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 across healthcare, telecommunications, and internet-scale consumer platforms. My work sits at the intersection of product strategy, organizational leadership and AI, with a consistent focus on converting frontier innovation into durable revenue growth, operational leverage, and differentiated user value.

Since November 2026, I have served as VP of Engineering – AI at Resmed, a global leader in AI-powered digital health and cloud-connected medical devices with approximately $5.7 billion in annual revenue. I drive AI R&D and innovation across Resmed’s product lines in support of its mission to create life-changing health technologies that people love and its 2030 goal of helping more than 500 million people worldwide achieve their full health potential through better sleep and breathing at home. Resmed operates one of the largest connected-health ecosystems in the world, with more than 36 million cloud-connected devices, over 28 billion nights of sleep and breathing data, and more than 37 million patients managed on its AirView platform. My focus is on turning this unique data foundation into AI that personalizes therapy, empowers clinicians and care teams, and accelerates how Resmed invents and delivers health technology, positioning AI not as an incremental feature, but as a core layer of the company’s products and platforms.

From 2025 to 2026, I served as VP of Engineering – AI at Nokia, where I helped shape the company’s AI strategy in support of its mission of Connecting Intelligence. I built the cornerstone for AI-native core networks, establishing the AI foundations for next-generation core network platforms to be designed, operated, and optimized with AI as a first-class citizen. I led the transformation of the product development lifecycle (PDLC) across an engineering organization of thousands, including an enterprise AI harness that grew from zero to more than 3,000 users, and rebuilt teams and organizations to be AI-native, reshaping roles, skills, processes, and operating models around AI-first ways of working.

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 a ~10% engagement lift and more than $100M in annualized revenue growth 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, 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 centralized AI 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, 7.4K+ 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
  • AI for Physical Intelligence including Telecom, Healthcare and more
  • LLMs, agentic systems, 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

Since 2026, I have given invited talks on the topic of “Building AI-Native Telecom Technology for Connecting Intelligence” at:

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

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Recent Posts

Recent Papers

  • 2026-10-20 “A Mechanistic Study of Automated Failure Attribution in LLM-based Agentic Systems” was published in ArXiv. [LINK]
  • 2026-10-01 “Capability Scaling-Down Laws for LLM Compression” was published in ArXiv. [LINK]
  • 2026-09-30 “AnyJev Technical Report” was published in ArXiv. [LINK]
  • 2026-09-29 “Adapting Context Compression for Long-Horizon Agents with Counterfactual Continuations” was published in ArXiv. [LINK]
  • 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 2025. [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]

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