Hongning Wang

Copenhaver Associate Professor of Computer Science

Office: Rice Hall, Room 408

Phone: 434-982-2228

Email: hw5x-at-virginia-dot-edu

I am an associate professor in the Department of Computer Science of University of Virginia. My research interest includes data mining, machine learning, and information retrieval, with a special emphasis on computational user behavior modeling. I graduated from the CS@UIUC in May 2014. My research group's homepage is at HCDM@UVa.

News

  • Thrilled to receive the Karen Spärck Jones (KSJ) Award 2023!

  • Very glad to receive the Copenhaver Associate Professor appointment from the School of Engineering!

  • We have reorganized our group's algorithms and released most of them through this GitHub site. You are highly welcomed to try them out and provide us feedback!

  • Interested in multi-arm bandits? You can now play with such algorithms here.

  • Our research in "Moving offline learning to rank online, from theory to practice" has been funded by NSF IIS program. More details about this award can be found here.

  • I will be visiting the Department of Computer Science and Technology at Tsinghua University in the academic year 2021-22 for my sabbatical leave.

  • I am offering CS4780 Information Retrieval in the spring semester.

  • Our research in "Towards Explainable Personalization" has been funded by NSF IIS program. More details about this award can be found here.

  • I am offering CS6501 Reinforcement Learning in the fall semester.

  • We are giving a tutorial on Learning by Exploration at KDD'2020.

  • I am offering CS4780/6501 Information Retrieval in the spring semester.

  • Our paper "Variance Reduction in Gradient Exploration for Online Learning to Rank" received the Best Paper Award at SIGIR'2019!

  • I gave an invitated talk about "Join Network Embedding with Topic Embedding for User Representation Learning" at LinkedIn.

  • I gave an invitated talk about "Learning Contextual Bandits in a Non-Stationary Environment" at Pinterest.

  • I gave an invitated talk about "Context Attentive Document Ranking and Query Suggestion in Search Tasks" at the Task Intelligence Workshop, WSDM'2019, Melbourne Australia.

  • I was selected for the WSDM 2019 Outstanding Senior Program Committee Award.

  • Our research in "Learning and Improving Alzheimer's Patient-Caregiver Relationships via Smart Healthcare Technology" has been funded by NSF SCH program. More details about this award can be found here.

  • Thanks Nvidia for the generious support of a Titan XP GPU!

  • Our research of "The Building Adapter: Automatic Mapping of Commercial Buildings for Scalable Building Analytics" has been funded by the U.S. Department of Energy. More details about this award can be found here.

  • Our research of "Cyber Physical Mappings - Empower Building Analytics at Scale" has been funded by NSF IIS program. More details about this award can be found here.

  • I gave an invitated talk about "Contextual Bandits in a Collaborative Environment" at the Department of Computer Science and Technology, Tsinghua University, Beijing China.

  • We have been awarded by the Yelp Dataset Challenge Round Eight!

  • Our group homepage is now available at HCDM@UVa.

  • Our research of "Collaborative Sensing: An Approach for Immediately Scalable Sensing in Buildings" has been funded by NSF CPS program. More details about this award can be found here.

  • Our research of "Collaborative Learning with Incomplete and Noisy Knowledge" has been funded by NSF III program. More details about this award can be found here.

  • I gave an invitated talk about "Collaborative Online Learning" at Yahoo Search Science Team.

  • I have received the NSF Faculty Early Career Development Program (CAREER) Award. More details about this award can be found here and our project website is here.

  • Our proposal of "Call for Special Issue on Search, Mining and their Applications on Mobile Devices" has been accepted by the ACM Transactions on Information Systems (TOIS). Call for papers will be announced shortly.

  • I gave an invitated talk about "Human-centric big data mining" at Center for Embedded Systems for Critical Applications (CESCA) in the Department of Electrical and Computer Engineering at Virginia Tech.

  • I gave an invitated talk about "Human-centric big data mining" in Quantitative Psychology Group in the Department of Psychology at UVa.

  • My research is reported on UVa Today.

  • The course website for CS6501 Text Mining has been deployed.

  • I was selected for the WSDM 2015 Outstanding Reviewer Award.

  • I have position opennings for Ph.D. students for Fall 2015. If you are self-motivated and want to pursue a Ph.D. degree in the areas of data mining and information retrieval, please contact me. More information about the graduate admission at CS@UVa can be found here. Please note the application deadline is December 15th, 2014.

  • I am going to offer CS6501: Text Mining next Spring semester.

  • I am offering CS6501: Information Retrieval this Fall semester.

  • I joined CS@UVa as an assistant professor.


Award and Honor


Invited Talks


Academic Activities

  • Program committee member:
    • International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR): 2023 (SPC), 2021, 2020, 2019, 2018, 2017, 2016, 2015
    • International ACM KDD Conference on Knowledge Discovery and Data Mining (KDD): 2023 (AC), 2022 (SPC), 2021, 2020, 2019, 2018, 2017, 2016, 2015
    • The World Wide Web Conference (WWW): 2023 (SPC), 2022, 2020 (SPC), 2019, 2018, 2016, 2015
    • ACM International Conference on Web Search and Data Mining (WSDM): 2023 (SPC), 2022 (SPC), 2021, 2019 (SPC), 2018 (SPC), 2017, 2016, 2015
    • Conference on Neural Information Processing Systems (NeurIPS): 2022, 2021, 2020, 2019
    • Annual Meeting of the Association for Computational Linguistics (ACL): 2023 (AC), 2019, 2018, 2017, 2015
    • ACM International Conference on Information and Knowledge Management (CIKM): 2022 (SPC), 2021 (SPC), 2020 (SPC), 2017, 2015, 2014
    • International Conference on Machine Learning (ICML): 2022, 2021, 2020, 2014, 2013, 2012
    • International Conference on Learning Representations (ICLR): 2023, 2022, 2021, 2020
    • Association for the Advancement of Artificial Intelligence (AAAI): 2023 (AC), 2022 (AC), 2021 (SPC), 2019, 2018, 2017
  • Journal associate editor: Frontiers in Big Data, ACM Transactions on Intelligent Systems and Technology (TIST)
  • Journal reviewer: JMLR, TKDE, TOIS, TPAMI, TOIS, Neurocomputing, BMC Bioinformatics, Information Processing & Management, IJAIT, Neural Processing Letters, World Wide Web Journal, International Journal of Machine Learning and Cybernetics.
  • Public services: SIGIR 2024 General Co-Chair, KDD 2022 Workshop Co-Chair, KDD 2020 Student Sponsorship Co-Chair, SIGIR 2018 Student Sponsorship Chair, WSDM 2018 Demo Track Chair, CIKM 2016 Publicity chair, AIRS 2016 area chair for IR Models and Theories, NLPCC 2015 area chair for Search and Advertisement.

Selected Publications

[Google Scholar] [DBLP] [Full List]
  1. Haozhe Ji, Cheng Lu, Yilin Niu, Pei Ke, Hongning Wang, Jun Zhu, Jie Tang and Minlie Huang. Towards Efficient and Exact Optimization of Language Model Alignment. arXiv:2402.00856, 2024. (PDF)
  2. Zhepei Wei, Chuanhao Li, Tianze Ren, Haifeng Xu and Hongning Wang. Incentivized Truthful Communication for Federated Bandits. The Eleventh International Conference on Learning Representations (ICLR'2024), 2024. (PDF)
  3. Fan Yao, Chuanhao Li, Denis Nekipelov, Hongning Wang, Haifeng Xu. How Bad is Top-K Recommendation under Competing Content Creators? The Fortieth International Conference on Machine Learning (ICML'2023), Oral presentation, 2023. (PDF)
  4. Chuanhao Li and Hongning Wang. Communication Efficient Federated Learning for Generalized Linear Bandits. Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS'2022), 2022. (PDF)
  5. Fan Yao, Chuanhao Li, Denis Nekipelov, Hongning Wang and Haifeng Xu. Learning from a Learning User for Optimal Recommendations. The Thirty-ninth International Conference on Machine Learning (ICML'2022), 2022. (PDF)
  6. Aobo Yang, Nan Wang, Renqin Cai, Hongbo Deng and Hongning Wang. Comparative Explanations of Recommendations, The ACM Web Conference 2022 (WWW'2022). (PDF)
  7. Yiling Jia, Huazheng Wang, Stephen Guo and Hongning Wang. PairRank: Online Pairwise Learning to Rank by Divide-and-Conquer. The Web Conference 2021 (WWW'2021), p146-157. Nominated for the Best Paper Award (PDF, code)

Open Implementations

[GitHub]
  • BanditLib: It is a python library for a rich collection of bandit algorithms, ranging from context-free multi-armed bandits to contextual linear bandits, neural bandits, collaborative bandits, and non-stationary bandits. We also include various testing environments, from synthetic data based simulations to large-scale real-world data based evaluations.
  • Online Learning to Rank: It is a python library for our group’s developed online learning to rank algorithms. It is based on a public git repo for simulated online learning to rank environment.
  • xRec: It is a python library for our group’s developed explainable recommendation algorithms. It covers algorithms for explaining both latent factor based and neural network based recommendation models.