About

B.Eng. in Computer Engineering from the Hong Kong University of Science and Technology; M.S. in Computer Science from Stanford University, focused on distributed systems, AI infrastructure, and technology commercialization; completed Silicon Valley entrepreneurship programs in product strategy and engineering management.

Previously worked on AI platform, enterprise infrastructure, and technology-sector strategy programs at a Silicon Valley cloud computing company and a global consulting firm, gaining full-cycle experience from R&D to commercialization. Founded Beta Creative Tech (HK) Limited in Hong Kong in 2014 and guided the company through hardware engineering, AI compute transformation, proprietary chip development, and the formation of its industry solution matrix across Digital Intelligence Infrastructure, Applications, and Services.

Responsible for long-term strategy, business portfolio, strategic accounts, and ecosystem partnerships. Oversees the coordinated development of proprietary AI chips, compute servers, AI data centers, private training platforms, edge intelligence, and industry LLM solutions; leads strategic engagements across finance, government, manufacturing, healthcare, and retail while expanding an integrated chip, algorithm, system engineering, and service operations ecosystem in Hong Kong and the Greater Bay Area.

Ph.D. in Electrical Engineering and Computer Sciences from the University of California, Berkeley, focused on AI accelerator architecture, on-chip interconnects, and heterogeneous computing; M.S. in Electrical Engineering from MIT, specializing in high-performance chip design and system-level optimization.

Worked at international semiconductor companies on CPU/GPU/NPU architecture and production introduction, with end-to-end experience spanning chip definition, instruction set design, compiler toolchains, and system optimization. Since joining Beta in 2016, he has built the chip architecture and low-level software teams, led engineering validation of the first-generation AI chip, and evolved the technical roadmap for training, inference, and edge AI products.

Leads the company's core technology system and R&D organization. Defines roadmaps for proprietary AI chips, heterogeneous computing, compute servers, full-rack systems, edge AI nodes, and AI data centers; coordinates chip architecture, memory subsystems, high-speed interconnects, distributed training frameworks, inference acceleration engines, and operations scheduling platforms to ensure stability, performance, and scalability in finance, government, manufacturing, and smart city deployments.

B.Eng. in Information Engineering from the University of Hong Kong, with a strong foundation in technical products and data analysis; M.S. in Management from London Business School, focused on enterprise technology marketing, regional growth, and brand strategy.

Previously managed market positioning, industry solution packaging, and regional growth for cloud computing, data center, and AI platform products at enterprise technology companies in Hong Kong and Singapore. Since joining Beta in 2018, she has shaped the company's market narrative from hardware capability to 'AI compute infrastructure plus industry solutions,' launching the ten-solution matrix, Cloud Compute Sharing Program, and vertical case portfolio.

Responsible for brand strategy, market growth, industry communications, solution packaging, and ecosystem partner marketing. Builds vertical narratives around private LLM training, inference acceleration, AI data centers, edge intelligence, knowledge-base LLMs, industrial vision, and compliance auditing for finance, government, manufacturing, healthcare, retail, e-commerce, and smart transportation; supports sales through reference cases, value proof, and regional market expansion.

B.Eng. in Industrial and Systems Engineering from the National University of Singapore; M.B.A. from the Wharton School of the University of Pennsylvania, focused on operations management, enterprise services, and technology commercialization, combining engineering systems, project management, and enterprise operations.

Previously delivered cloud computing, data center, enterprise digital transformation, and complex systems integration programs at major IT services and consulting firms across Asia-Pacific, with full-cycle experience from pre-sales solutioning and resource planning to cross-border supply chain coordination, launch, and operations. Since joining Beta in 2021, he has built project management, customer success, delivery standardization, and partner coordination systems for scalable AI compute and LLM deployments.

Responsible for operations, project delivery, supply chain coordination, customer success, and service quality management. Converts the company's ten solutions and 20+ reference cases into repeatable delivery methodologies; coordinates cross-functional execution for private training platforms, AI data centers, edge nodes, industrial vision, government knowledge bases, and e-commerce inference centers; builds operating rules, resource scheduling, and service SLAs for the Cloud Compute Sharing Program.

B.B.A. in Accounting and Finance from the Chinese University of Hong Kong; M.Sc. in Finance from the London School of Economics and Political Science, focused on corporate finance, capital budgeting, technology company valuation, and corporate governance.

Previously managed financing, budgeting, cost control, and business analysis at Hong Kong technology investment firms and multinational corporate finance teams, with familiarity in the capital expenditure profile of chip R&D, server manufacturing, data center construction, and cloud compute operations. Since joining Beta in 2022, she has built financial management systems for R&D investment, project delivery, compute assets, and long-term service contracts, supporting the company's shift from project delivery to productized and platform-based operations.

Responsible for financial strategy, budgeting, business analysis, financing planning, risk control, and governance. Builds cost models, asset return calculations, and pricing mechanisms for proprietary chips, compute servers, AI data centers, and the Cloud Compute Sharing Program; supports commercial modeling and contract risk assessment for large finance, government, and manufacturing projects; helps the company maintain healthy cash flow and sustainable growth under high R&D and asset investment requirements.

B.A. in Computer Science from the University of Cambridge; M.S. in Management Science and Engineering from Stanford University, focused on enterprise software products, AI platform commercialization, and complex systems product design.

Previously led product planning for AI platforms, cloud-native toolchains, and data products at enterprise software companies in Europe and North America, with full-cycle experience from customer discovery and product definition to roadmap management and commercialization. At Beta, she drives a unified product system across private training, inference acceleration, knowledge-base LLM, and hybrid cloud scheduling platforms.

Responsible for product strategy, roadmap, user experience, and commercialization. Converts the company's ten solutions into standardized product modules, coordinating R&D, marketing, sales, and delivery teams to turn industry project experience into repeatable software platforms, hardware specifications, and service packages; continuously improves enterprise customer workflows across model training, inference deployment, knowledge-base construction, compute scheduling, and operations monitoring.

B.A. in Mathematics and Computer Science from the University of Oxford; Ph.D. in Machine Learning from Carnegie Mellon University, focused on large model training optimization, multimodal learning, model compression, and trustworthy AI.

Previously worked at international AI research labs and autonomous driving algorithm teams, leading large-scale model training, visual perception, and model evaluation systems, with deep experience translating research into production. At Beta, he leads industry LLM methodologies, model benchmarks, fine-tuning strategies, and edge inference optimization for finance, government, manufacturing, healthcare, and content moderation deployments.

Responsible for AI research, model system development, and frontier technology transfer. Defines roadmaps for LLM training, RAG, vertical fine-tuning, multimodal recognition, industrial vision inspection, and model safety evaluation; works with chip, platform, and delivery teams to optimize model performance, stability, and explainability on proprietary compute servers, edge AI nodes, and private training platforms.

M.Eng. in Information Security from École Polytechnique; M.Sc. in Technology Policy and Digital Governance from University College London, focused on data security, AI governance, privacy protection, and cross-border compliance.

Previously led security governance, privacy compliance, risk auditing, and enterprise security product design at European fintech companies and multinational cloud providers, with deep knowledge of data sovereignty, model auditing, and access control requirements in regulated sectors such as finance, government, and healthcare. At Beta, she drives the enterprise AI compliance and security audit platform and security architecture for private deployments.

Responsible for security strategy, data governance, privacy protection, AI compliance auditing, and customer security assessment. Builds security frameworks covering data ingestion, model training, inference calls, content moderation, audit logs, and access control; provides compliance solutions for finance, government, healthcare, and cross-border enterprise customers, ensuring private training platforms, knowledge-base LLMs, AIGC platforms, and cloud compute sharing services meet audit and regulatory requirements.