AI Executive Leader

I am an executive AI and platform leader with nearly 20 years of experience in building and scaling AI organizations, establishing AI-first operating models, and translating advanced AI technologies into core platform capabilities for global enterprises. My work sits at the intersection of machine learning, product strategy, and organizational leadership, with a consistent focus on converting frontier AI innovation into durable revenue growth, operational leverage, and differentiated user value.

Since 2025, I have served as Vice President of Engineering – AI at Nokia, where 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 Product Development Life Cycle (PDLC): 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.

From 2020 to 2025, I served as Director of Engineering – AI at LinkedIn, leading the Talent Marketplace AI organization (~60 ML engineers and applied researchers). I owned organic and paid job search, recommendation, and notification experiences, partnering closely with VP-level leadership to define AI-first product strategy and deliver LLM-powered experiences for job seekers and hirers. This work consistently generated ~10% engagement lift and more than $100M in annualized revenue growth per year across LinkedIn’s multi-billion-dollar Talent Solutions business. In addition to my role in Talent Marketplace, I previously served as AI lead for LinkedIn China and LinkedIn Sales Solutions as well, shaping AI work for a wide range of business applications.

Prior to LinkedIn, I led Data Science and Machine Learning at Etsy from 2016 to 2020, scaling a AI-centralized organization from almost scratch with 5 to nearly 40 senior scientists and engineers across New York and San Francisco. The team delivered $120M in incremental GMV and $10M in annual revenue, powering search, discovery, personalization, and advertising through deep learning, causal inference, computer vision, and large-scale experimentation. Earlier in my career at Yahoo Research from 2013 to 2016, I managed research and engineering teams building personalization and ranking systems for products serving billions of users, achieving sustained double-digit engagement gains.

My research has been widely published (H-index 30, 7.3K+ citations), recognized with the ACM RecSys Best Paper Award, and translated into patents and large-scale production systems. I am particularly passionate about:

  • AI-first product strategy and marketplace design
  • LLMs, recommender systems, and causal ML in production
  • Building and scaling high-performing ML organizations
  • Bridging research innovation with measurable business impact

I regularly keynote at leading industry and academic venues and contribute to the field through conference leadership and program committee service.

Tech Community Activities

From 2026, I started to give invited talks on the topic of “Building AI-Native Telecom Technology for Connecting Intelligence” at:

Previously, I gave a series of invited talks on the topic of “Supercharging Jobs Marketplace” and “Computational Jobs Marketplace” at:

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Recent Papers & Posts

  • 2026-08-11 “Self-Evolving Code-with-Image Reasoning” was published in ArXiv. [LINK]
  • 2026-08-09 “Towards Resilient and Autonomous Networks: A BlueSky Vision on AI-Native 6G” was published in KDD 2026. [LINK]
  • 2026-08-09 “A Unified Structured Query Understanding Framework for Industrial Semantic Search” was published in KDD 2026. [LINK]
  • 2026-08-08 “SPECTRA: Pushing the KV Cache Beyond the 2-Bit Cliff via Spectral Transform Coding” was published in ArXiv. [LINK]
  • 2026-08-06 “Toward Reliable Context Compression for Long-Horizon Agents: An Empirical Study of Execution Instability” was published in ArXiv. [LINK]
  • 2026-08-03 “Field Aware Agent Skill Retrieval” was published in ArXiv. [LINK]
  • 2026-07-20 “Unified Semantic Modeling Framework for Large-Scale Job Understanding at LinkedIn” was published in SIGIR 2026. [LINK]
  • 2026-06-10 “WiSP: A Working-Set View of Mixture-of-Experts Serving on Extremely Low-Resource Hardware” was published in ArXiv. [LINK]
  • 2026-05-17 “SAPO: Step-Aligned Policy Optimization for Reasoning-Based Generative Recommendation” was published in ArXiv. [LINK]
  • 2026-04-23 “SemCoT: Accelerating Chain-of-Thought Reasoning through Semantically-Aligned Implicit Tokens” was published in NeurIPS 2026. [LINK] [Earlier arXiv versions]
  • 2025-11-10 “Powering Job Search at Scale: LLM-Enhanced Query Understanding in Job Matching Systems” was published in CIKM 2025. [LINK]
  • 2025-11-04 “CoRAG: Enhancing Hybrid Retrieval-Augmented Generation Through a Cooperative Retriever Architecture” was published in EMNLP 2025. [LINK]
  • 2025-10-07 “LANTERN: Scalable Distillation of Large Language Models for Job-Person Fit and Explanation” was published in ArXiv. [LINK]
  • 2025-09-18 “LLM-Enhanced User–Item Interactions: Leveraging Edge Information for Optimized Recommendations” was published in ACM Transactions on Intelligent Systems and Technology. [LINK]
  • 2025-09-07 “Scaling Retrieval for Web-Scale Recommenders: Lessons from Inverted Indexes to Embedding Search” was published in RecSys 2025. [LINK]
  • 2025-08-11 “A Scalable and Efficient Signal Integration System for Job Matching” was published in KDD 2025. [LINK]
  • 2025-04-24 “Causal Effect Estimation with Mixed Latent Confounders and Post-treatment Variables” was published in ICLR 2025. [LINK]
  • 2024-10-20 “Learning Links for Adaptable and Explainable Retrieval” [LINK] and “Understanding and Modeling Job Marketplace with Pretrained Language Models” [LINK] were published in CIKM 2024.
  • 2024-09-08 A new post about KDD 2024.
  • 2024-05-13 “Collaborative Large Language Model for Recommender Systems” was published in The Web Conference 2024. [LINK]

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