster)
8. Graph-Structured Sparse Optimization for Connected Subgraph Detection
   Baojian Zhou and Feng Chen
   in Proceedings of the IEEE International Conference on Data Mining (ICDM 2016) , 2016. (Regular paper; acceptance rate: 8.4%). [Technical Report, Source Code]
9. Multi-resolution Spatial Event Forecasting in Social Media
   Liang Zhao, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan
   in Proceedings of the IEEE International Conference on Data Mining (ICDM 2016) , 2016. (Regular paper; acceptance rate: 8.4%).
10. Graph Topic Scan Statistic for Spatial Event Detection
    Yu Liu, Baojian Zhou, Feng Chen, and David W. Cheung
    in Proceedings of the 25th ACM International Conference on Information and Knowledge Management (CIKM 2016), 2016. (Long paper, acceptance rate: 17.6%).
11. Hierarchical Incomplete Multisource Feature Learning for Spatiotemporal Event Forecasting
    Liang Zhao, Jieping Ye, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan
    in Proceedings of the 22st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'16), pages 2085-2094, 2016.
12. A Generalized Matching Pursuit Approach for Graph-Structured Sparsity
    Feng Chen and Baojian Zhou
    in Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI'16), pages 1389-1395, 2016. [Technical Report, Source Code]
13. Efficient Nonparametric Subgraph Detection using Tree Shaped Priors [PDF]
    Nannan Wu, Feng Chen, Jianxin Li, Baojian Zhou, Naren Ramakrishnan
    in Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI'16), 2016.
14. Topical Analysis of Interactions between News and Social Media [PDF]
    Hua Ting, Yue Ning, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan
    in Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI'16), 2016.

2015

1. Examining political mobilization of online communities through e-petitioning behavior in We the People
   Catherine L. Dumas, Daniel LaManna, Teresa M. Harrison, S. S. Ravi, Christopher Kotfila, Norman Gervais, Loni Hagen, and Feng Chen
   Journal of Big Data & Society, volume 2, issues 2, pages 1-20, 2015.
2. Fast Adaptive Kernel Density Estimators for Data Stream [PDF]
   Arnold P. Boedihardjo, Chang-Tien Lu, Feng Chen
   in Knowledge and Information Systems: An International Journal (KAIS), pages 1-33, Springer, 2015.
3. Human rights event detection from heterogeneous social media graphs [PDF]
   Feng Chen and Daniel B. Neill
   in Big Data Journal, volume 3, issue 1, pages 34-40, 2015.
4. A framework for intelligence analysis using spatio-temporal storytelling [PDF]
   Raimundo F. Dos Santos, Sumit Shah, Arnold Boedihardjo, Feng Chen, Chang-Tien Lu, Patrick Butler, Naren Ramakrishnan
   Journal of GeoInformatica, pages 1-42, 2015.
5. How Events Unfold: Spatiotemporal Mining in Social Media
   Ting Hua, Liang Zhao, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan,
   ACM SIGSPATIAL Newsletter, volume 7, issue 3, pages 19-25, 2015.
6. Dynamic Theme Tracking in Twitter [PDF]
   Liang Zhao, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan
   in Proceedings of the IEEE International Conference on Big Data (BigData'15), pages 561-570, 2015.
7. SimNest: Social Media Nested Epidemic Simulation via Online Semi-supervised Deep Learnin [PDF]
   Liang Zhao, Jiangzhuo Chen, Feng Chen, Wei Wang, Chang-Tien Lu, and Naren Ramakrishnan
   in Proceedings of the IEEE International Conference on Data Mining (ICDM'15), pages 639-648, 2015.
8. Multi-task learning for spatio-temporal event forecasting [PDF]
   Liang Zhao*, Qian Sun* (co-first author), Jieping Ye, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan
   in Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'15), pages 1503-1512, 2015.
9. Spatiotemporal Event Forecasting in Social Media [PDF]
   Liang Zhao, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan
   in Proceedings of the 2015 Siam International Conference on Data Mining (SDM'15), pages 963-971, 2015.

2014

1. Non-Parametric Scan Statistics for Disease Outbreak Detection on Twitter [PDF]
   Feng Chen, Daniel B. Neill
   in Online Journal of Public Health Informatics, volume 6, issue 1, 2014.
2. Analyzing Civil Unrest through Social Media [PDF]
   Ting Hua, Chang-Tien Lu, Naren Ramakrishnan, Feng Chen, Jaime Arredondo, David Mares, and Kristen Summers
   in Computer Magazine, volume 46, issue 12, pages 80-84, 2013.
3. SpecMonitor: Towards Efficient Passive Traffic Monitoring for Cognitive Radio Networks [PDF]
   Qiben Yan, Ming Li, Feng Chen, Tingting Jiang, Weijing Lou, Y. Thomas Hou, Chang-Tien Lu
   in IEEE Transactions on Wireless Communication (TWC), 2014.
4. Unsupervised Spatial Event Detection in Targeted Domains with Applications to Civil Unrest Modeling [PDF]
   Liang Zhao, Feng Chen, Jing Dai, Ting Hua, Chang-Tien Lu, and Naren Ramakrishnan
   in PLOS ONE, volume 9, issue 10: e110206. doi:10.1371/journal.pone.01102062014, 2014.
5. Road Traffic Congestion Monitoring in Social Media with Hinge-Loss Markov Random Fields [PDF]
   Po-Ta Chen, Feng Chen, Zhen(Sean) Qian
   in Proceedings of the IEEE International Conference on Data Mining (ICDM'14), pages 80-89, 2014.
6. Forecasting Location-based Events with Spatio-temporal Storytelling [PDF]
   Ray Dos Santos, Sumit Shah, Feng Chen, Arnold Boedihardjo, Chang-Tien Lu, Naren Ramakrishnan
   in Proceedings of the 7th ACM SIGSPATIAL International Workshop on Location-Based Social Networks (LBSN'14), 2014.
7. Non-Parametric Scan Statistics for Event Detection and Forecasting in Heterogeneous Social Media Graphs [PDF]
   Feng Chen and Daniel B. Neill
   in Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'14), pages 1166-1175, 2014.
8. Modeling Mass Protest Adoption in Social Network Communities using Geometric Brownian Motion [PDF]
   Fang Jin, Rupinder Khandpur, Nathan Self, Edward Dougherty, Feng Chen, B. Aditya Prakash, and Naren Ramakrishnan
   in Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'14), pages 1660-1669, 2014.
9. Beating the news’ with EMBERS: Forecasting Civil Unrest using Open Source Indicators [PDF]
   With Naren Ramakrishnan and Others
   in Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'14), pages 1799-1808, 2014.

2013

1. On Detecting Spatial Categorical Outliers [PDF]
   Xutong Liu, Feng Chen, Chang-Tien Lu
   Journal of GeoInformatica, volume 18, issue 3, pages 501-536, 2013.
2. A Carpooling REcommendation System Based on Social Vanet and Geo-Social Data [PDF]
   Ahmed Elbery, Mustafa EINainay, Chang-Tien Lu, Feng Chen, and Jeffrey Kendall
   in Proceedings of the 21th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM-GIS'13), 2013.
3. Learning Thread Reply Structure on Patient Forums [PDF]
   Yunzhong Liu, Feng Chen, Yi Chen
   in Proceedings of the International Workshop on Data management and Analytics for Healthcare (DARE'13), CIKM, 2013.
4. Automatic Event Detection and Storytelling in Social Media [PDF]
   Feng Chen
   NSF Workshop on Knowledge Discovery in Cyberspace and Big Data, 2013.
5. STED: Semi-Supervised Targeted Event Detection [PDF]
   Ting Hua, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan
   inProceedings of the 19th ACM SIGKDD Conference on Knowledge Discovery and Data (KDD'13), Demo Track, 2013.
6. A Generalized Student-t Based Approach to Mixed-Type Anomaly Detection [PDF]
   Yen-Cheng Lu, Feng Chen, Yang Chen, Chang-Tien Lu
   in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI'13), 2013.
7. Optimal Network Traffic Surveillance in Cognitive Radio Networks [PDF]
   Qiben Yan, Ming Li, Feng Chen, Tingting Jiang, Wenjing Lou, Chang-Tien Lu
   in Proceedings of the 32nd IEEE International Conference on Computer Communications (INFORCOM'13), pages 1240-1248, 2013.

2012

1. Student-t Based Robust Spatio-Temporal Prediction [PDF]
   Yang Chen, Feng Chen, Jing Dai, T. Charles Clancy
   in Proceedings of the IEEE International Conference on Data Mining (ICDM'12), pages 151-160, 2012.
2. Robust Inference and Outlier Detrection for Large Spatial Data Sets [PDF]
   Xutong Liu, Feng Chen, Chang-Tien Lu
   in Proceedings of the IEEE International Conference on Data Mining (ICDM'12), pages 469-478, 2012.
3. Signal Disaggregation via Sparse Coding with Featured Discriminative Dictionary [PDF]
   Bingsheng Wang, Feng Chen, Haili Dong, Arnold Boedihardjo, and Chang-Tien Lu
   in Proceedings of the IEEE International Conference on Data Mining (ICDM'12), pages 1134-1139, 2012.
4. Spatial Surrogates to Forecast Social Mobilization and Civil Unrests [PDF]
   Feng Chen, Jaime Arredondo, Rupinder Paul Khandpur, Chang-Tien Lu, David Mares, Dipak Gupta, and Naren Ramakrishnan
   in Computing Community Consortium (CCC) Workshop on "From GPS and Virtual Globes to Spatial Computing-2012," 2012
5. Traffic Flow Prediction for Urban Network using Spatial Temporal Random Effects Model [PDF]
   Yao-Jan Wu, Feng Chen, Chang-Tien Lu, Brian Smith, Yang Chen
   the 91st Annual Meeting of the Transportation Research Board (TRB'12), 2012

2011

1. Spatial Categorical Outlier Detection: Pair Correlation Function Based Approach [PDF]
   Xutong Liu, Feng Chen, and Chang-Tien Lu
   in Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (GIS'11), pages 465-468, 2011
2. Multi-granular Demand Forecasting in Smarter Water [PDF]
   Jing Dai, Ming Li, Sambit Sahu, Milind Naphade, Feng Chen
   in Proceedings of the 13th International Conference on Ubiquitous Computing (Ubicomp'11), pages 595-596, 2011
3. Activity Analysis Based on Low Sample Rate Smart Meters [PDF]
   Feng Chen, Jing Dai, Bingsheng Wang, Sambit Sahu, Milind Naphade, Chang-Tien Lu
   in Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'11), pages 240-248, 2011
4. On Path Anomaly Detection in a Large Transportation Network [PDF]
   Qifeng Lu, Feng Chen, Kathleen Hancock
   in Journal of Computers, Environment and Urban Systems (JCEU), volume 33, pages 448-462, 2009.

2010

1. GLS-SOD: A Generalized Local Statistical Approach for Spatial Outlier Detection [PDF]
   Feng Chen, Chang-Tien Lu, Arnold P. Boedihardjo
   in Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'10), pages 1069-1078, 2010
2. Regional Behavior Change Detection via Local Spatial Scan [PDF]
   Jing Dai, Feng Chen, Sambit Sahu, Milind Naphade
   in Proceedings of the 18th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (GIS'10), 2010
3. Spatial Outlier Detection: Random Walk Based Approaches [PDF]
   Xutong Liu, Chang-Tien Lu, Feng Chen
   in Proceedings of the 18th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (GIS'10), 2010

2008

1. On Detecting Spatial Outliers [PDF]
   Dechang Chen, Chang-Tien Lu, Yufeng Kou, Feng Chen
   in Journal of Geoimformatica, volume 12, pages 455-475, 2008.
2. On Locally Linear Classification by Pair-wise Coupling [PDF]
   Feng Chen, Chang-Tien Lu, Arnold P. Boedihardjo
   in Proceedings of the IEEE International Conference on Data Mining (ICDM'08), pages 749-754, 2008
3. A Framework for Estimating Complex Probability Density Structures in Data Streams [PDF]
   Arnold P. Boedihardjo, Chang-Tien Lu, Feng Chen
   in Proceedings of the ACM 17th Conference on Information and Knowledge Management (CIKM'08), pages 619-628, 2008
4. HOMES: Highway Operations and Monitoring and Evaluation System [PDF]
   Chang-Tien Lu, Arnold P. Boedihardjo, David Dai, Feng Chen
   in Proceedings of the ACM 16th International Conference on Advances in Geographic Information Systems (GIS'08), pages 529-530, 2008
5. Nearest Neighbor Query [PDF]
   Feng Chen and Chang-Tien Lu
   Encyclopedia of Geographical Information Science (1st Edition), Springer-Verlag, pages 776-781, 2008
6. An Entropy-Based Method for Assessing the Number of Spatial Outliers [PDF]
   Xutong Liu, Chang-Tien Lu, and Feng Chen
   in Proceedings of the IEEE International Conference on Information Reuse and Integration (IRI'08), pages 244-249, 2008

Book Sections

Feng Chen and Chang-Tien Lu, "Nearest Neighbor Query," Encyclopedia of Geographical Information Science (1st Edition), Springer-Verlag, pages 776-781, 2008

Patents

1. "Behavior Change Detection," with Jing Dai, Sambit Sahu, Milind Naphade, IBM, T.J. Waston, 2012
2. "Utility Consumption Disaggregation Through Human Activity Association," with Jing Dai, Sambit Sahu, Milind Naphade, IBM, T.J. Waston, 2012

Grants

1. University-Principal Investigator, IARPA, ``Hidden Activity Signal and Trajectory Anomaly Characterization,'', Duration 06/1/2023 - 05/30/2027, $833,514
2. Principal Investigator (with Yifei Lou from University of North Carolina at Chapel Hill), National Science Foundation (NSF), ``ATD: Sparse and Localized Graph Convolutional Networks for Anomaly Detection and Active Learning,'' DMS-2220574, Duration 07/01/2023 - 06/30/2026, $100,000
3. Principal Investigator (with Latifur Khan from UT Dallas, Xintao Wu from University of Arkansas, and Christan Grant from University of Oklahoma), National Science Foundation (NSF), ``FAI: A novel paradigm for fairness-aware deep learning models on data streams,'' FAIN-2147375, Duration 09/2022 - 08/2025, $628,789 (NSF portion: $392,993; Amazon portion: $235,796)
4. Lead Principal Investigator (with Jin-Hee Cho from Virginia Tech and Dong Hyun Jeong from University of the District of Columbia), National Science Foundation (NSF), ``III: Medium: Collaborative Research: MUDL: Multidimensional Uncertainty-Aware Deep Learning Framework,'' IIS-2107449, Duration 10/2021 - 09/2025, $1.2 million (my share: $499,955)
5. Principal Investigator (with Haipeng Cai from Washington State University (Lead PI)), Army Research Office (ARO), ``Advancing the Knowledge about Software Vulnerability Analysis via Large-Scale Benchmarking,'' Duration 01/2021-12/2023, $570,000 (my share: $265,697)
6. Lead Principal Investigator (with Xiaohua Tony Hu from Drexel University), National Science Foundation (NSF), ``A novel paradigm for detecting complex anomalous patterns in multi-modal, heterogeneous, and high-dimensional multi-source data sets,'' IIS-1815696, Duration 09/2018 - 09/2021, $499,718 (my share: $249,989)
7. Principal Investigator, National Science Foundation (NSF), ``CAREER: SPARK: A Theoretical Framework for Discovering Complex Patterns in Big Attributed Networks,'' IIS-1750911, Duration 05/01/2018 - 05/01/2023, $537,044
8. Co-Principal Investigator (with Jianwei Zhang, Mei-Hwa Chen, Marlene Scardamalia, Carolyn Rose), National Science Foundation (NSF), ``Connecting Idea Threads across Communities for Sustained Knowledge Building,'' Duration: 09/2014 - 08/2018, $1,342,537.
9. Principal Investigator (with Doug DeGroot and Gopal Gupta), Vistra Corporate Services Company, ``Advanced Analytics and Deep Learning for the Power Generation and Distribution Industry,'' Duration 01/2020-09/2020, $330,864
10. Co-Principal Investigator (with Tolga Soyata, Ming-Ching Chang, Chinwe Ekenna, Yelin Kim, Jeong-Hyon Hwang), SUNY Center-Scale Planning and Development Grant Program, ``A General, Intelligent, Bio-Inspired Computing Framework for Sensor Networks,'' duration 6/2018 to 1/2019, $25,000
11. Principal Investigator, SUNY-A Faculty Research Award, ``A New Nonparametric and Deep Learning Framework for Anomalous Pattern Detection in Heterogeneous Multi-Source Data,'' Duration 2018-2021, $10,000.
12. Principal Investigator, NIH Small Business Innovation Research (SBIR) Program, ``An Integrated, Open-source, Web Platform for Continuous Chronic Disease Surveillance on Social Media,'' Duration 9/20/2017 - 3/20/2018, $50,000.
13. Principal Investigator, Army Research Office (ARO), ``Uncertainty Management for Dynamic Decision Making,'' Duration 9/1/2017 - 12/30/2019, $330,000.
14. Co-Principal Investigator (with Jeong-Hyon Hwang and Petko B Bogdanov), UAlbany University Presidential Innovation Fund for Research and Scholarship (PIFRS) program, ``A Systems-Oriented Framework for Mining Complex Subgraphs,” Duration: 02/01/2017 -1/30/2018, $41,260.
15. Principal Investigator (with Ozlem Uzuner from George Manson Univ.), Strategic Partnership for Industrial Resurgence (SPIR) Program at UAlbany, ``Data analytics on market insights from online news," Duration 5/1/17 - 7-30/2018, $92,000
16. Co-Principal Investigator (with Catherine Lawson), US Department of Transportation (DOT), ``Techniques for Efficient Detection of Rapid Weather Changes and Analysis of their Impacts on a Highway Network,'' Duration 09/01/16 - 0901/17, $71,684.
17. Principal Investigator, SUNY-B Faculty Research Award, ``Non-Parametric Graph Scan for Event Detection,''; 06/2014 – 04/2015, $3,324.
18. University-Principal Investigatorr, IARPA, ``Early Model Based Event Recognition using Surrogates,'' 04/2015 – 06/2016, $120,000.
19. University-Principal Investigator, IARPA, ``Early Model Based Event Recognition using Surrogates,'' 06/2014 – 04/2015, $124,861.
20. Co-Principal Investigator (with Catherine Lawson, Jeong-Hyon Hwang, S. S. Ravi), US Department of Transportation (DOT), ``Techniques for Information Extraction from Compressed GPS Traces,'' 03/2014 - 02/2015, $100,000.

Services

1. Associate Editor
   ACM Transactions on Knowledge Discovery from Data, 2023 to present
2. Associate Editor
   Frontier in Big Data, 2021 to present
3. Publication Chair
   IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Nov. 13-15, Arlington, VA, 2006
4. Session Chairs
   2015 Siam International Conference on Data Mining (SDM); 2015 IEEE International Conference on Data Mining (ICDM).
5. PC Members
   KDD-16, ICDM-15, SDM-15, 16, IJCAI-13, UAI Bayesian Applications Workshop (BIBW-14, 15), IEEE International Workshop on Trust, Security and Privacy for Big Data (TrustData-14), Mobile Sensing, Mining and Visualization for Human Behavior Inference Workshop (MSMV-MBI-14), Digital Forensics Experiments and Results (DiFER-15), IEEE International Workshop on Big Data in Computational Epidemiology (BDCE-15), ACM International Workshop on Data Mining for Brain Science (BrainKDD-15).
6. Journal Reviewer
   Information Science, IEEE Transactions on Knowledge and Data Engineering (TKDE), Computer Magazine. ACM Transactions on Knowledge Discovery from Data, IEEE Transaction on Big Data, Geoinformatica, Transactions on Intelligent Systems and Technology, Transactions on Services Computing, Knowledge and Information Systems, Journal of Meteorological Research
7. Tutorials
   Feng Chen, Petko Bogdanov, Daniel B. Neill, and Ambug K. Singh, “Anomalous and Significant Subgraph Detection in Attributed Networks,” 2016 IEEE International Conference on Big Data (IEEE Big Data 2016).
8. NSF Service
   NSF Review Panelist CISE (2016-03), NSF proposal review (2015).

Honors and Awards

1. Outstanding research and service contributions, CS department, UTD, 2022
2. Outstanding research and service contributions, CS department, UTD, 2021
3. CAREER Award, National Science Foundation, 2018
4. Faculty Research Award, SUNY, 2018
5. Faculty Research Award, SUNY, 2014
6. 2nd Place Poster Prize in Security Category, National Academy of Engineering Grand Challenges Summit Contest, Mar. 2-3, Durham, NC, 2009
7. 2st Place Research Poster Prize, Virginia Tech Northern Capital Region Graduate Research Symposium, Mar. 23-27, Falls Church, VA, 2009
8. Service Excellence Award as Publication Chair, IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Nov. 13-15, Arlington, VA, 2006