Haoming Koo

Full-time AI engineering apprenticeship under AI Singapore's national AI programme. Building and deploying end-to-end ML systems across structured data, computer vision, and sequential modelling domains.

• Delivered production-ready pipelines using GitLab CI/CD, Docker, and Google Cloud – including backend inference services and frontend UIs
• Built CNN architectures and transfer learning models for image classification and object detection
• Developed LSTM-based sequence modelling systems for time-series prediction
• Implemented containerised deployment, model lifecycle management, and MLOps automation
• Built 6 live portfolio applications integrating ML models with cloud-hosted dashboards
• Expanding into NLP, Large Language Model architectures, and agentic AI workflows

Structured upskilling period focused on AI engineering, full-stack development, and applied transformation – bridging 7 years of semiconductor leadership into production AI roles.

• Completed Full Stack Development with AI (NUS x Emeritus, 24 weeks) – React, Flask, MongoDB, SQL, Node.js, REST APIs, AI integration
• Completed GA100 Generative AI (Heicoders Academy, WSQ) – prompt engineering, RAG, AI agents, workflow automation (n8n), MCP
• Built 6 live portfolio applications deployed at kooexperience.com
• PMP certification preparation
• Exposure to innovation ecosystems across Europe and Asia – digital platforms, sustainability, and cross-cultural leadership

Led global AI-enabled transformation and cross-site program delivery, aligning 3,000+ engineers across four fabs (Singapore, Boise, Hiroshima, Taichung). Managed 6 direct and up to 9 indirect engineers with a USD 9M operational portfolio.

• Designed and executed a 5-year autonomous conversion roadmap using 3M strategy – $832M in opportunities surfaced, $50M+ realised savings
• Unified fragmented process platforms into a single AI-integrated ecosystem – standardised 80% of workflows across 4 countries
• Co-led LPDDR5X yield ramp (Apple iPhone 16 Pro Max) – maturity pulled in by 7 months, unlocking 9-figure USD revenue
• Co-led HBM3E yield program (NVIDIA H200/Blackwell) – Micron's first HBM market entry, driving $500M+ above business plan
• Modernised infrastructure from Hadoop to Google Cloud Platform (GCP)
• Developed ML risk-scoring dashboards – preventing USD 100M+ potential losses
• Built GenAI SOP Assistant: 80–90% answer relevance, ~15 min/query deflection, scaled to ~3,000 users
• Scaled RPA (UiPath/Power Automate) for targeting-lot workflow – 100% adoption; mentored ~30 engineers
• Built reliability/observability: pipeline health dashboards, canary/shadow runs, rollback runbooks – near-zero ML-pipeline outages
• Drove SEMulator3D rollout: accelerated validation by 2 weeks, >$10M in time-to-market gains
• Conducted red-team UAT simulations, reducing post-deployment issues by 25%

Transitioned from equipment engineering to data-driven automation – developing ML-enabled defect prediction and control systems.

• Built ResNet-50 computer vision model for wafer/chamber misplacement detection – earned Micron Innovation Award
• Scaled AI-based OCR defect detection system fab-wide – 4–5% yield improvement
• Reduced scanner wafer rejection to <0.5% and MA excursion impact to <0.1%
• Developed anomaly detection and OOF monitoring with automated MES-integrated alerting
• Pioneered novel bevel etch detection using peak colour analysis – became permanent defence strategy

Led smart manufacturing initiatives integrating digital twins, ML models, and automation pipelines.

• Built a digital twin of TEL Celesta – ML-guided DoE optimisation reduced chemical usage by 60% and saved USD 7M+
• Saved 40+ hours/week of manual validation through simulation-based process qualification
• Qualified >10 alternate chemistries during COVID disruptions – zero production downtime, mitigating >USD 100M potential losses
• Co-authored 7 technical whitepapers on yield analytics, process automation, and cross-site harmonisation

Built core engineering and program delivery foundations during the Hiroshima Fab Expansion.

• Achieved USD 7.5M CAPEX savings through smart ramp planning and tool reuse across global fabs
• Developed “MAXout” onboarding framework – boosted team productivity by 20%, later scaled across sites
• Aligned Hiroshima ramp plans with Boise and Singapore through remote calibration

NUS MSc capstone – digital transformation focused on Industrial IoT and applied ML for maritime safety and enforcement.

• Detected illegal vessel mooring using geospatial analytics with GPS and tidal-corrected clustering (K-means, DBSCAN)
• Built scalable ML pipeline on AWS (S3, Lambda) for real-time anomaly detection – reduced false positives by 22%
• Delivered end-to-end prototype influencing ~$2M potential annual revenue across four ports

R&D in bio-lasing technologies – investigating biological materials as laser gain mediums.

• Engineered and tested laser prototypes integrating biological components
• Automated data collection and visualisation using MATLAB

Hydrogen evolution research under Prof. Jason Yeo – advancing catalyst science for sustainable energy.

• First to identify sulfur atoms as active sites in amorphous MoSₓ catalysts – novel experimental finding
• Executed Operando Raman Spectroscopy to examine catalytic behaviour under real-time conditions