Dr. Qing Wang is a machine learning researcher whose work focuses on sequential decision-making, reinforcement learning, causal inference, and large language models.

Dr. Qing Wang is an Honorary Research Associate in the Department of Statistics and Actuarial Science at The University of Hong Kong. She earned her Ph.D. in 2018 from School of Computing and Information Sciences at Florida International University under the supervision of Distinguished University Professor, Ryder Professor & Director S. S. Iyengar. Prior to joining HKU, Dr. Wang conducted industrial research at IBM Research. Her research has received multiple recognitions, including the IBM Research Accomplishment Award, the IBM Outstanding Technical Achievement Award, the FIU Overall Outstanding Graduate Student Award, and the IEEE SCC Best Student Paper Award. Her long-term research vision is to develop trustworthy, efficient, and adaptive AI systems capable of learning from limited feedback and making intelligent decisions in complex real-world environments.

More details can be found in her CV and Google Scholar (an h-index of 14).

Jun. 25th, 2026, I'll be giving a talk at the 30th Center for Advanced Signal and Image Sciences (CASIS) Workshop hosted by Lawrence Livermore National Laboratory (LLNL) [Talk] 🎉

Jun. 21rd, 2026, Excited to share that I have been elevated to IEEE Senior Member! 🎉

Mar. 13th, 2026, we've filed a new patent on circuit generation in collaboration with IBM colleagues from MIT-IBM Watson AI Lab. 🎉

Jul. 14th, 2025, I’m happy to share that I’m starting a new position as Honorary Research Associate at the School of Computing and Data Science, The University of Hong Kong! 🎉

Dec. 10th, 2024, Advancing Graph AI with Scientific and Business Impacts has been selected as 2024 IBM Outstanding Acommplishments project (14/280 submissions). 🎉

Mar. 16th, 2024, we presented our work "GRACE: Generating Cause and Effect of Disaster Sub-Events from Social Media Text" at WWW2024! [Talk] 🎉

Jul. 11th, 2023, IBM watsonx GA'd today. AIOps is an IBM AI Product using wastonx's foundation models [IT Automation]!

Jun. 28th, 2023, our patent "Anomaly Detection Using Event Sequence Prediction" using both generative AI and reinforcement learning has been filed! 🎉

Jun. 2th, 2023, I've given a talk about "Generative AI". [slides]

Mar. 13th, 2023, I've become a member of IBM Patent Review Committees of AI-Reinforcement Learning Track, (IBM IDT).

Dec. 12th, 2022, our paper "Clustering of Bandit with Frequence-Dependent Information Sharing" has been accepted by The 45th European Conference on Information Retrieval (ECIR-23) , Dublin, Ireland. 🎉

Oct. 3rd, 2022, our paper "Fault Injection based Interventional Causal Learning for Distributed Applications" has been accepted by The Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI-23) , Washington, DC, USA. 🎉

Sep. 22nd, 2022, Electrolux uses AIOps to enhance automation, IBM News.

• Honorary Research Associate, School of Computing and Data Science, The University of Hong Kong, CA, USA (Jul. 2025 - Present)

  Research Interests: Large Language Models, Large-scale Data Mining, Multi-armed Bandits, Causal Inference, Generative Models

• IBM Thomas J. Watson Research Center, Postdoctoral Researcher at Cognitive Service Foundation Team, Yorktown Heights, NY, USA (Mar. 2019 - Nov. 2021)

  I have been working on AIOps since its early stages in 2016, with a focus on extracting actionable insights from large-scale operational data. My research addresses a fundamental challenge: How can we infer system behavior and identify root causes without full instrumentation or explicit knowledge of system structure?

  (1) Log-based system understanding
  Modeling unstructured log data to detect anomalies and infer latent system states.

  (2) Causal structure learning for microservices
  Inferring service dependencies and causal relationships directly from observational data, enabling root cause analysis without requiring manual instrumentation.

  (3) Data-driven diagnosis frameworks
  Integrating heterogeneous signals—such as logs, metrics, and alerts—for comprehensive incident analysis.

• IBM Thomas J. Watson Research Center, Research Intern at Cognitive Service Foundation Team, Yorktown Heights, NY, USA (May 2018 - July 2018)

  (1) Proposed and implemented learning Models for AI Skills Orchestration: aiming to utilize bandit algorithms for interactive skills planning.

  (2) Developed a learning Logical Representations model of Natural Languages with Little Supervision: aiming to learn the logic forms from human language using deep learning and reinforcement learning.

  (3) Patent: System and Method to Assess Technical Risk in IT Service Management Using Visual Pattern Recognition.

  (4) Paper 1: Learning Logical Representations from Natural Languages with Weak Supervision and Back Translation, NIPS Workshop, 2019.

  (5) Paper 2: AISTAR: an intelligent system for online IT ticket automation recommendation, IEEE BigData, 2018.

• IBM Thomas J. Watson Research Center, Research Intern at Cognitive Service Foundation Team, Yorktown Heights, NY, USA (May 2017 - July 2017)

  (1) Proposed and modeled the automation recommendation procedure of IT automation services as a contextual bandit problem with dependent arms, where the arms are organized in the form of hierarchies.

  (2) Paper publication: Online it ticket automation recommendation using hierarchical multi-armed bandit algorithms, SDM 2018.

• IBM Thomas J. Watson Research Center, Research Intern at Cognitive Service Foundation Team, Yorktown Heights, NY, USA (June 2016 - August 2016)

  (1) Proposed and implemented an integrated framework for constructing the domain knowledge base, which is capable of extracting the domain informative phrases from unstructured text snippets.

  (2) Paper publication: Constructing the knowledge base for cognitive IT service management, IEEE SCC, 2017, Best Student Paper Award.

• Knowledge Discovery Research Group, Discovery Lab, School of Computing and Information Science, FIU, Aug. 2014 - Dec. 2018
• Log Mining System. It is an online mining system including a pre-processing module for raw log data, a visualization module for log data with 2D and 3D presentation, algorithms to mine association or temporal relationships for log events.
• Multi-armed Bandit Models. Bandit models are very popular applying into various interacitve recommender systems. I am interested in modeling the interactive behaviors between users and items to learn user's preference. [slides]
• AI + Healthcare Course Project [project link]

• IBM Silicon Valley Lab, Senior Data Scientist, IBM, San Jose, CA, USA (Nov. 2021 - May. 2025)

  AI Capability for Data Privacy Governance (AI-powered governance/privacy on unstructed/structure data of IBM enterprise).

  (1) Personal Information (PI) Detection based on Bert. (Sequential Labeling)

• Sogou, Inc., Java Software Engineering, Beijing, Beijing, China (July 2013 - July 2014)

  Ensure the quality of User Passport services related to login and registration across all Sogou products. Primarily responsible for developing and promoting both interfaceand GUI automation testing frameworks using Java and Spring Framework.

• IBM SPSS, Java Software Engineering Intern, Xi'an, Shaanxi, China (August 2011 - June 2012)

  Designed and implemented an automatic testing framework for CADS platform using IBM Rational Functional Tester. This framework can help customizing test cases with XML configuration file, and execute them automatically.

1. Session Chair of 2018 IEEE Big Data. (Dec.,2018).

2. Session Chair of 2021 ICSOC. (Nov.,2021).

3. Reviewer, SIGKDD, CIKM, ICDM, ECIR, AAAI, IJCAI, EMNLP., 2016-2026.

4. PC member 2020, CIKM, DSHealth (SIGKDD Workshop), DLG (SIGKDD Workshop).

5. PC member 2021, AAAI, CIKM, DSHealth (SIGKDD Workshop), ICSOC, TNSM.

6. PC member 2022, AAAI, CIKM, PKDD, DSHealth (SIGKDD Workshop).

7. PC member 2023, CIKM, Journal of Artificial Intelligence Research (JAIR).

8. PC member 2024, CIKM, Journal of Artificial Intelligence Research (JAIR).

9. PC member 2026, ICDM.

1. "An Integrated Solution for Ontology-based Ticket Recommendation Using Problem Inference," IBM Intern Workshop, Yorktown Heights, NY, Jun. 20-23, 2016.

2. "STAR: A System for Ticket Analysis and Resolution," SIGKDD, Halifax, Canada, Aug.13-17, 2017.

3. "AISTAR: An Intelligent Integrated System for Online IT Ticket Automation Recommendation", oral presentation, IEEE BigData 2018, Seattle, WA, USA, Dec., 2018.
4. "Taking Digital Service by Storm: from Traditional AI to Deep AI", 'Five Minute Madness' presentation, 2019 IBM Northeast Region Academy Affiliates Face-to-Face Meeting. (Jun.,2019).

5. "Detecting Causal Structure on Cloud Application Microservices Using Granger Causality Models", IEEE CLOUD 2021. (Sep.,2021).

6. "Fault-injection based Causal Learning for Cloud Applications", SREconference@IBM 2021. (Nov.,2021).

7. "GRACE: Generating Cause and Effect of Disaster Sub-Events from Social Media Text", Demo presentation. WWW 2024, Singapore, Singapore. (May.,2024).

8. "Graph Grammar Learning for Automatic Circuit Generation", Lawrence Livermore National Laboratory (LLNL)’s 30th Center for Advanced Signal and Image Sciences (CASIS), Livermore, CA, USA (June.,2026).

Journal Articles

1. Qing Wang " Intelligent Data Mining Techniques for Automatic Service Management. " Dissertation, 2018.

2. Qing Wang " Quantum Neural Networks: Research and Applications in Weather Prediction. " Master Thesis, 2013.

3. Qing Wang, Chunqiu Zeng, Wubai Zhou, Tao Li, S. S. Iyengar, Larisa Shwartz, Genady Y. Grabarnik " Online Interactive Collaborative Filtering Using Multi-armed Bandit with Dependent Arms ", In IEEE Transactions on Knowledge and Data Engineering (TKDE), 2018. [paper]

4. Qifeng Zhou, Xiang Liu, Qing Wang " Interpretable Duplicate Question Detection Models based on Attention Mechanism ", Information Sciences, 2020. [paper]

5. Tao Li, Chunqiu Zeng, Wubai Zhou, Wei Xue, Yue Huang, Zheng Liu, Qifeng Zhou, Bin Xia, Qing Wang, Wentao Wang, Xiaolong Zhu "FIU-Miner (A Fast, Integrated, and User-Friendly System for Data Mining) and Its Applications", In Knowledge and Information Systems(KAIS), 2016.

Conference Papers

1. Xinxi Jiang, Xiang Li, Qifeng Zhou, Qing Wang, "GRACE: Generating Cause and Effect of Disaster Sub-Events from Social Media Text", The 2024 ACM Web Conference (WWW-24), Singapore, Singapore, 2024.

2. Shen Yang, Qifeng Zhou, Qing Wang, "Clustering of Bandit with Frequence-Dependent Information Sharing", 45th European Conference on Information Retrieval (ECIR-23), Dublin, Ireland, 2023.

3. Qing Wang, Jesus Rios Aliaga, Karthikeyan Shanmugam, et. al "Fault Injection based Interventional Causal Learning for Distributed Applications", 37th AAAI Conference on Artificial Intelligence (AAAI-23), 2023. [paper]

4. Qing Wang " The Use of Bandit Algorithms in Intelligent Interactive Recommender Systems, " arXiv preprint arXiv:2107.00161, 2021. [paper]

5. Qing Wang, et. al "Detecting Causal Structure on Cloud Application Microservices Using Granger Causality Models", 14th International Conference on Cloud Computing (IEEE CLOUD 2021). [paper][slides][code]

6. Pooja Aggarwal, Seema Nagar, Ajay Gupta, Larisa Shwartz, Prateeti Mohapatra, Amit Paradkar, Qing Wang, Atri Mandal, "Causal Modeling based Fault Localization in Cloud Systems using Golden Signals", 14th International Conference on Cloud Computing (IEEE CLOUD 2021).

7. Jinhon Hwang, Larisa Shwartz, Qing Wang, Raghav Batta, Harshit Kumar and Michael Nidd "FIXME: Enhance Software Reliability with Hybrid Approaches in Cloud", 43rd International Conference on Software Engineering. (ICSE SEIP 2021). [paper]

8. Vijay Arya, Karthikeyan Shanmugam, Pooja Aggarwal, Qing Wang, Prateeti Mohapatra and Seema Nagar "Evaluation of Causal Inference Techniques for AIOps", 8th ACM IKDD CODS and 26th COMAD (CODS-COMAD 2021).

9. Pooja Aggarwal, Ajay Gupta, Prateeti Mohapatra, Seema Nagar, Atri Mandal,Qing Wang, Amit Paradkar "Localization of Operational Faults in Cloud Applications by Mining Causal Dependencies in Logs using Golden Signals", AIOPs of the 18th International Conference on Service-Oriented Computing (ICSOC 2020).

10. Qing Wang, Larisa Shwartz, Genady Ya. Graharnik, Michael Nidd, Jinho Hwang, "Leveraging AI in Service Automation Modeling: from Classical AI Through Deep Learning to Combination Models", the 17th International Conference on Service-Oriented Computing (ICSOC 2019). Springer, Toulouse, France, 2019. (acceptance rate:15%) [paper][slides]

11. Kaylin Hagopian, Qing Wang, Yupeng Gao, Tengfei Ma, Lingfei Wu."" Learning Logical Representations from Natural Languages with Weak Supervision and Back Translation", " Knowledge Representation & Reasoning Meets Machine Learning Workshop at NeurIPS, Vancouver, Canada, 2019.

12. Qing Wang, Chunqiu Zeng, S. S. Iyengar, Tao Li, Larisa Shwartz, Genady Ya. Graharnik, "AISTAR: An Intelligent Integrated System for Online IT Ticket Automation Recommendation", In the proceedings of the 6th annual IEEE International Conference on Big Data (IEEE BigData 2018), Seattle, WA, USA 2018.[paper]

13. Qing Wang, S. S. Iyengar, Tao Li, Larisa Shwartz, Genady Ya. Graharnik "Online IT automation recommendation Using Hierarchical Multi-armed Bandit Algorithms", SIAM International Conference on Data Mining (SDM), 2018.[paper] [slides]

14. Wubai Zhou, Wei Xue, Ramesh Baral, Qing Wang, Chunqiu Zeng, Tao Li, Jian Xu, Zheng Liu, Larisa Shwartz, Genady Ya.Grabarnik, "STAR: A System for Ticket Analysis and Resolution", In the proceedings of the 23nd annual ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2017.[paper][poster]

15. Wei Xue, Wubai Zhou, Tao Li, Qing Wang, "MTNA: A Neural Multi-Task Model for Aspect Category Classification and Aspect Term Extraction on Restaurant Reviews ", In the proceeding of the 8th International Joint Conference on Natural Language Processing (IJCNLP), 2017.

16. Qing Wang, Wubai Zhou, Chunqiu Zeng, Tao Li, Larisa Shwartz, Genady Ya.Grabarnik, "Constructing the Knowledge Base for Cognitive IT Service Management ", In the proceedings of the 14th IEEE International Conference on Services Computing (SCC), 2017. [Best Student Paper Award][paper][slides]

17. Chunqiu Zeng, Qing Wang, Wentao Wang, Tao Li, Larisa Shwartz, " Online Inference for Time-Varying Temporal Dependency Discovery from Time Series ", In the proceedings of the 4th annual IEEE International Conference on Big Data(IEEE Big Data), 2016. Regular Research Paper (acceptance rate: 18.68%)
18. Tao Li, Wubai Zhou, Chunqiu Zeng, Qing Wang, Qifeng Zhou, Dingding Wang, Jia Xu, Yue Huang, Wentao Wang, Minjing Zhang, Steve Luis, Shu-Ching Chen, Naphtali Rishe, "DI-DAP: An Efficient Disaster Information Delivery and Analysis Platform in Disaster Management", In Proceedings of the 25th ACM Conference on Information and Knowledge Management (CIKM 2016), Indianapolis, US, Oct.2016. Full paper at industrial track(acceptance rate:22/111=19.8%)

19. Chunqiu Zeng, Qing Wang, Shekoofeh Mokhtari, Tao Li,"Online Context-Aware Recommendation with Time Varying Multi-Armed Bandit ", In the proceedings of the 22nd annual ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2016. Research Track (acceptance rate:142/784=18%) [paper,video].

Selected Patents

1. Graph Grammar Learning for Automatic Circuit Generation, to be filed.
2. Anomaly Detection Using Event Sequence Prediction.
3. Fault localization in a distributed computing system, 2025.
4. Shiftleft topology construction and information augmentation using machine learning, 2025. [High Value Patent]
5. Just in time assembly of transactions, 2024.
6. Learning Causal Relationships, 2023.
7. Transformation of data from legacy architecture to updated architecture, 2023.
8. Synthetic system fault generation, 2023.
9. Automatic mapping of records without configuration information, 2023.
10. Computing system event error corrective action recommendation, 2022.
11. Linking operational events with system changes, 2022.
12. Application topology discovery, 2022. [High Value Patent]
13. Cross-Environment Event Correlation Using Domain-Space Exploration and Machine Learning Techniques, 2022. [High Value Patent]
14. Assessing technical risk in information technology service management using visual pattern recognition, 2022.