CV

Recent Positions

Nokia Corporation
VP of Engineering, AI, 2025 – Present

  • As Vice President of Engineering – AI, I help to shape the company’s AI strategy and execution in support of its mission of Connecting Intelligence and shaping the next-generation architecture of the telecommunications industry. My mandate is to embed AI as a foundational layer across Nokia’s network, cloud, and software platforms. Our strategy is anchored in three platform-centric pillars:
    • AI-powered engineering Software Development Life Cycle (SDLC): Modernizing the end-to-end software development lifecycle through state-of-the-art AI and developer productivity platforms, driving step-change improvements in engineering velocity, quality, and scalability across large, globally distributed telecom organizations.
    • Agentic automation for Telecom operations: Building AI-driven automation platforms that transform Day 1 and Day 2 lifecycle management for cloud-native networks, delivering order-of-magnitude gains in operational efficiency, resilience, and simplicity across complex multi-cloud and hybrid environments.
    • AI-native telecom platforms: Developing system-level, end-to-end optimization across network intelligence, cloud infrastructure, and workload orchestration—laying the foundation for an open telecom marketplace where AI workloads are deployed, optimized, and managed as first-class citizens across edge, cloud, and network domains.
  • Across this work, my focus is on positioning AI not as an incremental feature, but as a core architectural layer that drives convergence across networks, cloud infrastructure, and intelligent software systems.

LinkedIn Corportation
Director of Engineering, AI, 2020 – 2025

  • As a manager, I lead multiple teams of ML engineers and applied researchers, driving AI solutions for Talent Solutions and LinkedIn China. For Talent Solutions, we support products such as Job Search, Job-You-May-Be-Interested-In (JYMBII), Guest Search, Recruiter Search, Recommended Matches, and Instant Notifications. For LinkedIn China, we support a job-seeking based user experience for the local market.
  • As a senior AI leader, working closely with senior partners in product and engineering to shape LinkedIn’s vision in Talent Solutions and LinkedIn China, building cross-functional teams, with the product, data science, and infrastructure to streamline innovation.
  • From 2020 – 2021, I also briefly led AI team for Sales Solutions.

Etsy Inc.
Director of Engineering, Data Science and Machine Learning, 2016 – 2020

  • As a manager, I grow an organization of multiple teams from 5 to almost 40 Master and Ph.D. level Data Scientists and Machine Learning Engineers, located in New York City and San Francisco offices, with backgrounds in Computer Science, Operation Research, Electrical Engineering, Statistics, Economics, Physics and others, including graduates from Harvard University, Cornell University, Carnegie Mellon University and others. My reports include several front-line managers and one architect, both grown internally and hired externally.
  • As a department head, I work with senior leaders in the company (e.g., VP of Engineering, CTO and others) to develop strategies for cross-functional collaboration to incorporate Machine Learning as a key element in product development, complementing Design, Analytics, UX Research, and Product Engineering.
  • As a technical leader, I drive Machine Learning and Data Science vision and deliver cutting-edge scientific solutions for Search & Discovery, Personalization & Recommendation and Computational Advertising by utilizing a wide range of technologies such as deep learning, probabilistic modeling, image understanding (computer vision), user profiling, query understanding, text mining and others. Results are published in SIGIR 2018, WSDM 2019, KDD 2019 and other venues.

Yahoo Research
Senior Manager of Research, Search and Personalization Sciences, 2013 – 2016

  • Design and build a large-scale machine learning system to power the next generation card-driven mobile search experiences for millions of users, utilizing learning-to-rank, deep learning and causal inference techniques. 4 patents filed.
  • Build large-scale ranking models to personalize Yahoo homepage content items for billions of U.S. users and improve 10% over a major user engagement metric (usually 1% considered as significant), utilizing ensemble methods, tree-based models and online learning. 2 publications with 1 Best Paper Award in prestige conferences and 4 internal publications.
  • Build large-scale ranking models to personalize Yahoo homepage content items for millions of users in 20+ international markets with 20% improvements over a major user engagement metric (usually 2% considered as significant), utilizing ensemble methods, tree-based models and multi-armed bandit algorithms.
  • Build large-scale statistical models to track and predict click-through-rate (CTR) of native streaming ads with 2% improvements over a multi-year production system, utilizing generalized linear models and online learning.

Education

Patents

  • US Patent US11132700B1: “Identifying Direct and Indirect Effects in A/B Tests“. Sept 28, 2021.
  • US Patent US20180011854A1: “Method and System for Ranking Content Items based on User Engagement Signals“. Jan 11, 2018.
  • US Patent US20170344552A1: “Computerized System and Method for Optimizing the Display of Electronic Content Card Information when Providing Users Digital Content“. Nov 30. 2017.
  • US Patent US20170098283A1: “Methods, Systems and Techniques for Blending Online Content From Multiple Disparate Content Sources Including a Personal Content Source or a Semi-Personal Content Source“. Apr 6, 2017.
  • US Patent US20170097933A1: “Methods, Systems and Techniques for Ranking Blended Content Retrieved from Multiple Disparate Content Sources“. Apr 6. 2017.

Selected Publications

  • X. Wang, L. Wu, L. Hong, H. Liu and Y. Fu. LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendations. ACM Transactions on Intelligent Systems and Technology, Volume 16, Feb, 2025. [DOI]
  • P. Liu, J. Shen, Q. Shen, C. Yao, K. Kao, D. Xu, R. Arora, B. Zheng, C. Johnson, L. Hong, J. Wu and W. Zhang. Powering Job Search at Scale: LLM-Enhanced Query Understanding in Job Matching Systems. In Proceedings of the 34th ACM International Conference on Information and Knowledge Management (CIKM 2025), Seoul, Korea, November, 2025. [DOI]
  • Z. Zheng, S. Wang, Z. Chen, Y. Zhu, Y. He, L. Hong, Q. Guo and J. Li. CoRAG: Enhancing Hybrid Retrieval-Augmented Generation Through a Cooperative Retriever Architecture. In Proceedings of The 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP 2025), Suzhou, China, November, 2025. [DOI]
  • Y. Juan, J. Shen, S. Zhang, Q. Shen, C. Johnson, L. Simon, L. Hong and W. Zhang. Scaling Retrieval for Web-Scale Recommenders: Lessons from Inverted Indexes to Embedding Search. In Proceedings of the Nineteenth ACM Conference on Recommender Systems (RecSys 2025), Prague, Czech Republic, September, 2025. [DOI]
  • P. Liu, R. Arora, X. Shi, B. Le, Q. Shen, J. Shen, C. Jiang, N. Zhiltsov, P, Bannur, Y. Zhu, L. Dong, H. Wei, Q. Guo, L. Simon, L. Hong and W. Zhang. A Scalable and Efficient Signal Integration System for Job Matching. In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 2 (KDD 2025), Toronto, Canada, August, 2025. [DOI]
  • Y. Zhu, J. Ma, L. Wu, Q. Guo, L. Hong and J. Li. Causal Effect Estimation with Mixed Latent Confounders and Post-treatment Variables. In Proceedings of the Thirteenth International Conference on Learning Representations (ICLR 2025), Singapore, April, 2025. [DOI]
  • J. Shen, Y. Juan, P. Liu, W. Pu, S. Zhang, Q. Shen, L. Hong and W. Zhang. Learning Links for Adaptable and Explainable Retrieval. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM 2024), Boise, ID, USA, October 2024. [DOI]
  • Y. Zhu, L. Wu, B. Zhang, S. Wang, Q. Guo, L. Hong, L. Simon and and J. Li. Understanding and Modeling Job Marketplace with Pretrained Language Models. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM 2024), Boise, ID, USA, October 2024. [DOI] [Earlier ArVix Version]
  • Y. Zhu, L. Wu, Q. Guo, L. Hong and J. Li. Collaborative Large Language Model for Recommender Systems. In Proceedings of the ACM on Web Conference 2024 (WWW 2024), Singapore, May 2024. [DOI] [Earlier ArVix Version]
  • Y. Zhu, J. Ma, L. Wu, Q. Guo, L. Hong and J. Li. Path-Specific Counterfactual Fairness for Recommender Systems. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023), Long Beach CA, August 2023. [DOI] [Earlier ArVix Version]
  • Z. Wang, X. Yin, T. Li, and L. Hong. Causal Meta-Mediation Analysis: Inferring Dose-Response Function From Summary Statistics of Many Randomized Experiments. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2020). Virtual Event, August 2020. (Full Paper, 16% Acceptance) [DOI] [Local Copy]
  • R. Guo, X. Zhao, A. Henderson, L. Hong, and H. Liu. Debiasing Grid-based Product Search in E-commerce. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2020). Virtual Event, August 2020. (Full Paper, 16% Acceptance) [DOI] [Local Copy]
  • J. Wang, K. Ding, L. Hong, H. Liu, and J. Caverlee. Next-item Recommendation with Sequential Hypergraphs. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2020), Virtual Event, July 2020. (Full Paper, 26% Acceptance) [DOI]
  • J. Wang, R. Louca, D. Hu, C. Cellier, J. Caverlee and L. Hong. Time to Shop for Valentine’s Day: Shopping Occasions and Sequential Recommendation in E-commerce. In Proceedings of the 13th ACM International Conference on Web Search and Data Mining (WSDM 2020), Houston, Texas, Feb, 2020. (Full Paper, 15% Acceptance) [Local Copy] [DOI]
  • X. Yin and L. Hong. The Identification and Estimation of Direct and Indirect Effects in A/B Tests through Causal Mediation Analysis. In Proceedings of the 25th SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019), Anchorage, Alaska, August, 2019. (Full Paper, Oral Presentation, 6.4% Acceptance) [Local Copy] [DOI]
  • H. Jiang, A. Sabharwal, A. Henderson, D. Hu and L. Hong. Understanding the Role of Style in E-commerce Shopping.In Proceedings of the 25th SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019), Anchorage, Alaska, August, 2019. (Full Paper, 20% Acceptance) [Local Copy] [DOI]
  • N. Ju, D. Hu, A. Henderson and L. Hong. A Sequential Test for Selecting the Better Variant – Online A/B testing, Adaptive Allocation, and Continuous Monitoring. In Proceedings of the 12th ACM International Conference on Web Search and Data Mining (WSDM 2019), Melbourne, Australia, Feb, 2019. (Full Paper, 16% Acceptance) [Local Copy]
  • L. Wu, D. Hu, L. Hong and H. Liu. Turning Clicks into Purchases: Revenue Optimization for Product Search in E-Commerce. In Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018), Ann Arbor, Michigan, U.S.A. July 8-12, 2018. (Full Paper, 21% Acceptance) [Local Copy] [DOI]
  • T. Chen, Y. Sun, Y. Shi and L. Hong. On Sampling Strategies for Neural Network-based Collaborative Filtering. In Proceedings of the 23rd SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2017), Halifax, Nova Scotia, August, 2017. (Full Paper, 17% Acceptance) [Local Copy] [Local Supplementary] [DOI]
  • Q. Zhao, Y. Shi and L. Hong. GB-CENT: Gradient Boosted Categorical Embedding and Numerical Trees. In Proceedings of the 26th International Conference on World Wide Web (WWW 2017), Perth, Australia, April, 2017. (Full Paper, 17% Acceptance) [Local Copy] [DOI]
  • X. Yi, L. Hong, E. Zhong, NN. Liu and S. Rajan. Beyond Clicks: Dwell Time in Personalization. In Proceedings of the 8th ACM Conference on Recommender Systems (RecSys 2014), Foster City, Silicon Valley, USA, October, 2014. (Full Paper, 23% Acceptance) [Local Copy] [DOI] Best Paper Award
  • A. Ahmed, L. Hong and A. Smola. Nested Chinese Restaurant Franchise Process:  Applications to User Tracking and Document Modeling. In Proceedings of the 30th International Conference on Machine Learning (ICML 2013), Atlanta, GA, May, 2013. [PDF] [Supplementary]
  • A. Ahmed, L. Hong and A. Smola. Hierarchical Geographical Modeling of User Locations from Social Media Posts. In Proceedings of the 22nd International Conference on World Wide Web (WWW 2013), Rio de Janeiro, Brazil, May, 2013. [Local Copy] [DOI]
  • L. Hong, A. Doumith and B. D. Davison. Co-Factorization Machines: Modeling User Interests and Predicting Individual Decisions in Twitter. In Proceedings of the 6th ACM International Conference on Web Search and Data Mining (WSDM 2013), Rome, Italy, Feb, 2013. [Local Copy] [DOI] Best Paper Nominated
  • L. Hong, A. Ahmed, S. Gurumurthy, A. Smola and K. Tsioutsiouliklis. Discovering Geographical Topics in the Twitter Stream. In Proceedings of the 21st International Conference on World Wide Web (WWW 2012), Lyon, France, 2012. [Local Copy] [DOI] [Slides]
  • L. Hong, O. Dan and B. D. Davison. Predicting Popular Messages in Twitter [Poster]. In Proceedings of the 20th international conference on World Wide Web (WWW 2011), Hyderabad, India. [Local Copy] [DOI] Best Poster Award

Selected Talks & Presentations

Selected Awards and Honors

  • Yahoo Patent Milestone Award 2016
  • RecSys Best Paper Award 2014
  • WSDM Best Paper Nominated 2013
  • Dean’s Teaching Assistantship & Research Assistantship 2012
  • Rossin Doctoral Fellows 2012
  • KDD Student Travel Grant 2011
  • WWW Best Poster Award 2011

Selected Professional Activities

Organizer:

Session Chair:

(Senior) Program Committee:

Journal Editorial Board and Review

Mentorship

  • Ziqiao Guan — Stony Brook University (2020 Summer)
  • Md Mehrab Tanjim — University of California at San Diego (2019 Summer)
  • Saket Gurukar — The Ohio State University (2019 Summer)
  • Jianling Wang — Texas A & M University (2019 Summer)
  • Zenan Wang — University of California at Berkeley (2019 Summer)
  • Ruocheng Guo — Arizona State University (2019 Summer)
  • Amin Javari — University of Illinois at Urbana-Champaign (2018 Summer)
  • Hao Cui — Tufts University (2018 Summer) -> Google
  • Nianqiao Ju — Harvard University (2018 Summer)
  • Liang Wu — Arizona State University (2017 Summer) -> Airbnb
  • Wei Qian — Cornell University (2017 Summer)
  • Qian Zhao — University of Minnesota (2016 Summer) -> Bloomberg
  • Qingyun Wu — University of Virginia (2016 Summer)
  • Yue Ning — Virginia Tech University (2016 Summer) -> Stevens Institute of Technology
  • Ting Chen — Northeastern University (2016 Summer) -> UCLA -> Google
  • Jay-Yoon Lee — Carnegie Mellon University (2015 Summer)
  • Shalmali Joshi — University of Texas at Austin (2015 Summer) -> Vector Institute
  • Mingjie Qian — University of Illinois at Urbana-Champaign (2014 Summer) -> Yahoo Labs -> Microsoft

Collaborators