Olena Zhu

Chiplet and disaggregated architectures are rapidly becoming mainstream across applications from edge to server. Yet the resulting design complexity exceeds the capabilities of today’s tools, flows, and methodologies—particularly when aiming for highly optimized solutions at scale.

Augmented Intelligence, the combination of human expertise and machine intelligence, offers a transformative approach to this challenge. By assigning strategic, high-level decision-making to engineers and delegating computationally intensive, iterative tasks to AI, this framework enables multi-level and multi-domain optimization. The result is the ability to generate a far greater number of custom-optimized designs with the same resources—delivering competitive products with higher quality and faster time-to-market.

At Intel, in collaboration with partners, we have developed and deployed Augmented Intelligence solutions spanning silicon to system design and hardware to software design. These efforts have demonstrated efficiency gains exceeding 90% in critical areas. In this talk, I will share practical examples and key insights from several years of applying Augmented Intelligence to end-to-end design, highlighting how human–AI collaboration is reshaping the path to innovation.

Biography:

Dr. Olena Zhu is the Head of AI Solutions & Ecosystem in Intel’s Client Computing Group, where she leads Intel’s strategy for AI-enabled client and edge computing platforms. She drives the development of multi-agent, hybrid on-device/cloud AI solutions, including the Intel AI Assistant Builder, and builds strategic AI ecosystems with partners such as Microsoft, Mistral, and Perplexity. Her work focuses on transforming PCs and edge devices into agentic, intelligent platforms, accelerating practical AI adoption across consumer and enterprise environments through tightly integrated hardware–software–AI co-design.

Previously, Dr. Zhu served as Chief AI Technologist for Intel’s Client Computing Group, where she initiated and led Intel’s corporate-level Augmented Intelligence (AuI) strategy—combining domain expertise with machine intelligence to transform end-to-end design methodologies. She led the development of AI-driven optimization solutions spanning silicon, package, board, and system design, achieving over 90% reduction in design cycle time with corporate-wide adoption. Her technical contributions include AI-assisted auto-overclocking for Intel’s 14th Generation CPUs, personalized PC optimization for performance and power efficiency, and high-dimensional global optimization algorithms achieving 2× speedup over prior approaches. Earlier roles at Intel include Principal Engineer for the Intel Evo™ platform, System Architect, and Technical Lead, where she delivered major advances in battery life, power management, low-power architectures, high-speed I/O design, and ML-enabled nonlinear circuit simulation.

Dr. Zhu received her Ph.D. in Electrical and Computer Engineering from Purdue University and her B.S. in Electronic Engineering from the University of Science and Technology of China. She has authored 50+ journal and conference publications in computational electromagnetics, signal and power integrity, and AI-driven system optimization, and holds 40+ granted or pending U.S. patents. Her work includes foundational contributions to full-wave electromagnetic solvers, nonlinear signaling analysis, and EMC/SI modeling, published in leading IEEE journals. She is a recipient of the Intel Achievement Award and the SWE Emerging Leader Award, has served as Industry Chair for an IEEE MTT-S conference, and is an active mentor and leader within the global engineering and research community.

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