Chenhao Wu

How do you optimize ML pipelines across massive video, image, and speech datasets while simultaneously increasing training efficiency by 30%? I have spent the last decade bridging that exact gap, turning complex machine learning research into high-impact, scalable production environments. My career is defined by a "research-to-revenue" mindset, where I translate deep technical findings into clear insights for non-technical audiences and commercial teams. Core Impact & Achievements: * Computer Vision & Industrial AI: At Kunlun Digital (China Petro), I engineered ML pipelines for large-scale video/image datasets and built an automated labelling system that slashed manual annotation time by 50%. * Speech Innovation & Research: As a published researcher, I pioneered active-learning systems that achieved an 80% improvement in ASR accuracy for low-resource settings and curated over 500 hours of large-scale speech datasets. * Commercial Data Science: In the UK tech sector, I developed NLP-driven customer segmentation models that generated £100k+ in revenue within just three months while ensuring total GDPR compliance. * Global Technical Leadership: I have a proven track record of securing project alignment with international partners like ExxonMobil and mentoring over 500 multidisciplinary professionals in engineering and tech. Technical Specialization: * AI & ML: Algorithm Engineering, Computer Vision Pipelines, NLP, Unsupervised/Semi-supervised Learning, and MLOps. * Speech Technology: Automatic Speech Recognition (ASR), Acoustic Modeling (MAP, MLLR, GMM, HMM), and Dataset Curation. * Strategy & Infrastructure: SaaS Data Infrastructure, Stakeholder Management, and Technical Curriculum Design. Beyond code, I am a published author of three textbooks on Python and ASR. I am deeply committed to tech education, having empowered diverse groups—from training softwar