Qing Wang earned her Ph.D. in 2018 from School of Computing and Information Sciences at Florida International University under the supervision of Eminent Scholar Chaired Professor [Tao Li], Distinguished University Professor, Ryder Professor & Director S. S. Iyengar and Associate Director & Eminent Scholar Chaired Professor Shu-Ching Chen. Prior to her doctoral studies, she obtained her Master and Bachelor degree in Computer Science from Xidian University in 2013 and Zhengzhou University in 2009. She was the recipient of 2018 FIU SCIS Overall Outstanding Graduate Student. She also received the Best Student Paper Award in 2017 from IEEE SCC. She joined IBM Research as a postdoctoral researcher in 2019 and continued her research on cognitive IT service management. She was awarded IBM Outstanding Technical Achievement Award, IBM Research Accomplishment Award in 2021 for her great contribution to IBM Waston AIOps Launch . Qing's research focuses on large-scale data mining, interactive recommender system [irs_slides], bandit models [slides] and automatic IT service management. Her dissertation is "Intelligent Data Mining Techniques for Automatic Service Management". [slides]

More details can be found in her CV and Google Scholar.

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! [Speaker] 🎉

Dec. 1st, 2023, I've delivered a presentation with demo about "Retrieval-augmented Generation" on Q&A of 2022 FIFA World Cup. [slides]

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.

• 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]
• Memory of FIU: ML course project with friends: [project link]

• IBM Silicon Valley Lab, Senior Data Scientist at AI Capability Team, IBM Enterprise Data, San Jose, CA, USA (Dec. 2021 - Present)

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

  (1) Personal Information (PI) Detection. (Sequential Labeling)

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

  I was responsible for designing and developing AI models for Watson AIOps (e.g., event grouping model, fault localization model). Our elite research team have published more than 10+ top conferece papers and 10+ patents. (Publications)

  (1) event grouping summarization, event correlation

  (2) fault localization, root cause analysis

  (3) fault generation, fault injection

• 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.

• IBM SPSS, 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., 2016-2019.

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).

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. "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).

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

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

Journal Articles

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

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

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